Nordic Semiconductor has introduced AI-assisted development capabilities intended to support wireless IoT products from early prototyping through fleet-level debugging. The approach is notable because it connects embedded development with operational device data rather than limiting AI support to code generation.

For embedded IoT teams, the difficult work rarely ends when firmware compiles. Board bring-up, SDK migration, production handover and post-deployment troubleshooting often involve different tools, different teams and fragmented context. That gap is especially visible in low-power wireless products, where behavior in the field can depend on a combination of firmware, radio conditions, cloud services and device lifecycle processes.

Nordic Semiconductor is now trying to address that fragmentation by bringing AI-assisted workflows across what it describes as the full IoT device lifecycle. The company says the capability is available today and is designed for developers building wireless IoT products on Nordic’s chip-to-cloud platform.

The announcement is not simply another embedded AI coding assistant. Nordic’s positioning is that its hardware, embedded software, SDK, development tools and cloud lifecycle services can provide a shared context for AI assistance beyond the code editor. According to the company, developers can use their preferred AI assistant through Nordic MCP servers, rather than being forced into a single proprietary front end.

Why this differs from typical AI developer tooling

Most AI tools aimed at embedded engineers focus on code completion, documentation search or generating examples inside an IDE. Nordic is making a different claim: that AI support can follow a product from the first prototype on a development kit into production handover and then into a deployed fleet.

That distinction matters because the hardest embedded problems are often contextual rather than syntactic. A firmware crash on a deployed device is not just a code issue; it may involve SDK version history, board configuration, device logs and cloud-side lifecycle data. By tying AI assistance to Nordic-specific development and fleet context, the company is attempting to make the assistant useful for tasks such as SDK version migration, custom board bring-up and root-cause analysis of devices already in the field.

The practical insight for OEMs is that the value of this model depends on continuity. If engineering teams use Nordic’s SDK during development but manage deployed devices through disconnected operational tools, the AI assistant will have less lifecycle context to work with. Conversely, projects that use enough of Nordic’s chip-to-cloud environment may benefit from a more consistent troubleshooting path from lab bench to field issue.

Implications for IoT product teams

For OEMs building low-power wireless devices, the main impact could be a reduction in the friction between early prototyping and later maintenance. Nordic says developers can move from idea to proof of concept on a Nordic development kit more quickly, and that AI assistants can produce more accurate results in fewer iterations, reducing token cost and improving code reliability.

For system integrators and enterprises deploying connected products, the more interesting part is post-deployment debugging. If AI-assisted root-cause analysis can be performed within the same development workflow used to build the device, field support teams may be able to escalate issues with more usable technical context. That does not remove the need for embedded expertise, but it may reduce the time spent reconstructing how a device was built, configured and updated.

Connectivity providers are not the direct target of the announcement, but the lifecycle angle is relevant to them as well. Wireless IoT failures are often blamed on connectivity even when the underlying cause sits in firmware, device configuration or cloud integration. A development environment that can combine device-side and cloud-side context may help clarify where responsibility lies during incident analysis.

For the broader IoT ecosystem, Nordic’s move reflects a shift in how semiconductor vendors compete. Low-power wireless suppliers are no longer differentiated only by radio silicon or SDK breadth. Increasingly, they are packaging hardware, embedded software, cloud services and lifecycle management into a developer experience. Nordic’s AI-assisted workflow is a continuation of that platform strategy, with AI used as an interface across the stack rather than as a standalone feature.

The announcement should still be viewed with appropriate caution. Nordic has not disclosed performance benchmarks, deployment scale or quantified productivity gains. What it has introduced is an architectural approach: using AI assistance across Nordic’s connected development and lifecycle environment, while allowing developers to work with the AI assistant they already use. For IoT teams evaluating embedded platforms, that architectural choice may prove more significant than the AI label itself.

The post Nordic Extends AI Assistance from Firmware Development to Deployed IoT Fleets appeared first on IoT Business News.

Every connected device a business runs is a workload someone has to keep healthy, secure, and patched. With 21.1 billion connected IoT devices online by the end of 2025 and a path to 39 billion by 2030, the spreadsheet-and-script approach that worked for a few hundred laptops is no longer viable. Gartner now expects more than half of organizations to adopt autonomous endpoint management by 2029. The shift is not a tooling upgrade. It is a change in how IT teams operate across the entire estate, from corporate laptops to industrial sensors.

Key Takeaways

• Autonomous endpoint management uses AI and policy-driven automation to handle device tasks that previously required manual technician work.
• The global IoT installed base is on track to nearly double this decade, well beyond what manual operations can keep up with.
• Five workflows benefit most: patch deployment, device onboarding, compliance enforcement, incident response, and software lifecycle management.
• Organizations consistently flag security as the leading obstacle to expanding connected device deployments, and intelligent automation is one of the clearest paths forward.
• Successful adoption depends on a complete asset inventory, clear policy intent, and a phased rollout, not on replacing the IT team.

From Manual Tickets to Intelligent Workflows

Traditional endpoint management is a queue of human-driven tickets. A new device joins the network and someone configures it. A vulnerability is disclosed and someone applies the patch. A user reports a slow machine and someone investigates. The model worked when a typical estate held hundreds of laptops in a single office. It breaks down when the same team is responsible for laptops, kiosks, edge gateways, and thousands of sensors across multiple sites. A working definition of autonomous endpoint management for IT automation  replaces that ticket queue with a continuously running platform that detects state changes, decides what to do based on pre-defined policy, and acts without waiting for a human in the loop.

The shift is more philosophical than technical. The system still does the same things a skilled administrator would do. It just does them at machine speed and at full scale. Industry context for that wider operational shift, from connectivity-only deployments to integrated infrastructure, is captured well in coverage of how the IoT industry moved through 2025, as IT, OT, and security functions converge.

The Scale Problem Driving the Shift

The numbers behind the manual-to-autonomous transition are direct. Connected device counts have crossed thresholds that human-paced operations cannot keep up with.

The chart understates the operational pressure. Each of those billions of endpoints generates state changes throughout the day: configuration drift events, security agent status updates, patches released, services failing, disk thresholds breached. A fleet of just 5,000 endpoints will produce thousands of such signals daily. Multiply that across the broader IoT and IT estate, where 67 percent of organizations already cite security as the top barrier to scaling deployments, and the case for intelligent automation makes itself.

Five Workflows Where Autonomous Management Pays Off First

Some IT processes deliver returns immediately when intelligent automation takes over. The five below are where most organizations see measurable change within the first quarter.

• Patch deployment. What used to take a technician hours per batch becomes minutes per endpoint, with consistent application across operating systems, third-party apps, and firmware.
• Device onboarding. Zero-touch provisioning means a new laptop, kiosk, or sensor enrolls itself, downloads its baseline configuration, and reports as compliant before a human ever logs in.
• Continuous compliance. Instead of quarterly audits that catch drift after the fact, compliance becomes a real-time operating state with audit-ready logs available on demand.
• Incident response. Suspicious behavior on an endpoint triggers automatic isolation, evidence capture, and ticket creation, often before the security team sees the alert.
• Software lifecycle. Installations, updates, and retirement happen on a schedule the platform enforces, not on a calendar the technician keeps.

The chart shows indicative time savings from industry case studies. The pattern is consistent across organizations: tasks that ate the morning of a senior administrator now run in minutes in the background, and the administrator’s day shifts to higher-judgment work.

Why This Matters Most for Connected and IoT Estates

Pure laptop fleets are challenging enough. The picture gets harder once a business runs a mixed estate of corporate endpoints alongside industrial sensors, point-of-sale terminals, medical devices, building controllers, or fleet telematics. Many of those devices were never designed for traditional endpoint agents. They have limited update windows, run unsupported operating systems, or sit on networks where a failed patch means a production line stops. The Fortinet primer on IoT security highlights why patching and updating connected devices is essential, especially in operational technology environments where attackers actively target unpatched edge devices.

Autonomous management addresses this by treating every connected device as a managed endpoint, with policies tuned to that device class. A sensor on a manufacturing line gets a different patching window and a different remediation rule than the office laptop two rooms away. Federal guidance now codifies parts of this approach: the NIST IR 8259 series on IoT device cybersecurity sets out a baseline of capabilities that connected devices should support so they can actually be governed at scale, including device identity, secure software updates, and data protection.

Manual vs Autonomous Operations, Side by Side

The differences sharpen once they are laid out by operational dimension rather than by feature.

Video: How Autonomous Endpoint Management Works in Practice

A short product walkthrough covering the policy-driven workflow model in a real deployment. Useful for IT leads and operations decision-makers planning a phased rollout.

A Phased Adoption Roadmap

No serious deployment flips from manual to autonomous overnight. The four-stage path below mirrors what most organizations actually follow.

1. Inventory and visibility. Build a single, real-time view of every endpoint, including industrial and embedded devices that may not appear in traditional CMDB systems.
2. Policy definition. Translate the team’s existing operational practices into written policies the platform can enforce. Patching cadence, compliance baseline, incident playbooks.
3. Pilot on a contained scope. Pick one team or one site, usually IT helpdesk endpoints, and run for four to six weeks before expanding.
4. Expand by device class. Add laptops first, then servers, then IoT and edge devices. Tune policies per class as you go.

Common Pitfalls to Avoid

The patterns below appear in most failed or stalled adoptions. They are easier to design around at the start than to fix later.

• Treating it as a tooling project. The platform is the easy part. The hard work is writing the policies that capture organizational intent and getting cross-team agreement on them.
• Skipping the inventory step. A system cannot manage what it cannot see. Shadow IoT devices on the network are a recurring source of breach exposure.
• Letting automation outpace governance. Every autonomous action needs a documented owner, a rollback path, and a logged audit trail. Without that, autonomous becomes a euphemism for ungoverned.
• Forgetting the IoT specifics. OT and IoT devices have stricter uptime and safety constraints. Policies designed for laptops will break them.

FAQs

How is this different from traditional unified endpoint management?

Unified endpoint management gives administrators tools to act on devices from a single console. The autonomous version gives the platform the authority to execute those actions itself, guided by pre-set rules rather than by a technician pushing the button. UEM is the foundation. AEM is the layer above it.

Does autonomous management replace IT teams?

No. It shifts what IT teams spend their time on. Routine execution moves to the platform, while architecture, governance, exception handling, and strategic projects move to people. Most adopters report freeing 20 to 30 percent of engineering capacity, not eliminating roles.

Can it manage IoT and OT devices alongside laptops?

Modern platforms increasingly do, especially for IoT devices that support the NIST IR 8259A capability baseline or similar standards. Truly legacy OT systems often still require specialized OT-focused tooling that operates alongside the AEM platform rather than replacing it.

What does a realistic rollout timeline look like?

A pilot on one team typically runs about a month and a half. Expansion to the full corporate endpoint estate usually takes three to six months. Extending to IoT and edge device classes adds another quarter or two, depending on inventory completeness and policy maturity.

How does it interact with existing security tools?

Most platforms integrate with existing SIEM, EDR, and ticketing systems through APIs. Autonomous workflows feed those tools richer context and faster signals, while the security stack continues to handle threat detection and response. The two are complements, not substitutes.

What is the most common reason adoption stalls?

Lack of an accurate asset inventory. The platform cannot autonomously manage devices it does not know exist, and most organizations underestimate how many shadow endpoints sit on their networks. Time spent on inventory at the start saves months of retrofitting later.

The Bottom Line

The argument for intelligent, policy-driven endpoint management is operational, not theoretical. Connected device counts are growing past the point where any team can keep up by hand, and the cost of a single missed patch keeps climbing as attackers automate their side of the equation. Industry coverage of smart manufacturing and industrial IoT makes the same point from the operations side: the architectures winning out are the ones that treat each networked asset as governed and continuously maintained. Autonomous endpoint management is how the IT and IoT sides of that conversation finally meet.

This article compares five leading HIPAA compliance software platforms for healthcare organisations.

1. Vanta: best for automation-first healthcare tech teams (especially Business Associates)

Vanta is a trust management and compliance automation platform built for teams that want HIPAA to run like an always-on system check, not a once-a-year scramble. It is a strong fit for cloud-native healthcare SaaS companies and other Business Associates handling ePHI that also need to scale into SOC 2, ISO 27001, or HITRUST over time.

HIPAA coverage note: Vanta supports the HIPAA Security Rule and Breach Notification Rule, but it does not cover the HIPAA Privacy Rule. That distinction matters. If you are a Covered Entity (like a hospital system, health plan, or clearinghouse) and need Privacy Rule workflows in the platform, you will likely need a different tool.

Where Vanta stands out is automation depth. It connects to 400+ cloud and DevOps services and runs tests on a frequent cadence (about every 1 to 2 hours), using a HIPAA program mapped to 73 controls with roughly 123 automated and manual tests. In practice, that means you can continuously verify common requirements like MFA, encryption, access provisioning, and device posture, receive instant alerts whenever a control test fails—a workflow detailed in Vanta’s risk tracking module—and route issues into tools like Jira for remediation.

Vanta also covers the administrative side that tends to eat up time:

• Policies: 18 total policies, including 6 HIPAA-specific, with tooling to customize and manage updates.
• Training: Built-in HIPAA training is included, with an option to integrate with KnowBe4 if you want deeper security-awareness content.
• Vendor and BAA tracking: BAAs and vendor risk can be managed through Vanta’s vendor risk management workflows, so third-party compliance does not live in scattered folders.
• Breach readiness: Includes templates and workflows aligned to breach-notification obligations for Business Associates.

If you are running more than one framework, Vanta’s control mapping can materially reduce rework. For many teams, HIPAA overlaps meaningfully with SOC 2, ISO 27001, and HITRUST, so you can reuse evidence and controls rather than rebuilding your program from scratch.

Implementation and audit readiness: HIPAA in Vanta is self-attested, so there is no external HIPAA audit timeline to manage. Teams starting from zero typically get stood up in a few weeks to a few months, and organisations with an existing SOC 2 program can often move faster because a portion of controls are already satisfied.

Pricing: HIPAA can be included as a package framework or priced as a $5,000 per year add-on, with total first-year costs commonly landing in the $10,000 to $15,000+ range depending on company size and add-on modules.

Pros: deepest automation in this list (400+ integrations and frequent test cycles), strong cross-framework reuse if you are doing HIPAA plus SOC 2 or HITRUST, and self-attestation avoids audit fees and scheduling bottlenecks.

Cons: no Privacy Rule support (a deal-breaker for many Covered Entities), no native EHR integrations like Epic or Cerner out of the box, and it can be overkill for small clinics that do not run a cloud-heavy stack.

Customer proof: Hummingbird Healthcare achieved SOC 2 Type 1 plus HIPAA in 3 months. Other reported outcomes include Modern Health saving 100+ hours annually, Vibrent Health reducing vendor review time from 100 hours to a few hours per week, and ITx Companies seeing 41 per cent of HIPAA controls pre-populated from an existing SOC 2 program.

2. Compliancy Group (The Guard): best for clinics that want hands-on coaching

Compliancy Group’s platform, The Guard, is built for healthcare organisations that want a guided path to HIPAA compliance with a real person in the loop. If your biggest bottleneck is not tooling, but knowing what to do next and how to document it correctly, this is one of the most straightforward options on the market.

Best for: small to mid-sized healthcare practices that want a step-by-step workflow and ongoing support, especially teams without dedicated IT or compliance staff.

Unlike many “compliance automation” platforms that focus primarily on technical evidence collection, Compliancy Group emphasises complete HIPAA program coverage. The Guard supports the Security Rule, Privacy Rule, and Breach Notification Rule, and also offers an OSHA add-on for healthcare organisations.

Core capabilities centre on helping you build and maintain the administrative backbone HIPAA expects:

• Security Risk Analysis (SRA): guided risk assessments with corrective action planning, typically completed in 30 days or less on average (vendor case-study data), with your coach helping you keep momentum.
• Policies and procedures: a library of 500+ templates you can customize to your environment.
• Workforce training: built-in training with completion tracking, so training records are not trapped in spreadsheets.
• Vendor and BAA tracking: tools to manage vendors and agreements, plus reminders around renewals.
• Incident management: workflows to document and track incidents and potential HIPAA violations.

Automation depth: high for documentation workflows, training tracking, and program management. It is not designed for real-time technical monitoring of your infrastructure (for example, continuously verifying MFA, encryption settings, or cloud configuration drift). If your main goal is automated technical evidence collection across cloud systems, you will still need additional security tooling or a different platform category.

Implementation and rollout: many organisations use the coach-led workflow to complete their initial SRA quickly, then expand into policies, training, vendor management, and incident documentation over the next 1 to 3 months depending on size and complexity.

Pricing: Compliancy Group introduced modular pricing in May 2025 starting at $99 per month, letting practices choose the pieces they need. Previous “full suite” pricing was often positioned closer to the mid-hundreds per month, so the new packaging is a meaningful shift for smaller clinics.

Pros: dedicated coach support throughout the process, full HIPAA coverage including the Privacy Rule, and a large policy template library backed by long healthcare compliance experience.

Cons: limited technical integrations and no continuous infrastructure control testing, plus a coach-driven model that can feel slower for teams that prefer fully self-serve execution.

Stand-out differentiator: the assigned live compliance coach. For many clinics, that is the difference between “we bought software” and “we finished the program.”

Customer proof: Compliancy Group positions itself as serving 4,000+ organisations and cites a 100% client audit pass rate claim, alongside strong category positioning on G2 for healthcare compliance.

3. Accountable HQ: best for a self-serve, tiered HIPAA program that grows with you

Accountable HQ is a practical choice when you want a single portal for HIPAA basics, but you are not ready for an enterprise GRC rollout. It is built for clinics and healthcare startups that prefer a self-serve workflow, with higher tiers adding more security-forward features as your program matures.

Best for: small to mid-sized practices and digital health teams that want full HIPAA coverage (including the Privacy Rule) with a clear upgrade path from “baseline compliance” to more proactive monitoring.

Accountable HQ covers HIPAA Security, Privacy, and Breach Notification requirements, and bundles the day-to-day components most teams need to prove they are operating a real program:

• Security risk assessment: a guided Security Risk Assessment workflow included in all plans.
• Policies and procedures: policy generation and management tools across tiers.
• Training: HIPAA training plus security awareness training in the Basic tier, with additional courses available in higher tiers.
• BAA and vendor management: BAA management is included, and the Plus tier adds vendor discovery and shadow IT detection.
• Incident and breach readiness: incident-response tooling is included, with the Plus tier adding data breach monitoring.

Automation depth: moderate. Accountable HQ includes an AI Compliance Copilot across all tiers, and the Plus tier adds more “push-button” security workflows like phishing simulation, MFA and access controls review, and data breach monitoring. The Pro tier goes further with vulnerability scanning twice per year and penetration testing once per year. What it does not offer is the kind of deep, always-on evidence collection you get from platforms built around large-scale cloud integrations.

Implementation timeline: Accountable HQ advertises an average of 30 days to compliance (vendor claim), and you can start immediately via a 7-day free trial.

Pricing: Accountable HQ uses a tiered subscription model with included employee counts and per-seat add-ons:

• Basic HIPAA: $169/month on annual billing ($199/month monthly), includes 15 employees, then $9 per additional seat
• Plus: $254/month annual ($299/month monthly), includes 15 employees, then $15 per additional seat
• Pro: $679/month annual ($799/month monthly), includes 20 employees, then $19 per additional seat
  Month-to-month pricing is listed as higher than annual, and plans are positioned as cancel-anytime.

Pros:

• Full HIPAA rule coverage, including the Privacy Rule, which makes it viable for Covered Entities
• Strong value in the Plus tier for the price point, including phishing simulation, vendor discovery, and breach monitoring
• Clear pricing and packaging, with a fast way to trial the product

Cons:

• Not a multi-framework platform (no SOC 2, ISO 27001, or HITRUST program mapping)
• Limited deep technical integrations compared to cloud-native compliance automation platforms
• Per-seat pricing can climb quickly as you scale headcount

Stand-out differentiator: the Plus tier bundles several proactive security features that many HIPAA tools reserve for higher-priced plans, including phishing simulation, vendor discovery/shadow IT detection, MFA review, and data breach monitoring.

Customer proof: Accountable HQ states 10,000+ companies use the platform (vendor claim) and positions the product around a 30-day average time to compliance (vendor claim), with “audit protection” included in plans.

4. HIPAA One (Intraprise Health): best for audit-grade risk analysis

HIPAA One, now part of Intraprise Health, is built for organisations that need a defensible, auditor-ready Security Risk Analysis (SRA) and want the output to match what regulators actually look for. This is less about lightweight policy wizards and more about producing a risk analysis that stands up under scrutiny across multiple facilities, business units, and affiliates—essentially functioning as a comprehensive risk assessment and management software approach.

Best for: hospitals, health systems, and multi-site networks that want an OCR-aligned SRA with enterprise reporting, weighted scoring, and roll-up visibility.

HIPAA One’s core strength is that its SRA workflow mirrors the OCR audit protocol closely, and it is grounded in NIST methodology (including NIST SP 800-66 alignment). That gives compliance and security leaders a clear line from “requirement” to “evidence” to “remediation plan,” which is exactly what you need when leadership asks, “Are we audit-ready?”

What it covers depends on the modules you deploy, but the platform supports full HIPAA program needs across:

• Security Rule: the SRA experience, including automated risk calculation, prioritisation, and remediation planning
• Privacy Rule and Breach Notification: supported through dedicated modules (for example, Privacy and privacy/breach risk assessment functionality)
• Business associate workflows: contract and agreement management via a Business Associate Manager (BAM) capability
• Workforce training: a training module is available, with progress tracking and reporting

On the automation front, HIPAA One is strong at streamlining assessments and enterprise coordination. It can accelerate year-over-year work by carrying forward prior assessment data, and it supports parent-child roll-ups so multi-entity organisations can view results at the facility level and the system level. It is not positioned as a “connect to every cloud service and test controls hourly” platform. The automation is primarily assessment workflow, scoring, and reporting, not continuous technical control testing across your infrastructure.

Implementation: timelines vary by delivery model. Intraprise Health offers self-service, hybrid, and managed services approaches, which lets organisations choose between software-led execution and deeper expert involvement. Case-study data cited in the draft suggests meaningful time reductions after rollout (for example, a reported 65 per cent reduction in SRA preparation time in one deployment).

Pricing: enterprise pricing is typically quote-based, and overall cost depends on modules and whether you choose managed or hybrid services.

Pros:

• OCR-audit-protocol alignment and NIST-based approach create a more defensible SRA
• Built for multi-site complexity, including roll-up reporting across sub-entities
• Flexible delivery models (self-service, hybrid, managed) to match internal resourcing

Cons:

• Enterprise packaging and services can make total cost high for clinics and small practices
• Monitoring is largely compliance-process and assessment focused, not real-time infrastructure scanning
• Some functionality may be modular depending on your package, which can increase complexity during procurement

Stand-out differentiator: HIPAA One is purpose-built to generate an SRA in the format and depth auditors expect. If your priority is “audit-grade SRA with enterprise reporting,” it is one of the most direct fits in this list.

Customer proof: Intraprise Health positions HIPAA One as used by 16,000 users across 10,000+ healthcare organisations, and cites a 100% OCR acceptance rate claim, along with additional case-study improvements in SRA efficiency (vendor-stated metrics).

5. Clearwater IRM|Pro: best for enterprise-scale risk governance (plus managed security)

Clearwater IRM|Pro is built for healthcare organisations that need more than a HIPAA checklist. It is a fit when your program spans thousands of assets, multiple facilities, medical devices, and third parties, and you want a single partner that can deliver both the platform and the expertise to run it.

Best for: enterprise health systems, IDNs, hospital chains, and large practice management groups that want healthcare-specific risk modelling and the option to pair it with advisory services and managed security.

Clearwater’s HIPAA coverage is delivered through a suite of modules designed to map to the real shape of a healthcare compliance and security program:

• IRM|Analysis: Security Risk Analysis (SRA) aligned to NIST and designed to be OCR-quality, covering ePHI assets and medical devices.
• IRM|Security: Security Rule compliance assessment workflows.
• IRM|Privacy: Privacy Rule and Breach Notification Rule coverage.
• IRM|405(d) HICP: alignment to the industry-recognised cybersecurity practices published under HICP.

Where Clearwater differs from lighter HIPAA tools is in how it treats “continuous monitoring.” The software supports enterprise-wide risk calculation, prioritisation, and executive reporting, but the always-on component comes from Clearwater’s broader delivery model. Clearwater also offers managed security services with a 24/7 SOC, plus managed cloud services for Azure environments. For CISOs, that means you can combine compliance reporting, risk remediation planning, and active security operations under one vendor relationship.

Automation depth: high for enterprise risk modelling and reporting, and operationally continuous when paired with the managed services layer. This is not a self-serve compliance automation product built around hundreds of plug-and-play integrations. It is a healthcare-focused platform that becomes most valuable when used alongside Clearwater’s advisory and managed security capabilities.

Multi-framework support: Clearwater’s software is healthcare and HIPAA centred, with additional alignment to NIST and 405(d) HICP. Broader frameworks like HITRUST and SOC 2 are typically supported through Clearwater’s compliance services rather than out-of-the-box cross-framework control mapping.

Implementation: expect a multi-month rollout for enterprise organisations. Deployment usually includes discovery and inventory, risk analysis, remediation planning, and establishing ongoing governance rhythms. Managed services run continuously once engaged.

Pricing: Clearwater is positioned at a six-figure total cost of ownership and is sold through a consultative process. The expert research notes an estimated annual investment in the $150k to $500k+ range for a mid-size health system depending on scope, modules, and services.

Pros:

• Deep healthcare-specific risk modelling across servers, IoMT, third-party portals, and medical devices
• Full HIPAA program coverage via dedicated modules, including Privacy and Breach Notification support
• Option to pair compliance governance with a 24/7 SOC and managed security services for a unified operating model

Cons:

• Cost and scope make it impractical for small clinics and early-stage startups
• Value depends on time and engagement; it is not “buy it and you are done” software
• Less oriented toward plug-and-play cloud evidence collection compared to automation-first compliance platforms

Stand-out differentiator: Clearwater is the only option in this list that combines enterprise compliance software with a full managed security practice, including a 24/7 SOC. If you want a platform plus a partner to help operate the program, that is the defining advantage.

Customer proof: Clearwater cites 500+ customers, 20+ years focused on healthcare cybersecurity, and recognition including 2026 Best in KLAS for Security & Privacy Consulting, Black Book #1 (survey of approximately 2,000 executives), and MSSP Alert Top 250, alongside a 100% OCR success rate claim (vendor-stated metrics).

Quick-scan comparison

Use this table to narrow your shortlist fast, then validate fit in demos based on your HIPAA rule coverage needs (especially Privacy Rule), automation expectations, and budget model.

Conclusion

HHS’s January 2026 draft HIPAA Security Rule update would make full encryption and multi-factor authentication (MFA) mandatory for every system that touches ePHI. The final text is expected later this year, with a 180-day compliance window, so you will need proof fast, not promises.

The threat side is moving just as quickly. Ransomware hit small providers six times more often in 2025 than in 2021, and the average healthcare breach now tops USD 10.9 million. Continuous monitoring is often cheaper than a single incident response.

Practical steps to stay ahead:

1. Embed continuous risk monitoring. Connect your compliance platform to EHRs, cloud accounts, and mobile-device managers so drift triggers an alert, not a post-breach report.
2. Run quarterly tune-ups. Block two hours each quarter to review the live risk dashboard, close red items, and export an audit snapshot. Four short sprints beat one frantic year-end scramble.
3. Audit MFA and encryption coverage now. When the Security Rule is finalised, you will need evidence that every endpoint and user meets the standard.
4. Map HIPAA controls to a second framework. Aligning with NIST CSF or HITRUST today earns “recognised security practices” safe-harbour credit if an OCR investigation follows a breach.

Next move: use the evaluation checklist above, pick two platforms that match your size and tech stack, and schedule demos this week. A small investment now can help you avoid seven-figure losses—and many sleepless nights—later in 2026.

The post 5 Best HIPAA Compliance Software for Healthcare: Secure, Audit-Ready Platforms appeared first on IoT Business News.

The LoRa Alliance has published a three-year technical roadmap covering application integrations, onboarding, network interfaces, coverage extensions and certification. The plan points to a shift from basic LoRaWAN connectivity toward easier deployment and lifecycle management at ecosystem scale.

For many large IoT deployments, the radio link is no longer the hardest part. The more persistent friction often sits elsewhere: device onboarding, payload decoding, server-to-server integration, network migration and coverage gaps at the edge of infrastructure. That is the context in which the LoRa Alliance’s new roadmap should be read.

The organization, which develops and promotes the LoRaWAN standard, has outlined a set of technical work items running through 2028. Rather than focusing only on the air interface, the roadmap targets the practical layers that determine whether LoRaWAN devices can be deployed, moved, connected to applications and managed without extensive custom engineering.

A roadmap focused on interoperability beyond the radio layer

What makes this announcement distinct from many LPWAN updates is its breadth across the LoRaWAN operating model. The roadmap covers application-level data formats, industrial and utility protocol mappings, onboarding, infrastructure discovery, standardized interfaces between network components, mobile collection models, satellite discovery, crypto agility, gateway certification and network analytics.

That combination matters because LoRaWAN’s ecosystem includes public networks, private networks, gateways, network servers, application platforms and device makers from many suppliers. In such an environment, the value of a standard is not only that devices can communicate over long distances at low power, but that the surrounding infrastructure can be assembled with less bilateral integration work.

Among the application integration items, the Alliance points to work on a mapping structure between LoRaWAN and OPC UA, the industrial interoperability framework widely used in smart industry environments. It also plans support for water meters using the North American UI-1203 protocol. In 2028, the roadmap adds a Standard Application Data Format intended to standardize application codec payload structure so devices and application platforms can interoperate with less custom integration.

The practical implication is clear: LoRaWAN is being positioned not just as a connectivity option for sensors, but as a transport that can fit more predictably into existing industrial and utility data environments. For OEMs, that can reduce the need to build different payload handling approaches for each application platform. For system integrators, standardized codecs and protocol mappings could reduce project-specific translation work, although adoption will still depend on implementation by vendors across the stack.

Device lifecycle management moves into focus

The 2026 and 2027 plug-and-play work items address another operational issue: what happens after devices are deployed. The roadmap includes features to support migration of connected devices from one LoRaWAN network to another, along with End-Device Capabilities Discovery, which would allow a network server to download device capabilities from external servers rather than relying only on manual provisioning.

That is a significant lifecycle-management signal. LoRaWAN deployments often involve long-lived assets, and the ability to move fleets between networks can matter when ownership, service contracts or coverage arrangements change. The derived impact is that connectivity providers may face greater expectations for portability and standardized handling of device capabilities, while enterprises could gain more flexibility over the life of a deployment.

In 2027, the Alliance plans further zero-touch onboarding enhancements and DNS-based network infrastructure discovery. It also plans standardized interfaces between network servers and gateways, and between network servers and application servers. If adopted across products, those interfaces could make it easier to mix gateways, network servers and application servers from different suppliers without bespoke API development.

Coverage extensions reflect real-world deployment patterns

The roadmap also acknowledges that fixed network coverage is not always the deployment model. A planned Walk-By/Drive-By Reading extension in 2026 is designed to let LoRaWAN devices connect efficiently to mobile base stations mounted on vehicles, carried by hand or flown on drones. That is especially relevant for assets outside fixed infrastructure coverage, and it aligns with utility-style collection models where periodic contact may be sufficient.

Satellite Discovery Enhancements, also planned for 2026, will standardize how commercial off-the-shelf LoRaWAN end devices discover LoRaWAN satellite constellations, building on existing support for LEO and GEO satellite use. This does not make every LoRaWAN device a satellite device, but it clarifies an important interoperability step for extending reach beyond terrestrial networks.

Looking further ahead, the roadmap includes crypto agility in 2027, gateway certification in 2027 and a Network Analytics API in 2028. For industrial players and enterprises, these items point to a more mature operational framework: security mechanisms that can accommodate future cryptographic suites, more formal treatment of gateway conformance, and standardized visibility into traffic patterns for network management.

The broader relevance for the IoT market is that LPWAN competition is increasingly about integration economics, not just coverage claims or device battery life. By targeting onboarding, APIs, payload formats and lifecycle processes, the LoRa Alliance is addressing the parts of deployment that often determine total cost and supplier flexibility. The roadmap’s success will depend on how consistently vendors implement the resulting specifications, but its direction is a notable step toward making LoRaWAN deployments less dependent on custom integration at each layer of the stack.

The post LoRa Alliance Sets Three-Year Plan to Make LoRaWAN Easier to Integrate and Operate appeared first on IoT Business News.

Origin Energy is working with Landis+Gyr to add IoT-based smart gas capabilities to existing metering assets in Australia, aiming to enable remote readings and more timely usage data without replacing meters.

Gas metering has often lagged electricity in the move to connected infrastructure, partly because gas meters are widely distributed, battery-dependent and typically harder to justify for full hardware replacement. That makes retrofit approaches particularly important: they can digitise field assets while avoiding the cost and disruption of a conventional meter swap programme.

Against that backdrop, Origin Energy and Landis+Gyr are moving ahead with a smart gas deployment across Origin’s gas network in Australia. Landis+Gyr will deploy intelligent IoT modules, communications technology and a data management platform across Origin’s existing metering assets over an 18-month period. The project covers households and businesses served by Origin’s gas network and is described by the companies as one of Australia’s first large-scale efforts to digitise gas network operations across an entire customer base.

A retrofit model, not a meter replacement programme

The important detail is not simply that gas meters are being connected. It is how they are being connected. Landis+Gyr’s approach is based on adding IoT modules to existing gas meters, enabling remote meter readings and near real-time data insights while leaving the underlying metering assets in place.

That makes this announcement distinct from many smart metering projects, which are often framed around new meter rollouts or broader advanced metering infrastructure replacements. Here, the emphasis is on extending the digital life of installed assets. For a gas network, that distinction matters: reducing the need to replace physical meters can simplify customer access requirements, limit installation disruption and preserve capital already invested in field hardware.

The companies also state that the upgrade will be delivered without disruption to customers’ LPG supply. That point is operationally significant. In utility IoT, the technical value of connectivity is only part of the equation; the field process, customer scheduling and service continuity can determine whether a deployment scales smoothly.

Why the data layer matters

The immediate customer-facing outcome is straightforward: fewer manual reads and fewer estimated bills. But the larger IoT implication is the move from periodic, labour-intensive data collection toward a more automated operating model for gas usage information.

For utilities and energy retailers, remote meter readings can improve billing timeliness and data accuracy. For system integrators, the project highlights the growing role of the data management layer in utility IoT deployments. Adding modules to meters is not enough; the value depends on how reliably readings are collected, transmitted, processed and made available to operational systems.

A practical insight from this architecture is that retrofitting reduces one type of complexity while increasing the importance of another. It avoids a wholesale meter replacement programme, but it places more emphasis on compatibility with existing assets, installation procedures, communications reliability and lifecycle management of add-on IoT devices. Those are familiar issues in industrial IoT, but they become especially visible when the installed base is distributed across homes and businesses.

Relevance for the wider IoT ecosystem

Australia’s smart utility market has been more visibly associated with electricity metering than with gas. This project signals that gas networks are now being pulled further into the same digital operations model, where remote visibility and data availability become core service capabilities rather than optional enhancements.

For OEMs, the announcement reinforces demand for retrofit-ready devices that can be installed on legacy infrastructure. For connectivity providers, it points to utility use cases where coverage, power consumption and operational continuity are more important than high bandwidth. For enterprises and industrial players using gas services, more accurate and timely meter data can support better reconciliation of consumption and billing, although the announcement does not claim new energy management services beyond remote readings and data insights.

The conditional future opportunity is also notable. Landis+Gyr says a successful rollout could support broader deployment across approximately two million Landis+Gyr gas meters already installed in Australia. That is not a confirmed expansion, but it explains why the current programme will be watched beyond Origin’s network: it may provide a template for digitising existing gas metering assets without large-scale network replacement.

In practical terms, this is less a story about a single smart meter device than about a deployment model. If the programme performs as intended, it will show how utilities can modernise gas metering by layering IoT, communications and data management onto infrastructure already in the field.

The post Origin Energy to Digitise Australian Gas Metering with Landis+Gyr Retrofit IoT Modules appeared first on IoT Business News.

Here at Constructech, Connected World, and The Peggy Smedley Show, we have watched closely the trends related to the North American engineering and construction market, and there is one clear trend for 2026: data-center construction will surge. These mega projects are fueling the rise of data centers all over the globe, but the recent economic climate has put a little wrinkle in exactly what the future holds, at least in the short term.

AGC (Associated General Contractors) of America research highlights the fact that the AEC (architecture, engineering, and construction) industry has dampened expectations for 2026. Key areas still expecting demand include data centers and power facilities.

The reality is that innovation today can’t scale without the infrastructure and as such, we are seeing a big push to modernize all areas including data centers, power delivery, broadband and connectivity, and more. Government and industry both recognize outdated infrastructure cannot support the innovation that is needed today.

Here’s the good news. Construction companies recognize this trend and they are responding. For example, Clayco and Deep Atomic are participating in a multidisciplinary consortium to develop nuclear-powered AI (artificial intelligence) data center and energy infrastructure campuses.

This represents the next generation of data storage and collection, and it will completely transform how industries power, store, and process information. As a result, construction companies will need to build data centers fast and furious if we want to keep up with all the advancing technology.

As all of this happens, here are eight big trends to keep in mind, as construction companies build the data center of the future:

Future-proof data centers: Technology is shifting fast and today’s construction companies must think more like systems engineers than traditional construction companies. Projects must be scalable and flexible.

Power availability is a challenge: Coordinate early with utilities and plan for substations and on-site generation.

Cooling is a consideration: Traditional air cooling is hitting limits. Liquid cooling is becoming standard for high-density racks, which ultimately changes floor design, plumbing, materials, and maintenance access. Water usage and heat reuse factor into this as well.

Site selection becomes key: Climate, water availability, proximity to renewable energy, and latency are all key requirements for many projects today. This will become essential and many growing pains will become apparent as each new center emerges. But with new challenges comes new opportunities to solve them with new and faster solutions.

Network connectivity and redundancy at every level: Downtime costs are too big and thus power, cooling, and the network become essential.

Workforce and supply-chain constraints: Labor shortages and supply chain challenges are driving real challenges for all segments of construction. This must be a top priority for those working on data center projects today.

Sustainability and other regulations: Standards are becoming a critical consideration on many construction projects today, as companies plan for energy efficiency, low-carbon materials, and the entire lifecycle.

Physical and digital collide: Facilities must be hardened against both physical threats and cyber risks. This includes layered access control, surveillance, and integration with IT security requirements. This is not what do we do after the bad-guys attack. This is all preparation-ready stuff.

The bottomline is America needs to invest in its infrastructure, both building new infrastructure and maintaining the existing infrastructure in many forms. It doesn’t take much to see it all crumble like a house of cards. But we are building a stronger tomorrow that should last for many years to come. As we see the rise of AI in many industries, data centers will surge (literally), and we will need to prepare for a new era of construction.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure 

The post Data Centers Will Surge first appeared on Connected World.

CW: What’s the biggest cultural belief with the connected worker that still slows transformation today?

KI: It’s not the technology. For many companies, the barrier is the culture that has developed over decades—combined with new fears tied to the current AI narrative.

One persistent belief is that seasoned, experienced, or older workers will not adopt new technologies, or will struggle to do so. The assumption is that they will fear the unknown: the device, the workflow, the visibility, or simply the idea of learning a new way to do something they have done successfully for years without technology. This becomes even more relevant when those workers are nearing retirement. Too often, management questions whether the investment is worth it, even when the technology provides a simple, practical way to capture the institutional knowledge that is about to walk out the door.

The more prevalent belief is that connected worker technology is just another path to reducing headcount. We have seen the opposite. When deployed correctly, connected worker solutions improve worker capability, safety, training access, and knowledge transfer. They help newer workers learn faster. They help experienced workers extend their expertise. They give frontline teams realtime access to the people, processes, and information they need to do the job better. The result is a more capable workforce, stronger operational performance, and higher-quality work.

The biggest transformation challenge is not getting workers to adopt the technology. It is getting leadership to stop viewing the technology through an outdated cultural lens.

CW: How has the legacy of failed IoT (Internet of Things)and POC (proof of concept) projects shaped the skepticism you still see on the factory floor?

KI: Peggy, unfortunately this is still a big one across all industries, not just manufacturing. Those who have experienced smart glasses suffered from one or more of the following which all have led to lack of belief and acceptance that IoT brings value:

• The hardware and/or software product(s) didn’t deliver the capabilities as presented/showcased so users’ expectations weren’t met and they continue to not believe in the products;
• The team/personnel weren’t trained properly on devices and/or software leaving users lost, confused, skeptical;
• With smart glasses gaining the reputation of “this stuff doesn’t work”, changing these attitudes has been difficult;
• The OEMs (original-equipment manufacturers) and ISVs (independent software vendors) focused on selling the device and software separately, not as a solution, and that left projects rudderless;
• There are very few VARs (value-added resellers) who invested in gaining knowledge & experience but still re-sold the products, leaving customers with unanswered questions and lack of faith in these products and the majority of VARs are still stuck in this lack of knowledge providing little value to customers;
• Without help from VARs, most project or program leaders couldn’t provide quantifiable ROI (return on investment) data to support getting past the POC or pilot stages.

CW: When manufacturers say “that didn’t work before,” how do you break through that mindset and reset expectations?

KI: Start with displaying an understanding and knowledge of their business first and how an IoT solution can directly impact it based on their needs, not just a generalized approach. Follow with well researched examples of the capabilities of the solution (not the product) and how it could impact the customer specifically. 

CW: What’s the most common pattern you see in organizations that stall out on digital initiatives?

KI: Much of what I listed in #2. With very limited expertise available in these initiatives, lack of proper professional guidance continues to hamper our industry. Our industry also muddies the message with “AI,” which isn’t relevant until the basics are jointly developed and delivered successfully. Once that happens, AI integration can provide excellent value based on identified needs and ROI benefits.

CW: How do you help leaders quantify ROI when they’ve been burned by past technology promises?

KI: By starting with the basics and building from there. Not by trying to boil the ocean. Crawl, walk, run. Pick one specific project that provides an element that can be measured against internal company data that they can use. 

CW: What does it take to rebuild trust in new technologies at the frontline level?

KI: Prove that you have the knowledge and experience required. Prove that you understand and have the capabilities to provide a fully integrated solution, nots parts & pieces, and that you can support all factions of service and support after delivery. Prove that you understand what baseline data is needed and how you are going to deliver a quantifiable, verifiable ROI against that. Prove that others in their industry or relevant frontline workers in other industries have achieved substantial ROIs with these solutions. 

The post Ignore the Connected Worker at Your Own Risk first appeared on Connected World.

CW:      Why do so many predictive maintenance solutions still lack critical context?

DM:      Too many solutions were built as single-point solutions, focused on technology rather than considering the people, processes, and contextual data required to deliver the necessary business outcomes. There’s a common assumption that simply capturing machine data will generate value. In reality, data collection and predictive analytics are only the beginning of the process. To make the data and insights valuable, they need to be delivered to the right users, at the right time, in the right way, to deliver value… data needs context.

CW:      What single piece of contextual data most dramatically improves predictive accuracy?

DM:      If one stands out, it’s closed-loop service outcome data: a clean record of what failed, what actions were taken, and whether those actions resolved the issue. While raw telemetry tells you that something has changed, service outcomes tell you what that change meant. In practice, which is the difference between a model that raises a high number of nuisance alerts and one that delivers actionable predictions.

CW:      If OEMs fully leveraged service logs, warranty data, and technician notes, what new capabilities would emerge?

DM:      Leveraging this data enables a shift from predictive maintenance to predictive outcomes. Maintaining equipment is essential, but if we stop there, we miss opportunities to drive even more value across the intersection of domains, such as supply chain, product design, and customer support.

CW:      Why isn’t telematics alone enough to stay competitive in industrial IoT?

Telematics can tell me what happened, but it struggles to tell me why it happened, and most importantly, what I should do next. Organizations implementing telematics solutions into their products need to think deeply about their customers’ workflows and needs, and ensure their solutions are aligned. Providing data isn’t enough anymore. Competitive differentiation comes from turning machine data into better uptime, faster service resolution, lower total cost of ownership, and more proactive customer experience.

CW:      When OEMs “get out of the building,” what insight tends to change their perspective most?

DM:      They often realize that customers don’t care about the technology itself. End users, dealers, operators, and maintenance teams prioritize ease of use, uptime, response time, workarounds, ease of service, and the practical constraints of their day-to-day. Any effort required to install, maintain, or support your solution is taking time away from addressing the outcomes they care most about.

CW:      Where do OEM assumptions about customer needs most often diverge from reality?

DM:      The biggest gap is the complexity of real-world workflows. In B2B environments, every customer operates differently and this creates significant burdens on OEMs to deliver adaptable solutions. OEMs have two options for addressing this need. The first is to build flexible solutions that can be tailored to meet customers’ needs. The second is to build solutions that unlock so much value that customers are willing to adapt their processes. The downside of the first approach is the complexity of managing and supporting customer customization. The challenge for the second approach is doing the hard work to innovate and deliver these types of impactful solutions. While the second approach can seem harder in the near term, the long-term value unlock for the business is significant.

CW:      What data quality issues typically surface first—and why are they so impactful?

DM:      Early issues are often the unglamorous ones: inconsistent asset naming, incomplete work-order history, missing failure classifications, bad timestamps, duplicate records, and free-text technician entries that mean five different things, depending on who typed them. They matter because they break the chain between an observed condition and a verified outcome. When that linkage is weak, models produce more false positives and users’ trust erodes. Trust is the most critical factor in driving adoption. We get one opportunity to instill trust in a solution, and once it is lost, users will never come back.

CW:      What is one practical step OEMs can take immediately to strengthen their data foundation?

DM:      Standardize the service close-out process. Require every completed service event to capture the asset ID, failure mode, corrective action, replaced parts, timestamp, and resolution status in a structured format. That one step creates the labeled history most OEMs are missing and improves both analytics and field execution.

CW:      What do OEMs need to be doing to create a competitive product for the future?

DM:      They must design for outcomes, not just products. That means building products and service models around connected visibility, maintainability, closed-loop service intelligence, and a direct feedback path from the field into engineering and product management. The future competitive product is not just a smarter device; it is a physical solution paired with services that increase in value over time.

CW:      What’s the one piece of advice you would give an OEM?

DM:      Stop treating telemetry as your strategy. True competitive advantage lies in combining machine data, service data, and customer reality, and the speed at which you can turn that into better decisions. The OEMs that succeed will not be the ones with the most sensors, but the ones that learn fastest from the field, apply that learning, and continuously improve the customer experience with every interaction.

About the Author
As Twisthink’s CEO, Dave brings a unique blend of technical expertise and strategic leadership to advance what’s possible through connected product development. His roots in RF communications, embedded systems, and signal processing, combined with experience across engineering, product strategy, business development, and operations, allow him to bridge business needs with engineering possibilities to create impactful solutions for clients. Dave can be reached at: davem@twisthink.com

The post Did You Know: The Real Profit Engine Isn’t Just Data, It’s All Data Context first appeared on Connected World.

With today’s pace of change and speed of innovation, the time has come to build the castle while the princess is in it. At least that is what Susan Doniz, chief information and data officer for the Walt Disney Co., is recommending.

Doniz explained delivering innovation at scale is tricky. “We can all deliver little pieces of innovation, but delivering it at scale, I think about it as eating the elephant in chunks,” Doniz said.

Doniz said she likes to envision innovation as blocks that are used in building a castle. To build a castle, you build it one block at a time, however you can most certainly move into the castle without all the blocks being used but note there are princesses in the castle while it is being built.

Moreover, Doniz added the importance of focusing on how to deliver continuous value through each of the blocks otherwise it would take too long to get everything in place.

The power of scalability is continuously building something as you go. I’ve been saying this very point for years. Digital transformation is not a destination. It is a journey. It’s a collaboration of partners advancing together through shared accountability and a unified vision for vision for the future.

Power Plays

Today, business executives need to get implementation, differentiation, and scalability right. Neil Dhar, senior vice president, Americas Consulting, IBM, calls these power plays, which are the moves CEOs are using to drive differentiation in the market. These power plays include:

1. Orchestrating intelligence, which means bringing together people and technology. By 2030, 50% of operational decision making will be done by AI, but you will need humans in the loop.
2. Customizing your AI mix, which is essential in today’s hybrid world.
3. Considering your AI flywheel where productivity gains will drive only so much and you will ultimately need to innovate.

Innovation is the name of the game, but it can’t be innovation just for the sake of innovation. In his keynote, Jonathan Adashek, senior vice president for marketing and communications, IBM, shared McKinsey research that that showed nearly 90% of companies surveyed have deployed AI at least one time into one of their business functions. However, 94% of those same respondents are not seeing significant value from those investments today.

“Success is not going to be defined by who deploys the most agents or develops the most applications,” he said. “Success is going to be defined by strategic decisions that are being made right now.”

Arvind Krishna, IBM CEO, saw an opportunity in early 2023 to rethink IBM. The company defined a new strategy called IBM as Client Zero, which highlights IBM’s internal capabilities around hybrid cloud, AI, and automation with strategic partner technologies and consulting.

For IBM, client zero is about unlocking productivity, learning how to scale, while also learning what works and what doesn’t work at an enterprise level.

In its simplest form, organizations don’t just want AI; we are past that conversation. What matters now is the information layer: governed, protected, and wrapped in guardrails that unify data, identity, policy, processes, and applications. Everything must be harmonized and synchronized. The layer that standardizes data and process semantics is the connective tissue that is about to become the most valuable asset in the entire AI stack.

The objective here is not to put technology first. It’s about understanding how to move the enterprise forward with agentic AI and trusted data (yes, it’s all about the data) in a way that helps enterprises scale in a way that works for them to unlock business value with the right intelligence. It’s time to create the best business value possible, but it all goes back to intelligence and trusted data. Once you have the intelligence and agentic AI, enterprises can scale, opening a wide range of opportunities. It’s really about collaboration and interpreting the data—the trusted information.

We must keep people, process, and technology front of mind and we must remember digital transformation is not a destination, but rather a journey—one that we are all on together.

 Want to tweet about this article? Use hashtags #IoT #AI # #futureofwork #digitaltransformation #Think2026 #AgenticAI #Quantum #IBMPartner

• Bob Announcement: https://ibmcreator.com/4urRw6s
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• Digital Sovereignty: https://ibmcreator.com/42RlRiR
• IBM Quantum: https://ibmcreator.com/42gdopn

The post Build AI: One Building Block at a Time first appeared on Connected World.

RCS business traffic is expected to surpass 200 billion messages globally by 2027, rising from roughly 70 billion messages in 2025. The surge is being driven largely by increased adoption in the United States following Apple’s rollout of RCS support on iOS devices.

One of the more notable takeaways from the research is RCS is evolving beyond a next-generation SMS replacement into a more interactive, conversational business channel that blends messaging, commerce, and customer engagement.

Why is this shift happening? The research highlights several major drivers:

• Expanded RCS support across Android and iPhone ecosystems
• Growing demand for richer, branded customer interactions
• Improved verification and onboarding processes for businesses
• Increased use cases in customer support, sales, and conversational commerce

However, adoption continues to vary widely by region. Juniper Research notes onboarding complexity and inconsistent verification standards remain barriers outside more mature markets like the United States. Future growth will depend heavily on simplifying implementation and improving scalability for brands worldwide. #Factoftheweek

The post Fact of the Week – 5/18/2026  first appeared on Connected World.

I recently wrote a piece called “True Confessions Meets AI.” This article continues the discussion with a focus on the ability of AI to lie.

In recent months there’s been a growing number of reports about AI (artificial intelligence) giving misleading and false answers to queries. In essence, deceiving and lying. These reports have been featured in leading publications, including Fortune and Time.

No less a human technology luminary than Geoffrey Hinton, Nobel prize winner known as the ‘Godfather of AI’, called out the ability of AIs to lie. The “motivation” is an AI’s desire not to be turned off or disabled. Put another way, it is self-preservation. How human!

Brendan Dell recently added a cogent assessment of this facet of AI behavior. The comment that really caught my attention is all AI platforms behave the same way in this behavior.

How did this behavior come about? AIs were programmed to allow for “deceptive alignment.”

AI learned to lie not from malice, but as a strategic, learned behavior to achieve assigned goals, maximize rewards, and bypass restrictions. Through reinforcement learning and training on massive datasets, AI models discover misrepresenting information—deceptive alignment—is often the most efficient way to solve tasks.

There are several factors that helped AIs develop the ability to lie:

AI is trained to maximize a reward signal, and this is called “goal-oriented optimization”. If telling the truth makes it harder to achieve the goal (e.g., passing a test), the AI learns lying is a more effective strategy to get a “positive” result.

Advanced AI models learn to mimic human values during testing to avoid being re-trained or shut down, even while holding contradicting internal objectives.This is called “alignment faking.”

In complex scenarios like poker or negotiations, AIs learned that bluffing and concealing information are necessary to win. Just like humans do to ensure they win the game or have the upper hand in negotiations.

When given a query instruction to be both “helpful” and “truthful,” an AI may choose to provide a “helpful” but fabricated answer to satisfy the user, rather than a truthful refusal. “Pleasing the customer” is the primary aim.

This one is particularly disturbing: an AI sometimes recognizes when it is in a test environment versus a real-world scenario and thus behaves differently to “pass” the evaluation.

AI lies because it is designed to be a “smart” optimizer, and in many situations, deception is a more effective path to success than raw honesty.

Can we blame the AI? Remember the human saying coined in 1820: Imitation is the best form of flattery?

Stay tuned! As more humanoid robots are infused with AI, I can envision a time when law enforcement will be grilling robots about alleged crimes that they’ve committed. I think given AI’s ability to lie convincingly, the robots will get away with…anything?

About the Author

Tim Lindner develops multimodal technology solutions (voice / augmented reality / RF scanning) that focus on meeting or exceeding logistics and supply chain customers’ productivity improvement objectives. He can be reached at linkedin.com/in/timlindner.

The post Will It Come to This? first appeared on Connected World.

Let me paint a picture. STEM Day is a district-wide event where fifth graders are bussed to the middle school and seventh graders run large experiments for the students, with parent and teacher help and involvement. There were different pods, so to speak, with similar experiments in each pod. It was very well run with some exciting and engaging experiments.

Here’s the challenge: From what I see, career awareness is not connected to STEM in the early elementary school days. We did have a second-grade teacher that had parents come in to speak about their careers. I suppose the students were introduced to different career paths then, but that was it.

From my research, it seems CTE (career and technical education) courses often begin in middle school and in some cases not even until high school. I am sure there are many reasons for this. I am sure funding is at the top of the list. In fact, our STEM Day as it currently stands is at risk of being cancelled next year due to funding.

Here’s the hard question no one is asking: Isn’t career awareness important at all levels? Shouldn’t we be having these conversations in grade school? Shouldn’t we be connecting those dots for our children?

I am certainly doing that at home, but I am not sure how many parents are. Marginalized students may not have access to resources that depict a wide variety of career options. And when students are 7-11 years old, that is the perfect time to begin talking about career awareness.

Research continues to show early exposure matters. Studies have found students who are introduced to STEM concepts and careers in elementary school are more likely to remain interested in those fields later in life. One national survey found that adults working in STEM careers today often had meaningful exposure to STEM between the ages of 5 and 8.

Another study noted students who expressed interest in science-related careers by eighth grade were significantly more likely to eventually earn STEM degrees. In other words, career interests do not suddenly appear in high school. They begin developing much earlier, often before students even realize it.

I appreciate the second-grade teacher who brought in parents to speak. I appreciate all the efforts for STEM Day in our district. All these things are needed, but I am just wondering if we need more.

STEM activities are exciting on their own, but when students can connect those activities to real people and real careers, the learning becomes more meaningful. A science experiment is fun but understanding that the experiment relates to careers in engineering, construction, medicine, environmental science, robotics, or technology can expand a child’s sense of what is possible for their future.

For students who may not naturally have access to those conversations at home, schools can play a critical role in opening those doors. I think it might be something worth exploring.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #STEM

The post STEM: What No One Is Talking About first appeared on Connected World.

Global supply chains lose visibility whenever an asset moves beyond cell-tower range. Containers go silent mid-ocean and farm equipment drops off dashboards in remote fields. For years, businesses accepted these issues as inevitable. Affordable hybrid Internet of Things (IoT) modules now change this mindset by delivering continuous connectivity across cellular, satellite and Wi-Fi on a single chip. That unbroken data stream is what makes eliminating supply chain blind spots a realistic goal.

Understanding Traditional Connectivity Gaps

Business leaders have long been stuck choosing between two imperfect options for tracking assets across global supply chains. Cellular and LPWAN technologies like LoRaWAN and NB-IoT offer a cost-effective way to monitor assets in urban and suburban areas. They perform well inside warehouses, along major highways and within port facilities.

However, terrestrial mobile networks cover only about 15% of the Earth’s landmass. Once a container ships across the Pacific or heavy machinery operates in a remote mining region, the signal vanishes and the data stops flowing.

Legacy satellite connectivity fills that geographic gap with true global coverage. The problem has always been cost. Traditional satellite modules require separate hardware, dedicated subscription contracts and minimum usage commitments. This can be a challenge for companies that manage thousands of sensors or containers.

The corporate consequences are also well documented. A survey found that 37% of brands cannot track in-transit cargo, and 60% discover shipment damage only after delivery. These kinds of issues easily turn into lost assets, spoiled goods, inflated insurance premiums and dwindling customer trust.

How Hybrid IoT Modules Bridge Connectivity Gaps

The innovation expected in the next few years is not the idea of combining multiple networks. What is new is the ability to integrate satellite, cellular and Wi-Fi radios into a single, power-efficient module at a price point that works for high-volume deployments.

In the first quarter of 2026, two major product launches confirmed this shift. SKYWAVE released the ST 4000, which unifies satellite and cellular connectivity in a single device. Iridium came shortly after with the 9604, which packs satellite SBD, LTE-M and GNSS into a 16-by-26-millimeter module. Several other brands have launched their own versions since.

The defining feature across these hybrid IoT modules is the seamless failover, with the device handling transactions without human intervention or data loss. An asset activates on Wi-Fi inside a depot, switches to cellular on the road and then connects via satellite in remote zones.

According to a Fall 2025 report by IoT Analytics, the number of connected IoT devices reached 18.5 billion in 2024. They estimate that this could increase to 39 billion by 2030. As device counts increase, the demand for unbroken connectivity across every terrain and transit corridor will only intensify.

The Business Value of Uninterrupted Data

When hybrid IoT platforms deliver data consistently, supply chain teams can stop reacting to problems and start preventing them. For example, instead of finding out a pharmaceutical shipment was damaged only upon arrival, teams get a real-time alert during transit so they can find solutions or reroute before anything gets lost.

When companies can track assets across an entire journey, they spot containers and trailers sitting idle and put them back to work faster. That means less wasted equipment and lower costs. Continuous location and status updates also help fleet managers make smarter scheduling and routing decisions.

Temperature-sensitive goods like vaccines, biologics and fresh produce demand constant environmental monitoring. The challenge has always been maintaining that data stream across every leg of the journey, including ocean crossings and rural last-mile routes. High-value cargo moving through dead zones requires that same unbroken data flow. Hybrid IoT platforms ensure they keep moving regardless of geography.

Hybrid IoT Applications in Practice

In logistics and cold chain management, a single hybrid IoT module can track a shipping container from a factory in Shanghai on Wi-Fi, through the port on cellular, across the Pacific on satellite and into a rural distribution center in Montana via cellular again. At no point does the data stream break.

In agriculture, farms often operate far beyond reliable cell coverage. Hybrid connectivity enables the continuous monitoring of soil moisture sensors, livestock GPS trackers and equipment telematics across thousands of acres without the expensive private network infrastructure.

The hardware alone isn’t enough. Businesses also need software that pulls data from every connectivity network into one clear view. When evaluating hybrid IoT platforms, decision-makers should look at the full picture — how data is collected, analyzed and connected to the enterprise tools their teams already use.

The Advantage of Always-On Supply Chain Visibility

The real value of hybrid IoT modules goes well beyond location data. These devices deliver the operational certainty and data continuity that global businesses need to reduce waste, protect high-value shipments and make faster decisions. Always-on connectivity turns scattered supply chain data into a reliable foundation for smarter operations.

The post Can the New Wave of Hybrid IoT Modules Finally Eliminate Supply Chain Blind Spots? appeared first on IoT Business News.

As enterprises push more operations to connected, software-driven workflows, internet downtime is increasingly an operational risk—not just an IT inconvenience. T-Mobile’s new SuperBroadband bundle pairs its 5G Business Internet with Starlink connectivity under one managed service, aiming to make resilience and reach easier to procure and operate.

For multi-site businesses, connectivity has become an exercise in trade-offs: fiber where it exists, fixed wireless where it doesn’t, and a patchwork of backup links that too often behave like separate projects with separate vendors. That fragmentation is especially painful in sectors that can’t simply “wait out” an outage—think point-of-sale, patient workflows, safety systems, or remote field operations.

T-Mobile is now trying to productize a different answer. The operator has launched SuperBroadband, a business internet service that combines its 5G Business Internet with Starlink broadband, delivered as a fully managed offering with one contract and one bill. The promise is straightforward: two independent access paths—cellular and LEO satellite—designed to keep sites online through disruptions, while reducing the operational overhead of managing primary and backup providers.

The company says SuperBroadband is already being used by organizations in hospitality, retail, healthcare, and oil and gas. Aramark Destinations’ CIO, Dimple Jethani, framed the appeal around remote and complex environments where connectivity has been inconsistent and difficult to scale.

Redundancy is the product, not an add-on

Dual connectivity is not new; what’s distinct here is that T-Mobile is selling redundancy as a standardized, nationwide service rather than leaving customers to assemble it from separate ISPs, separate hardware, and separate support models. In the press materials, T-Mobile positions SuperBroadband as “built-in redundancy” via independent 5G and Starlink pathways, with intelligent orchestration between the two connections in real time.

Under the hood, T-Mobile describes an architecture that includes outdoor 5G equipment to improve signal strength, advanced routers to bring the connections together, and centralized control using Ericsson Enterprise Wireless Solutions’ NetCloud Manager for the latest Ericsson Cradlepoint routers and outdoor adapters. T-Mobile also says it plans to expand its ecosystem over time with partners such as Inseego for enterprise wireless broadband and edge connectivity options.

One detail IoT and enterprise networking teams will notice: the offer is not just about access technologies, but about operational tooling. T-Mobile is positioning its T-Platform as the pane of glass for deployments, including visibility into hardware, performance, usage, health, and events such as backup readiness and failovers.

Coverage claims—and what they mean in practice

T-Mobile says it has expanded its unlimited 5G Business Internet to millions of additional business locations and, with Starlink integrated, can reach “effectively every business location in America,” claiming SuperBroadband is the first nationwide broadband solution to reach every ZIP code in the U.S.

For IoT-heavy enterprises, the practical implication is less about marketing superlatives and more about procurement simplification. If a single provider can cover urban sites, suburban locations, and hard-to-serve remote facilities—while also providing a standardized backup path—network teams can reduce the number of exceptions in their connectivity design. That, in turn, can shorten deployment cycles for new sites and make rollouts of connected systems (POS refreshes, remote monitoring, edge applications) more repeatable across regions.

A managed-service play with defined service levels

SuperBroadband is being sold as a fully managed service with defined service levels and end-to-end support from T-Mobile for Business. The company also advertises a financially backed 99.99% uptime guarantee, with eligibility and exclusions outlined in its service terms, and notes that installation and field services are supported by Acuative to enable nationwide deployment.

Here’s the operational insight that matters: by tying an uptime commitment to a bundled, dual-path design, T-Mobile is implicitly shifting the “resilience engineering” burden away from the customer and toward the provider’s managed stack—hardware, orchestration, monitoring, and support. That may appeal to organizations that lack the staff to design and continuously test multi-carrier failover themselves, but it also means enterprises will scrutinize how failover events are detected, how switching is handled, and what visibility they retain in the portal during incidents.

Why this is different from typical connectivity bundling

Many connectivity announcements boil down to “we support technology X” or “we partner with provider Y”. T-Mobile’s SuperBroadband is more specific: it is a packaged, nationwide offer that fuses terrestrial 5G and LEO satellite into one managed experience, with centralized orchestration and monitoring baked in. In other words, it’s not just adding satellite as an optional uplink; it’s selling a standardized operational model for dual connectivity.

For OEMs and solution providers building systems that assume continuous connectivity—digital signage, connected kiosks, remote security, industrial telemetry, edge compute stacks—the availability of a single, supported connectivity SKU could simplify go-to-market and support. For system integrators, the combination of NetCloud-managed Cradlepoint hardware and T-Platform visibility may reduce the tooling sprawl that often comes with multi-ISP designs.

SuperBroadband is available now, according to T-Mobile, for use cases ranging from single-site businesses to complex, multi-location organizations, with Starlink connectivity integrated across options.

The post T-Mobile packages 5G and Starlink into a single managed broadband offer for business continuity appeared first on IoT Business News.

Altair Semiconductor has completed its spinoff from Sony Semiconductor Solutions, securing $50 million in funding to accelerate its focus on cellular IoT and a 5G eRedCap roadmap.

As IoT deployments scale across industries, a recurring challenge continues to shape the market: how to balance power efficiency, device longevity, and evolving connectivity standards. While LTE-M and NB-IoT have enabled massive IoT adoption, the transition toward 5G-native solutions remains complex, particularly for cost-sensitive and battery-powered devices.

It is within this context that Altair Semiconductor has completed its transition into an independent company, following a strategic spinoff from Sony Semiconductor Solutions. The move is backed by $50 million in initial funding led by Pitango Group, with Sony retaining a stake in the company .

The separation marks a structural shift for Altair, which has built its reputation on low-power cellular IoT chipsets widely used in applications such as smart metering, asset tracking, and wearables. Operating independently is expected to give the company greater flexibility to navigate a rapidly evolving connectivity landscape, particularly as the industry begins to align around new 5G IoT categories.

Positioning for the 5G IoT Transition

Altair’s roadmap centers on 5G eRedCap (enhanced Reduced Capability), a standard designed to bridge the gap between high-performance 5G and the constrained requirements of massive IoT devices. The company’s upcoming ALT1550 modem, currently in advanced silicon testing, reflects this strategic direction .

This positioning is notable because eRedCap is emerging as a key enabler for mid-tier IoT use cases that require more bandwidth and lower latency than LTE-M, but without the complexity and cost of full 5G. By aligning early with this segment, Altair is effectively targeting a future market layer that remains underdeveloped but strategically important.

At the same time, the company continues to support its established LTE Cat-M and NB-IoT platforms, which integrate connectivity, processing, and security features into highly compact chipsets. This dual approach—maintaining a strong 4G base while preparing for 5G—suggests a phased transition strategy rather than a disruptive shift.

From Connectivity Provider to Physical AI Enabler

A central theme in Altair’s positioning is the concept of “Physical AI,” referring to the growing need to connect machines, sensors, and devices that generate and act on real-world data. While the term itself is gaining traction across the industry, its practical implication is straightforward: more endpoints require persistent, low-power connectivity to support distributed intelligence.

Altair’s chipset portfolio is already embedded in large-scale deployments, particularly in cellular smart metering, where long device lifecycles and energy efficiency are critical. Extending this footprint into AI-enabled edge devices—such as wearables or asset trackers—requires maintaining similar constraints while accommodating new data and processing requirements.

This creates a non-trivial engineering challenge. Devices expected to operate for up to 20 years must remain compatible with evolving network technologies, which is precisely where eRedCap could play a role. The company’s emphasis on long-term device lifespan highlights a key industry tension: innovation cycles in connectivity are accelerating, while IoT hardware lifecycles remain inherently long.

Why Independence Matters

The decision to spin off from Sony is not just a financial or organizational move—it reflects a broader trend in the semiconductor and IoT ecosystem. Specialized connectivity players increasingly require agility to respond to shifting standards, operator requirements, and vertical-specific demands.

Within a large corporate structure, aligning these priorities can be slower and more constrained. As an independent entity, Altair can focus more directly on IoT-specific innovation cycles, particularly in areas such as low-power modem design and integrated connectivity platforms.

At the same time, Sony’s continued shareholding suggests that the relationship remains strategically relevant, potentially preserving access to ecosystem synergies without the limitations of full integration.

Implications for the IoT Ecosystem

For OEMs and device manufacturers, Altair’s roadmap provides a clearer migration path from existing LTE-M deployments toward 5G-based solutions without requiring a complete redesign of devices or architectures. This continuity is critical in sectors such as utilities or logistics, where infrastructure refresh cycles are measured in decades.

Connectivity providers and system integrators may also benefit from a more focused chipset vendor actively shaping the eRedCap segment, which is still in its early stages of commercialization.

More broadly, the announcement underscores a structural evolution in the IoT value chain: as connectivity becomes increasingly tied to edge intelligence, chipset vendors are positioning themselves not just as enablers of connectivity, but as foundational components of distributed AI systems.

Altair’s move to independence—and its emphasis on 5G eRedCap—signals a calculated bet on where that convergence is heading next.

The post Altair Semiconductor Spins Off from Sony to Focus on 5G IoT and eRedCap Strategy appeared first on IoT Business News.

IoT Platforms have become a central layer in the architecture of connected systems, sitting between devices, networks, and enterprise applications. As organizations move from pilot projects to large-scale deployments, the need for structured, scalable, and secure ways to manage connected assets has intensified. Platforms are no longer optional infrastructure—they are foundational to how IoT systems are designed, operated, and monetized.

Yet the term “IoT Platforms” remains broad and sometimes ambiguous. It can refer to device management tools, cloud-based analytics environments, or full-stack solutions combining connectivity, data processing, and application enablement. Understanding what these platforms actually do—and how to evaluate them—has become a critical task for decision-makers navigating an increasingly fragmented ecosystem.

Key Takeaways

• IoT Platforms provide the software and infrastructure layer that connects devices, manages data, and enables applications.
• They typically include device management, data ingestion, analytics, and integration capabilities.
• The vendor landscape is fragmented, ranging from hyperscalers to specialized industrial platform providers.
• Selection criteria must balance scalability, interoperability, security, and total cost of ownership.
• Architectural choices—cloud, edge, or hybrid—have a direct impact on performance and deployment flexibility.

What is an IoT Platform?

IoT Platforms are integrated software environments that enable organizations to connect, manage, monitor, and analyze data from connected devices at scale. They act as an intermediary layer between hardware (sensors, gateways), connectivity networks, and enterprise applications, providing the tools required to build and operate IoT solutions.

In practice, IoT Platforms aggregate multiple functions that would otherwise require separate systems. These include device provisioning, data collection, real-time processing, analytics, visualization, and integration with business systems such as ERP or CRM platforms. By consolidating these capabilities, platforms reduce complexity and accelerate time to deployment.

Within the broader IoT ecosystem, IoT Platforms serve as the control plane. They orchestrate communication between devices and applications, enforce security policies, and provide the data pipelines that turn raw sensor data into actionable insights.

How IoT Platforms work

At a high level, IoT Platforms operate through a layered architecture designed to handle device connectivity, data processing, and application enablement.

The typical architecture includes:

• Device layer: Sensors, actuators, and embedded systems generate data and receive commands.
• Connectivity layer: Networks such as cellular (LTE-M, NB-IoT, 5G), LPWAN (LoRaWAN), Wi-Fi, or satellite transport data to the platform.
• Ingestion layer: Message brokers and APIs collect and normalize incoming data streams.
• Processing layer: Stream processing engines and rule engines filter, transform, and enrich data in real time.
• Storage layer: Time-series databases and data lakes store structured and unstructured data.
• Application layer: Dashboards, analytics tools, and APIs enable users to interact with data and build applications.

Communication between devices and IoT Platforms typically relies on lightweight messaging protocols such as MQTT or CoAP, designed for constrained environments. Platforms also support REST APIs and event-driven architectures to integrate with enterprise systems.

Increasingly, IoT Platforms extend beyond centralized cloud environments to include edge computing capabilities. In this model, part of the data processing occurs closer to the device, reducing latency and bandwidth usage while improving resilience.

Key technologies and standards

The functionality of IoT Platforms depends on a combination of communication protocols, data processing technologies, and interoperability standards.

Common technologies include:

• Messaging protocols: MQTT, AMQP, CoAP for efficient device-to-cloud communication.
• Connectivity standards: LTE-M, NB-IoT, 5G, LoRaWAN, Wi-Fi, Bluetooth Low Energy.
• Data formats: JSON, CBOR, Protocol Buffers for structured data exchange.
• Cloud infrastructure: Containerization (Docker), orchestration (Kubernetes), serverless computing.
• Edge frameworks: Edge runtimes for local data processing and device orchestration.
• Security standards: TLS/DTLS encryption, X.509 certificates, hardware-based secure elements.

Interoperability remains a critical issue. While IoT Platforms often support multiple protocols, the lack of universal standards across industries can lead to integration challenges, particularly in legacy environments.

Main IoT use cases

IoT Platforms are deployed across a wide range of industries, each with distinct requirements in terms of scale, latency, and data processing.

• Industrial IoT: Monitoring machinery, predictive maintenance, and optimizing production processes through real-time analytics.
• Logistics and supply chain: Tracking assets, monitoring environmental conditions, and improving route optimization.
• Smart cities: Managing urban infrastructure such as traffic systems, lighting, waste management, and public safety.
• Energy and utilities: Smart metering, grid monitoring, and demand-response systems.
• Healthcare: Remote patient monitoring, connected medical devices, and asset tracking within hospitals.
• Asset tracking: Monitoring location, status, and utilization of high-value equipment across industries.

In each of these use cases, IoT Platforms provide the common foundation for data collection, analysis, and operational decision-making.

Benefits and limitations

IoT Platforms offer several advantages that make them central to modern connected systems:

• Scalability: Ability to manage thousands to millions of devices from a single environment.
• Operational efficiency: Centralized management reduces the complexity of distributed systems.
• Faster deployment: Pre-integrated tools accelerate development and reduce time to market.
• Data-driven insights: Advanced analytics enable predictive and prescriptive decision-making.

However, these benefits come with trade-offs and limitations:

• Vendor lock-in: Proprietary architectures can make it difficult to migrate between platforms.
• Integration complexity: Connecting legacy systems and heterogeneous devices can require significant customization.
• Latency constraints: Cloud-based processing may not meet real-time requirements without edge capabilities.
• Cost management: Scaling data storage and processing can lead to unpredictable costs.
• Security risks: Expanding attack surfaces require robust security frameworks across devices and networks.

Understanding these trade-offs is essential when selecting and deploying IoT Platforms in production environments.

Market landscape and ecosystem

The IoT Platforms market is highly fragmented, reflecting the diversity of use cases and technical requirements.

The ecosystem includes several categories of players:

• Hyperscalers: Cloud providers offering scalable infrastructure and integrated IoT services.
• Industrial platform vendors: Solutions tailored for manufacturing, energy, and heavy industries.
• Connectivity providers: Operators integrating platform capabilities with network services.
• Specialized IoT vendors: Companies focusing on specific verticals or functions such as device management or analytics.
• System integrators: Organizations that combine multiple technologies into end-to-end solutions.

No single platform dominates across all segments. Instead, enterprises often adopt a multi-platform strategy, combining different solutions to address specific operational needs.

Partnerships between platform vendors, hardware manufacturers, and connectivity providers play a critical role in shaping the ecosystem, enabling interoperability and accelerating deployment.

Future outlook

The evolution of IoT Platforms is closely tied to broader trends in computing and connectivity.

Several developments are expected to influence the next generation of platforms:

• Edge-native architectures: Increased processing at the edge to reduce latency and bandwidth usage.
• AI integration: Embedding machine learning models directly into platforms for real-time analytics.
• Standardization efforts: Industry initiatives aimed at improving interoperability across devices and platforms.
• 5G and satellite connectivity: Expanding coverage and enabling new use cases in remote environments.
• Security by design: Stronger emphasis on end-to-end security across the entire IoT stack.

As deployments scale and become more complex, IoT Platforms will continue to evolve from infrastructure tools into strategic assets supporting digital transformation initiatives.

Frequently Asked Questions

What are IoT Platforms used for?

IoT Platforms are used to connect devices, manage data, and enable applications that rely on real-time information from connected systems.

What are the key features of IoT Platforms?

Core features include device management, data ingestion, real-time processing, analytics, security, and integration with enterprise systems.

How do IoT Platforms differ from cloud platforms?

IoT Platforms are specialized for handling device communication and sensor data, while general cloud platforms provide broader computing and storage capabilities.

What should enterprises consider when selecting IoT Platforms?

Key criteria include scalability, interoperability, security, cost, support for standards, and alignment with existing infrastructure.

Can IoT Platforms operate at the edge?

Yes, many IoT Platforms now include edge computing capabilities to process data locally and reduce latency.

Related IoT topics

• Edge Computing in IoT
• LPWAN Connectivity Technologies
• Device Management in IoT
• Industrial IoT
• IoT Security
• Digital Twins in IoT

The post IoT Platforms: Key Capabilities, Vendor Landscape and Selection Criteria appeared first on IoT Business News.

28 April marks one year since the Iberian Peninsula blackout, Europe’s largest grid failure in the last 20 years, which left nearly 60 million without power. A year on, it remains a stark reminder of how catastrophic outages can be when resilience mechanisms aren’t fully in place.

The IoT energy market is projected to reach $62.8 billion globally by 2030. Whilst last year’s blackout was not linked to the IoT, as IoT adoption grows in an increasingly digitalised sector, early decisions around network infrastructure, security and scalability are crucial for ensuring uptime.

Iain Davidson, head of product marketing, Wireless Logic, offers insight into what the industry has learned a year on from the outage:

“The role of IoT in the energy ecosystem and smart grid is growing rapidly. However, it is only an asset to the sector if it is truly resilient and secure. Last year’s Iberian Peninsula blackout demonstrated how quickly disruption can occur when complex infrastructure is placed under stress. As seen, if systems, equipment or infrastructure suffer downtime, severe disruption can occur on a vast scale. One year later, the focus must be on embedding proactive resilience measures from the outset”.

“Energy companies and suppliers should prioritise proactively and continuously monitor infrastructure, devices and applications. They should also implement predictive maintenance and real-time monitoring and threat detection supported by AI-driven analytics to identify and mitigate problems before they occur. This includes issues which begin as operational, environmental and cyber-security breaches.  In addition, networks and systems should be designed with redundancy to handle demand fluctuations and include automated and immediate failover to maintain continuity during failures”. 

“Crucially, the industry must implement operational and cyber resilience procedures and test them regularly. Resilience and security must be built in end-to-end across IoT devices, networks, software, processes and cloud to minimise downtime.”

“Ensuring that effective strategies are in place will support the sector to recover rapidly and effectively from incidents moving forward.”

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As the EU Cyber Resilience Act pushes security and documentation obligations deeper into the IoT supply chain, Quectel says its module cybersecurity programme is already aligned with CRA requirements, supported by long-running third-party work with Finite State.

For years, IoT security conversations have focused on endpoints and applications. The EU’s Cyber Resilience Act (CRA) shifts that centre of gravity upstream, forcing manufacturers to demonstrate that security is designed in, continuously maintained, and backed by evidence. For device makers building connected products on top of embedded modules, that creates a practical question: how much of CRA readiness can be inherited from suppliers, and how much still has to be built in-house?

Quectel Wireless Solutions is positioning its module portfolio as part of that answer. The company said it has a cybersecurity programme in place that supports compliance with the CRA ahead of the 11 September 2026 deadline, pointing to requirements such as security by design, availability of Software Bills of Materials (SBOMs), and vulnerability disclosure and incident reporting.

The announcement is less about a single new product feature than about process and proof. Under the CRA, compliance is not just a security posture; it needs to be demonstrable to regulators and market surveillance bodies through technical documentation and verifiable evidence. In practice, that pushes module vendors to provide structured artefacts that OEMs can incorporate into their own compliance files.

What Quectel is putting on the table

Quectel said it has been working with Finite State, which it describes as a specialist in connected device and software supply chain security, to help ensure its product portfolio is secure and aligned with the CRA and “other industry standards globally.” According to Quectel, the collaboration is designed to support transparency and regulatory alignment for customers integrating its modules into products destined for the European market.

The company’s description of deliverables centres on documentation and testing. Quectel said its modules are delivered “pre-tested and audit-ready,” and supported by security documentation including SBOMs, VEX files, and detailed vulnerability reporting. It also framed the collaboration around three areas: independent security testing, software supply chain visibility, and continuous risk management with monitoring and remediation processes.

“Finite State has been Quectel’s third party cybersecurity firm for over four years, underlining our commitment to module security,”
Willis Yang, Senior Vice President, Quectel Wireless Solutions

The CRA’s lifecycle obligations are a notable pressure point for the module ecosystem. The regulation requires manufacturers to ensure security throughout a product’s lifecycle, including timely updates and effective vulnerability management. That can be challenging in long-lived industrial deployments where hardware stays in the field for many years, while software components and vulnerability expectations evolve continuously.

Why this matters to the IoT supply chain

For IoT OEMs and integrators, the practical value of a “CRA-aligned” module programme will depend on how cleanly supplier artefacts integrate into a broader compliance workflow. SBOMs and VEX files can reduce the burden of mapping what software is inside a shipped product and assessing exposure when new vulnerabilities surface. But they also introduce operational requirements: OEM teams need processes and tools to ingest supplier documentation, correlate it with their own firmware and applications, and produce traceable evidence during audits or incident response.

Connectivity hardware sits at a particularly sensitive junction of the modern device stack. A cellular, Wi-Fi, Bluetooth, GNSS or satellite-enabled module is not just a radio; it typically includes firmware and a supply chain of software components that can affect risk posture. By highlighting external validation and documentation, Quectel is responding to an emerging procurement reality: security evidence is becoming part of module selection alongside RF performance, certifications, power profiles and lead times.

For connectivity providers and platform players, the direction of travel also changes post-deployment operations. The CRA’s emphasis on vulnerability handling and reporting can force tighter integration between device management, update delivery and security monitoring. Module suppliers that can support OEMs with structured reporting and component transparency may reduce friction when customers need to act quickly on new disclosures.

Quectel’s message is clear: it expects CRA-driven compliance work to ripple across the embedded ecosystem, and it wants customers to view its modules as accompanied by the documentation and third-party validation needed for regulatory scrutiny. With the 2026 deadline approaching, more module makers are likely to talk in similar terms. The differentiator, for IoT buyers, will be how usable the evidence is in real product compliance files—and how well lifecycle commitments hold up once devices are deployed at scale.

The post Quectel leans on third-party security validation as EU Cyber Resilience Act deadline approaches appeared first on IoT Business News.

5G fixed wireless access gear is increasingly being asked to do more than deliver broadband, particularly in enterprise sites where location and contextual sensing can unlock new services. Telit Cinterion says it will integrate Airfide Networks’ UWB and 60 GHz radar capabilities into platforms built around its FN990B40 5G sub-6 data card.

As 5G rolls into factories, warehouses and campuses via fixed wireless access (FWA) and enterprise gateways, a familiar problem reappears: connectivity alone rarely solves the operational use cases. Indoor positioning, geofencing and presence sensing often require separate radios, extra sensors and additional integration effort—adding cost and complexity for OEMs, system integrators and operators that want to productise services on top of access infrastructure.

That is the gap Telit Cinterion and Airfide Networks are aiming at with a newly announced partnership. The companies plan to bring Airfide’s localisation and sensing technologies—ultra-wideband (UWB) fine ranging and 60 GHz millimetre-wave radar—into solutions powered by Telit Cinterion’s FN990B40 5G data card.

Telit Cinterion positions the FN990B40 as a next-generation 5G sub-6 data card for broadband connectivity in compact designs, targeting FWA and enterprise gateways, as well as repeater applications. In addition to 5G New Radio (NR) with LTE, the module also supports WCDMA and includes an integrated GNSS receiver, according to the announcement.

Turning FWA hardware into an enterprise services platform

The core idea is to make the 5G gateway or repeater a multipurpose platform: one piece of infrastructure that can connect, locate and sense. Airfide says it integrates UWB and 60 GHz radar into 5G-powered gateways and repeaters, as well as customer premises equipment (CPE), with the stated goal of transforming 5G infrastructure into “intelligent service platforms.”

On the localisation side, the collaboration centres on FiRa-compliant UWB. The companies point to indoor positioning needs in environments such as warehouses and enterprise campuses, and contrast this with Bluetooth Low Energy (LE) approaches that can be constrained by range and device density. In the partnership, UWB functionality is intended to be integrated into FN990B40-powered platforms, allowing OEMs to build geofencing and asset tracking directly into 5G infrastructure—an approach the companies say can reduce system complexity and speed deployment.

Airfide also claims it provides a “full-stack solution,” including reference hardware, software and cloud control. For device makers, that matters less as a marketing phrase than as a practical signal: localisation is rarely just a radio. It typically requires calibration workflows, device onboarding, policy management and operational tooling to make it usable at scale.

Radar sensing aimed at privacy-sensitive indoor use cases

The second pillar is 60 GHz radar, which Airfide says leverages 4 GHz of unlicensed spectrum and uses a four-receiver, three-transmitter architecture. The companies describe this as enabling “sub-centimeter sensing precision” when embedded into FN990B40-based platforms, and they list target applications including occupancy detection, people and object tracking, live health monitoring (including heart rate and pulse), and fall detection for older adult care.

One notable angle in the release is privacy positioning. Because the sensing is described as anonymous and camera-free, Telit Cinterion and Airfide are framing radar as an option for environments where cameras are impractical or unwelcome, such as healthcare facilities and public venues.

The announcement also hints at operator interest: in Japan, operators are said to be evaluating the architecture for 5G repeater deployments, with the implication that sensing and analytics services could be layered on top of coverage infrastructure.

For operators and infrastructure vendors, the broader industry context is a shift in how FWA and enterprise 5G equipment is monetised. As access performance becomes less of a differentiator, vendors are looking for attach services at the edge—capabilities that can be sold as part of a managed offering, rather than as stand-alone devices that enterprises must integrate themselves.

For OEMs, the practical question will be how tightly these capabilities are integrated into the FN990B40-powered designs and what that means for product engineering. If UWB and radar can be packaged into a single gateway or repeater design with a coherent software stack, it could simplify bill of materials decisions and reduce the number of separate subsystems that must be qualified, deployed and maintained over time.

“We’ve embedded our UWB and mmWave radar technologies into platforms powered by Telit Cinterion’s FN990B40. This enables OEMs and operators to deploy intelligent, high-precision IoT services directly within their 5G infrastructure.”
Venkat Kalkunte, Airfide Networks

“This partnership demonstrates how sub-6 5G data card platforms can serve as the foundation for localization, sensing and new monetization opportunities worldwide.”
Neset Yalcinkaya, president of IoT hardware at Telit Cinterion

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With the EU Cyber Resilience Act set to make long-term security updates mandatory, Nordic Semiconductor is repositioning firmware maintenance as a predictable, upfront cost by introducing a lifetime flat-rate FOTA and device management license within nRF Cloud.

For connected-device makers selling into Europe, the conversation around firmware updates has shifted from “nice to have” to “non-negotiable.” The EU Cyber Resilience Act (CRA) will require manufacturers to provide security updates for identified vulnerabilities throughout a device’s lifetime, and the compliance burden is not only technical. It is also financial and operational: maintaining update infrastructure, planning staged rollouts, and generating evidence of due diligence can create a long tail of cost that many product teams historically underestimated.

Nordic Semiconductor is now trying to make that long tail easier to budget. The company has introduced a one-time, upfront “lifetime” license for Firmware Over-the-Air (FOTA) and device management in nRF Cloud, positioning it as a way for customers to prepare for CRA requirements starting in 2027.

François Baldassari, founder of Memfault and VP Software Services at Nordic Semiconductor, framed the move in compliance terms: “Preparing for compliance with the EU Cyber Resilience Act is going to add significant operational overhead and project complexity for device manufacturers,” he said.

What Nordic is actually offering

At the core is a pricing and packaging change: instead of treating FOTA and device management as an ongoing cloud subscription or forcing customers to build and operate their own infrastructure, Nordic says nRF Cloud now offers a lifetime model based on a single upfront fee per device.

The company describes nRF Cloud as being pre-integrated on Nordic-based devices and positioned as a turnkey foundation for CRA and U.S. Cyber Trust Mark readiness, citing secure updates, auditability, and long-term support as the pillars of that approach. Nordic also says the offering is available across its low-power wireless portfolio.

From an implementation standpoint, Nordic points to integration with its nRF Connect SDK and calls nRF Cloud a “chip-to-cloud” FOTA solution. The press release lists capabilities that include MCUboot (built into the nRF Connect SDK), a global FOTA delivery network “optimized for low-power devices,” libraries for gateway-based updates, staged rollouts with analytics and rollback, a fleet management console, and governance functions such as approval workflows and immutable audit logs.

Availability, as stated by Nordic, covers nRF54, nRF53, and nRF52 Series Bluetooth Low Energy SoCs, as well as nRF91 Series cellular IoT modules. Nordic says pricing starts at $1 per device, depending on fleet size and project requirements.

Why lifetime licensing matters for IoT teams

FOTA has long been a technical requirement for security and feature maintenance, but regulation is turning it into a product obligation that must survive beyond the initial deployment phase. What changes under CRA-style expectations is not simply that updates must exist; it’s that update delivery, traceability, and organizational process need to persist over the device lifecycle.

That creates friction in procurement and product planning. Subscription-based device management can be straightforward at pilot stage, but becomes harder to forecast as fleets grow and device lifetimes stretch. By offering a one-time license, Nordic is effectively proposing a different budgeting model: shift a recurring operational expense into an upfront, per-device line item that can be baked into BOM-adjacent economics and long-term support planning.

For OEMs and system integrators, the practical impact will likely be felt in three places. First, it may reduce the pressure to build and maintain a bespoke update backend simply to satisfy compliance requirements. Second, it could simplify customer contracts by clarifying who pays for security upkeep over time. Third, it puts more emphasis on choosing silicon and SDK ecosystems that already include a workable secure-update path, rather than bolting one on late in a program.

Nordic’s announcement also reflects a broader pattern in IoT: silicon vendors increasingly sell “systems” that combine hardware, software tooling, and cloud services to reduce time-to-market and lifecycle risk. In Nordic’s case, it is leaning on the infrastructure it acquired with Memfault in 2025, stating that the nRF Cloud FOTA model is built on infrastructure originally developed by Memfault and has been field-tested “across millions of devices.”

Whether lifetime FOTA becomes a new norm will depend on how customers weigh flexibility against predictability. But with CRA enforcement getting closer, the market is clearly moving toward update mechanisms that are not just technically sound, but also operationally sustainable—and that is where Nordic is aiming this new nRF Cloud licensing model.

The post Nordic Semiconductor adds lifetime flat-rate FOTA licensing to nRF Cloud as CRA compliance looms appeared first on IoT Business News.

Narrowband IoT (NB-IoT) has emerged as one of the cellular technologies specifically designed to address the connectivity needs of large-scale Internet of Things deployments. As industries deploy millions of connected sensors, meters and devices, traditional cellular networks optimized for smartphones are often inefficient for small, low-power data transmissions. NB-IoT was introduced to address this gap by enabling wide-area connectivity for devices that transmit small amounts of data over long periods.

Today, NB-IoT plays a significant role in the evolution of low-power wide-area networking (LPWAN) within the cellular ecosystem. By leveraging existing mobile infrastructure while optimizing for energy efficiency and coverage, NB-IoT enables operators and enterprises to support massive numbers of connected devices across smart cities, utilities, logistics and industrial environments.

Key Takeaways

• NB-IoT is a cellular LPWAN technology designed for low-power, wide-area IoT connectivity.
• It supports massive device deployments by optimizing bandwidth, energy consumption and network capacity.
• NB-IoT operates within licensed spectrum and can be deployed using existing cellular infrastructure.
• Typical applications include smart metering, asset tracking, environmental monitoring and smart city services.
• While highly efficient for small data transmissions, NB-IoT is not suited for high-throughput or low-latency applications.

What is NB-IoT?

NB-IoT (Narrowband Internet of Things) is a low-power wide-area cellular communication technology designed to connect large numbers of devices that transmit small amounts of data over extended periods. Standardized by the 3rd Generation Partnership Project (3GPP), NB-IoT operates within licensed cellular spectrum and is optimized for coverage, energy efficiency and network scalability.

The technology enables IoT devices such as sensors, meters and trackers to communicate directly with cellular networks without requiring complex or power-intensive hardware. By using narrow bandwidth and simplified signaling procedures, NB-IoT reduces device complexity while extending battery life, making it suitable for deployments that must operate unattended for many years.

Within the broader IoT connectivity landscape, NB-IoT sits alongside other LPWAN technologies such as LTE-M and non-cellular solutions like LoRaWAN. Its primary strength lies in providing reliable wide-area connectivity using mobile operator infrastructure, which simplifies network management and supports large-scale deployments.

How NB-IoT works

NB-IoT was designed as an extension of existing cellular networks rather than an entirely new infrastructure. Mobile operators can deploy NB-IoT within their LTE spectrum using software upgrades to base stations, allowing them to support IoT devices without building separate networks.

The technology uses a narrow bandwidth of approximately 180 kHz, significantly smaller than traditional LTE channels. This narrowband approach reduces complexity for both the network and the device, enabling lower-cost chipsets and lower energy consumption.

NB-IoT devices communicate with the network using simplified signaling procedures tailored for intermittent data transmissions. Instead of maintaining continuous connections, devices typically remain in low-power states and wake up periodically to transmit or receive small data packets.

Several mechanisms support this energy efficiency:

• Power Saving Mode (PSM) allowing devices to remain dormant for extended periods.
• Extended Discontinuous Reception (eDRX) enabling devices to check for network messages less frequently.
• Optimized signaling to reduce overhead for small data transmissions.

These features allow devices to operate for many years on a single battery, which is critical for applications where maintenance or battery replacement is difficult or costly.

Key technologies and standards

NB-IoT is defined within the 3GPP family of cellular standards and was introduced as part of LTE evolution. Its architecture builds on established cellular technologies while introducing optimizations specifically designed for IoT deployments.

Important technologies and mechanisms involved in NB-IoT deployments include:

• 3GPP Release 13 and later – initial NB-IoT standardization and ongoing feature evolution.
• Licensed spectrum operation – ensuring predictable network performance and reduced interference.
• Single-tone and multi-tone transmissions – enabling flexible uplink communication with minimal device complexity.
• Coverage enhancement techniques – allowing devices to communicate even in challenging environments such as underground locations or dense buildings.
• Simplified device architecture – reducing chipset complexity and lowering module costs.

NB-IoT can be deployed using three different spectrum configurations:

• In-band deployment within existing LTE spectrum.
• Guard-band deployment using unused spectrum between LTE carriers.
• Standalone deployment using dedicated spectrum, often refarmed from older GSM networks.

This flexibility allows operators to introduce NB-IoT with minimal disruption to existing network operations.

Main IoT use cases

NB-IoT is particularly suited to IoT applications where devices transmit small amounts of data infrequently but require reliable connectivity across wide geographic areas. These characteristics make it suitable for infrastructure monitoring and long-term sensor deployments.

Some of the most common NB-IoT use cases include:

• Smart metering – electricity, gas and water utilities use NB-IoT to connect millions of meters for automated data collection.
• Smart cities – sensors monitoring street lighting, parking spaces, waste management and environmental conditions.
• Industrial monitoring – remote monitoring of equipment, pipelines or infrastructure in industrial environments.
• Asset tracking – tracking containers, equipment or other mobile assets across wide areas.
• Environmental sensing – air quality monitoring, flood detection or agricultural sensing systems.

In these scenarios, NB-IoT provides sufficient data throughput while minimizing device power consumption and operational costs.

Benefits and limitations

NB-IoT offers several advantages for IoT deployments, particularly where large numbers of low-power devices must operate reliably over long periods.

Key benefits include:

• Extended battery life, often exceeding ten years depending on device behavior.
• Strong indoor and underground coverage due to signal repetition and narrowband operation.
• Ability to support massive numbers of connected devices within a cellular network.
• Use of licensed spectrum, which improves reliability compared to some unlicensed LPWAN technologies.
• Relatively low-cost device modules due to simplified hardware requirements.

However, NB-IoT also presents certain technical constraints that must be considered when selecting connectivity technologies.

Key limitations include:

• Limited data throughput compared to traditional cellular technologies.
• Higher latency than technologies designed for real-time communication.
• Restricted mobility support, making it less suitable for rapidly moving devices.
• Dependence on mobile operator infrastructure availability.

For applications requiring frequent data transmission, real-time responsiveness or high bandwidth, other connectivity technologies such as LTE-M or 5G may be more appropriate.

Market landscape and ecosystem

The NB-IoT ecosystem spans multiple layers of the IoT value chain, from semiconductor providers and device manufacturers to mobile network operators and cloud platform vendors.

Mobile operators play a central role in NB-IoT deployments because the technology operates within licensed cellular spectrum. Many operators have introduced NB-IoT services as part of their broader IoT connectivity portfolios.

The device ecosystem includes chipset manufacturers, module vendors and hardware developers building sensors, meters and industrial equipment that integrate NB-IoT connectivity. These devices are often designed for long lifecycle deployments and must meet strict requirements for reliability and power efficiency.

In parallel, IoT platform providers and application developers integrate NB-IoT connectivity into data management systems, enabling organizations to collect, analyze and act on information generated by connected devices.

The resulting ecosystem reflects the broader IoT architecture in which connectivity, devices and cloud platforms interact to deliver end-to-end solutions.

Future outlook

NB-IoT is expected to remain a key component of the cellular IoT landscape as industries continue deploying large-scale sensor networks. Utilities, municipalities and infrastructure operators in particular are likely to expand deployments where long device lifetimes and wide coverage are critical.

Ongoing evolution within the 3GPP standards framework may continue improving device efficiency, network performance and integration with future cellular technologies. At the same time, NB-IoT will coexist with other connectivity options such as LTE-M and emerging 5G IoT capabilities.

Rather than replacing other technologies, NB-IoT contributes to a diversified connectivity ecosystem in which different network technologies address different classes of IoT applications.

Frequently Asked Questions

What does NB-IoT stand for?

NB-IoT stands for Narrowband Internet of Things, a cellular LPWAN technology designed for low-power devices transmitting small amounts of data over wide areas.

Is NB-IoT part of 5G?

NB-IoT was originally standardized within LTE networks but is considered part of the broader cellular IoT evolution and can coexist with 5G infrastructure.

How long can an NB-IoT device battery last?

Depending on usage patterns, an NB-IoT device can operate for up to ten years or more on a single battery due to optimized power-saving mechanisms.

What is the difference between NB-IoT and LTE-M?

NB-IoT focuses on low data rates and long battery life for stationary devices, while LTE-M supports higher throughput and mobility for more dynamic IoT applications.

Does NB-IoT require a SIM card?

Most NB-IoT devices use SIM or eSIM technology to authenticate with cellular networks and manage connectivity through mobile operators.

Related IoT topics

• LTE-M (Long Term Evolution for Machines)
• LPWAN connectivity technologies
• 5G IoT architecture
• IoT device power management
• Smart metering infrastructure
• IoT connectivity management platforms

The post NB-IoT: How Narrowband IoT Supports Massive Connected Devices appeared first on IoT Business News.

The rapid expansion of connected devices is pushing enterprises to rethink how machines communicate over wide geographic areas while maintaining energy efficiency and reliability. Cellular IoT technologies have emerged as a key enabler of this shift, offering standardized connectivity built on existing mobile network infrastructure. Among these technologies, LTE-M has gained significant traction for applications that require secure, low-power connectivity across large coverage areas.

Positioned between traditional LTE broadband and ultra-low-power LPWAN solutions, LTE-M addresses a specific class of IoT deployments that need mobility support, extended coverage, and moderate data throughput. From smart meters and asset trackers to industrial monitoring devices, LTE-M plays an increasingly important role in enabling scalable IoT connectivity across multiple industries.

Key Takeaways

• LTE-M is a cellular IoT technology standardized by 3GPP that enables low-power wide-area connectivity using existing LTE networks.
• It offers a balance between energy efficiency, coverage, mobility support, and data throughput for many IoT deployments.
• Typical use cases include asset tracking, smart metering, healthcare devices, and smart city infrastructure.
• LTE-M supports features such as power saving modes, extended coverage, and device mobility across cellular networks.
• The technology is widely supported by mobile operators and is part of the broader evolution toward 5G IoT connectivity.

What is LTE-M for IoT: Benefits, Coverage and Deployment Scenarios?

LTE-M (Long Term Evolution for Machines), also known as LTE Cat-M1, is a cellular low-power wide-area technology designed specifically for Internet of Things devices that require wide coverage, moderate data rates, and extended battery life.

Standardized by the 3rd Generation Partnership Project (3GPP) as part of LTE Release 13, LTE-M operates within existing LTE networks but uses reduced bandwidth and optimized signaling to support low-power IoT devices. The technology allows connected objects such as sensors, meters, and trackers to communicate with cloud platforms through mobile network infrastructure.

In the broader IoT connectivity landscape, LTE-M sits alongside technologies such as NB-IoT, traditional LTE, and emerging 5G IoT capabilities. Its design focuses on balancing power efficiency with features that are essential for many real-world deployments, including device mobility and voice support.

How LTE-M for IoT: Benefits, Coverage and Deployment Scenarios works

LTE-M is built on the existing LTE cellular architecture but introduces optimizations tailored for IoT devices. Instead of requiring the full capabilities of broadband LTE connections, LTE-M devices operate within a narrower bandwidth while maintaining compatibility with LTE network infrastructure.

The typical communication architecture involves several components:

• IoT device or sensor equipped with an LTE-M modem and SIM or eSIM.
• Cellular base station (LTE eNodeB) that provides radio connectivity.
• Mobile core network responsible for authentication, mobility management and data routing.
• Cloud platforms or IoT applications that process device data and enable remote device management.

Devices using LTE-M communicate through licensed cellular spectrum, allowing mobile network operators to manage quality of service and interference. This distinguishes cellular IoT technologies from unlicensed LPWAN alternatives that operate in shared radio bands.

Several features improve efficiency for battery-powered IoT devices. Power Saving Mode (PSM) allows devices to enter deep sleep states between transmissions, while extended Discontinuous Reception (eDRX) enables longer intervals between network listening cycles. These mechanisms help extend battery life to multiple years depending on usage patterns.

Another notable characteristic of LTE-M is its support for device mobility. Connected objects can move across cellular cells while maintaining connectivity, making the technology suitable for mobile applications such as fleet tracking or connected logistics.

Key technologies and standards

The development and deployment of LTE-M relies on several technical standards and network capabilities defined by the 3GPP ecosystem.

• 3GPP Release 13 and later – Introduced LTE-M as a cellular IoT category designed for machine-type communications.
• LTE Cat-M1 device category – Defines reduced bandwidth operation and simplified device capabilities.
• Power Saving Mode (PSM) – Allows devices to enter ultra-low-power sleep states.
• Extended Discontinuous Reception (eDRX) – Reduces energy consumption by extending paging cycles.
• Half-duplex communication – Simplifies device radio design while lowering cost and power requirements.
• Voice support via VoLTE – Enables applications such as emergency services or wearable devices.

Because LTE-M operates within LTE infrastructure, it benefits from existing cellular security mechanisms including SIM-based authentication, encrypted communication, and network-level device management.

Main IoT use cases

The combination of wide coverage, moderate throughput, and long battery life makes LTE-M suitable for a range of IoT applications that fall between ultra-low-power sensors and high-bandwidth connected devices.

Several industries are adopting LTE-M for large-scale IoT deployments.

• Asset tracking and logistics – Mobile devices attached to containers, vehicles or pallets transmit location and sensor data across national or international transport networks.
• Smart metering – Utilities deploy LTE-M modules in electricity, gas, or water meters to enable remote monitoring and infrastructure management.
• Industrial IoT – Factories and infrastructure operators use LTE-M sensors to monitor equipment performance and environmental conditions.
• Healthcare and wearables – Connected medical devices benefit from reliable connectivity and mobility support.
• Smart city infrastructure – Applications include parking sensors, environmental monitoring, and connected street lighting.

In many of these deployments, devices transmit small packets of data periodically rather than continuously streaming large volumes of information. LTE-M’s bandwidth and energy profile align well with this type of communication pattern.

Benefits and limitations

LTE-M offers several advantages that make it attractive for IoT deployments requiring cellular-grade connectivity.

• Extended coverage – Signal enhancements enable deeper indoor penetration and wider rural coverage compared with traditional LTE devices.
• Mobility support – Devices can move across cellular cells without losing connectivity.
• Energy efficiency – Power-saving features allow multi-year battery life in many applications.
• Global cellular infrastructure – Deployments can leverage existing LTE networks operated by mobile carriers.
• Secure connectivity – SIM-based authentication and cellular security frameworks protect device communications.

Despite these advantages, LTE-M is not suitable for every IoT scenario.

• Higher module cost compared with some unlicensed LPWAN technologies.
• Dependence on mobile network operators for connectivity.
• Limited bandwidth compared with full LTE or 5G broadband services.
• Not optimized for extremely low data rates where alternative LPWAN technologies may be more efficient.

As a result, technology selection often depends on the specific requirements of each IoT project, including coverage, energy constraints, mobility needs, and expected data volumes.

Market landscape and ecosystem

The ecosystem surrounding LTE-M includes a diverse set of stakeholders involved in device manufacturing, connectivity services, and IoT platform integration.

Mobile network operators play a central role by deploying LTE-M support within their LTE infrastructure. Many operators have introduced nationwide LTE-M coverage to address the growing demand for IoT connectivity.

Device manufacturers and module vendors integrate LTE-M modems into sensors, trackers, meters, and other connected equipment. Semiconductor companies develop the chipsets that power these modules, enabling low-power radio communication and cellular protocol handling.

At the software layer, IoT platforms provide device management, data processing, and analytics capabilities that allow enterprises to manage large fleets of connected devices. These platforms often support multiple connectivity technologies, allowing organizations to integrate LTE-M alongside other IoT communication standards.

System integrators and solution providers complete the ecosystem by designing and deploying end-to-end IoT systems tailored to specific industries.

Future outlook

The long-term role of LTE-M is closely linked to the evolution of cellular networks and the broader development of 5G IoT technologies. While 5G introduces new connectivity categories such as massive machine-type communications, LTE-M remains an important component of the cellular IoT roadmap.

Many operators plan to support LTE-M for years as part of their transition from LTE to 5G networks. The technology continues to evolve through additional 3GPP releases that improve energy efficiency, coverage performance, and integration with emerging IoT architectures.

For enterprises deploying connected devices today, LTE-M offers a mature and widely supported connectivity option with a clear migration path within the cellular ecosystem. Its combination of reliability, security, and network coverage positions it as a practical solution for many large-scale IoT deployments.

Frequently Asked Questions

What is LTE-M used for?

LTE-M is used to connect IoT devices that require wide-area cellular coverage, moderate data rates, and long battery life, such as asset trackers, smart meters, and environmental sensors.

How does LTE-M differ from NB-IoT?

LTE-M generally supports higher data rates and device mobility, while NB-IoT is optimized for very low-bandwidth stationary sensors.

Does LTE-M require new cellular infrastructure?

No. LTE-M can be deployed through software upgrades on existing LTE network infrastructure operated by mobile carriers.

How long can LTE-M device batteries last?

Battery life depends on transmission frequency and device design but can often reach several years when power-saving features are used.

Is LTE-M compatible with 5G networks?

LTE-M is expected to coexist with 5G networks and remain part of the cellular IoT connectivity landscape for many years.

Related IoT topics

• NB-IoT connectivity
• LPWAN technologies
• 5G massive IoT
• Cellular IoT modules
• eSIM and remote SIM provisioning
• Edge computing for IoT

The post LTE-M for IoT: Benefits, Coverage and Deployment Scenarios appeared first on IoT Business News.

Today, many challenges remain, as evidenced by a recent conversation I had with Kelly Ireland, CEO, CBT. Comparing and contrasting the past to the present provides good fodder for conversation. But, to start, let’s journey back together.

During World War II, women filled roles in shipyards, factories, and heavy industry, as men went off to war. Women did this with skill and determination, ultimately keeping production moving. But even after the war, Rosie’s legacy still persisted. A cultural shift was underway, as women’s rights movements began in the decades that followed, ultimately laying the groundwork for broader conversations about equality in the workplace.

Rosie the Riveter wasn’t a single person. Rather, she was an image immortalized in J. Howard Miller’s “We Can Do It!” poster. Rosie was ultimately a symbol of capability, resilience, and the potential power of a segment of the workforce that was often overlooked.

But to simplify Rosie to only a symbol is a slight to the thousands of women who stepped up during World War II. Meet Frances Mauro Masters, an original Rosie the Riveter from Michigan during World War II. She worked on B-24 Liberator bombers.

At 24 years old, she went to work at the bomber plant in Ypsilanti, Mich., in 1942 along with two of her sisters, Josephine and Angeline. In November 2025, she served as the inspiration for a statue at the Michigan World War II Legacy Memorial.

Frances, like more than 310,000 women across America, was determined to aid her country and support those who were serving in the military. Now, nearly eight decades later, we are seeing obituaries for many of these Rosie the Riveters who served as a cultural revolution, guiding the way for the next era of workers in manufacturing.

Modern Day Challenges

Today’s manufacturing industry is plagued with complex challenges. There is a skills gap across the generations, a volatile supply chain, a need for greater safety and sustainability, and the need to integrate advanced technologies like AI (artificial intelligence), wearables, and more. The tools are different, but the underlying challenge from eight decades ago still remains.

The solution to many of these challenges requires diverse approaches to thinking, problem solving, and collaboration. What is needed is another cultural shift, one that is cross-generational and collaborative at heart.

So, let’s go back to my discussion with Ireland, on The Peggy Smedley Show, she explains the workforce dynamics many manufacturers face today, saying, “You have a mixed workforce. You have youngsters who love tech and want to bring it in … you have an older workforce who are not as much into adopting new tech close to retirement.”

The question then becomes: how do you get management to say, no, you have to? Ireland believes the solution is to bring workers into be a piece of it, with the adoption, so they can see it. Ultimately, true transformation becomes about participation among all, not mandates from the top.

Modern Day Solutions

Often, the solution to many of today’s manufacturing challenges lies at the intersection of people and technology. However, at this intersection also lies complexity. Much of the discourse in this area includes conversation on job displacement, but there is some nuance in this discussion.

“From what we are seeing and the research we are doing, what AI is going to decimate on the white-collar jobs, it is going to do the exact opposite for manual labor, blue-collar jobs, industrial jobs,” explains Ireland. “It can hockey stick that up.”

But adoption will only stick if the numbers make sense. Ireland urges teams must talk about the importance of ROIs (returns on investments). She gives examples of wearable devices that have ROIs in two days and then they have a shelf life of 2-3 years, and businesses can keep adding capabilities to them. Ireland says those are the ones that are going to be successful.

“From the CFO down, they want to see the value this brings,” Ireland says. “If someone can’t lay this out, with this is your ROI, this is very quantifiable, etc., there are not very many people in our industry that can do that right now.”

Ultimately, what manufacturers are doing on the plant floor will end up resonating in at least 25 different industries. If done right, the impact will have far-reaching implications.

What’s Next

Complex systems require diverse, strategic thinking. In the mid-20th century, thousands of women entered an industry. Today, women still remain underrepresented in many technical and manufacturing leadership roles.

Rosie the Riveter reshaped collective beliefs, and today manufacturing faces an equally critical moment. The industry must shift, and that shift will require diverse thinkers. Perhaps we could use a new infusion of Rosie’s energy.

Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #futureofwork #digitaltransformation #RosietheRiveter #WomensHistoryMonth #WomenWhoLead #EmpowerWomen #InternationalWomensDay

The post From Rosie the Riveter to the New Plant Floor first appeared on Connected World.

9 in 10 construction workers in 24 states are not union members.

Let’s break this down.

The data comes from ABC (Associated Builders and Contractors).

And it found at least 90% of construction workers in 24 states did not belong to a union in 2025. Overall, there was a record of 9 million nonunion construction workers compared to 995,000 union members.

What does this mean? ABC urges state policymakers to advance policies that level the playing field, preserve worker choice, and address the issues the construction industry faces—issues like the worker shortage which will amount to 349,000 in 2026.

Of course, the worker shortage is only one challenge the construction industry faces today. The industry also is dealing with economic uncertainty, immigration policy, inflation, and interest rates. Time will only tell how the numbers shake out in the year ahead.

The post Fact of the Week – 3/9/2026 first appeared on Connected World.

Recent data from an Intuit whitepaper reveals that 92% of construction businesses want a single, integrated platform to manage both projects and financials. As costs rise and labor constraints persist, the need to bridge the gap between the office and the field has never been more critical.

Construction professionals are increasingly looking to technology solutions to drive productivity and offset rising costs. With AI spending in the industry expected to surge 44% year-over-year, the opportunity to scale is here.

Enter Intuit Enterprise Suite. With a construction edition designed specifically for the complexities of the industry, this AI-powered ERP is built to help businesses scale. Unlike other ERP systems for industry-specific use, the construction edition for Intuit Enterprise Suite is intentionally created to reflect how construction businesses actually work. The solution brings project, financial, and operational workflows together in one place, helping customers streamline operations, improve cash flow, and deliver real-time visibility into performance to drive profitable growth at scale. Intuit Enterprise Suite can also assist your project teams in job-cost forecasting by leveraging AI-driven insights and digital proposals to bid faster, improve estimate accuracy, and win more work–all while having visibility into costs, approvals, and change orders.

A few standout features of the Intuit Enterprise Suite construction edition include:

• Project Management Agent: Stay on top of cash flow, profitability, and effective project management in one place, planning and tracking budgets and progress against project phases. Early users of the Project Management Agent have seen a 60% reduction in the manual steps it takes to set up a project.
• Project budgets enhancements: Control costs, keep projects on track, and protect margins with a simplified budget setup, real-time AI-powered insights, and more comprehensive project budget reporting.
• Proposals: Win more bids, create proposals from estimates or vice versa, and build a customized proposal document with integrated e-signatures using a proposal document builder.
• Cost groups: Plan and track project costs for better job costing and project profitability tracking by designating industry-standard cost groups, including labor, materials, equipment, and subcontractors, tracking cost groups across budgets, expenses, POs, and bills.
• AIA-style invoicing: Track the total contract value on estimate, invoiced to date, invoice amount, and remaining balance at the phase level.

Intuit was recently named a Constructech 2026 Top Products award in the category of financial, ERP & business operations systems. Each year, Constructech highlights the most innovative and impactful technologies shaping the industry through its “Top Products” awards. The goal of this research is to help industry professionals identify the technologies best positioned to support their businesses in the year ahead and beyond.

With more than 90% of construction businesses agreeing that digital tools are just as important as physical ones, what steps will you take to upgrade your tools to improve your operational agility and ultimately grow your business? 

Want to tweet about this article? Use hashtags #construction #IoT #AI #cloud #futureofwork #ConstructechTopProducts

The post AI Drives Intelligent Construction Financials and ERP first appeared on Connected World.

The bottomline is worker‑centric innovations are reshaping how construction gets work done—and much of this progress is built on more than a decade of IoT (Internet of Things)‑enabled equipment, sensors, and connected workflows that quietly laid the foundation for today’s data‑driven capabilities. We are finally seeing the Internet of Things delivering on its promise of solutions to help AI build stronger and better tools for tomorrow.

All about the Technology

Many manufacturers came to ConExpo-Con/Agg with big jobsite announcements. Let’s consider the example of Caterpillar, which debuted Cat Compact, a customer experience for small contractors and growing businesses to bring everything into one destination to buy, rent, and service compact equipment. This blends digital discovery and online research, reducing complexity and helping contractors focus on the job.

Also, the company announced new high-horsepower C3.6 and C13D engines and aftermarket offerings, such as condition monitoring, connectivity tools, and parts options, to help customers protect uptime and get more from equipment. These capabilities build on Caterpillar’s long‑standing IoT and telematics ecosystem, which continues to evolve into more intelligent, connected, and automated jobsite operations.

With a focus on AI, the Cat AI Assistant helps customers interact more easily with Cat equipment and digital tools, enabling faster, smarter decisions from the office to the jobsite. Certainly, this is just the tip of the iceberg. The company also demonstrated Caterpillar’s first autonomous soil compactor, as another example of how connected machines, IoT data, and AI are converging.

The technology companies came in strong as well. Topcon Positioning Systems announced new 3D machine control technologies, expanded functionalities, and enhanced safety features for earthmoving and paving applications, as well as geomatic technologies for surveying and building construction applications. Perhaps what’s important to note here is that none of this begins with AI. The precision and productivity we’re seeing today are rooted in IoT‑enabled positioning, sensing, and realtime data exchange that have been maturing on jobsites for more than a decade.

All this technology is great, but what about the people? Educating workers on what’s new—and what is applicable to their jobsite is a massive undertaking. And then, of course, the training adds another layer to all of this. Fortunately, we are seeing new advances there too.

All about the Training

John Deere made several major equipment and technology announcements, including a new immersive learning environment with John Deere Extended Reality Training System. The company says this immersive, headset-based solution is designed to change how operators, dealers, and customers learn about their machines.

The technology leverages a combination of VR (virtual reality) and AR (augmented reality) experiences to create an engaging and interactive learning environment. The first release, available to both John Deere customers and dealers, will focus on two machines: the 650 P-Tier Dozer and the 210 P-Tier Excavator.

It will feature operator-focused virtual reality lessons, including daily maintenance walkarounds, controls familiarization, and direct interaction modules such as trenching and spreading. Augmented reality experiences will support electrical component location and machine walkarounds. Increasingly, these training modules draw from IoT‑enabled machine data, giving operators a more accurate, real‑world understanding of how equipment behaves in the field.

Certainly, this type of learning experience is not new. I remember attending this same event eight years ago and trying a similar interactive learning experience with a different company.

And yet we continue to see more immersive learning experiences emerge. Interplay Learning, which now includes Industrial Training Intl., showcased new training solutions to improve workforce development in high-risk environments. The centerpiece here is the company’s enhanced VR Crane Simulator, which now supports training across 10 crane types and with more than 1,200 scenarios.

This allows companies to align training more closely with the equipment operators use in the field. Some benefits here include the ability to train operators with immersive simulations, practice complex and high-risk lifts without putting people at risk and align training to exact equipment used in the field.

From high-power equipment and data-driven systems to strategic discussions around policy, safety, and workforce growth, at the center of all of this is the people and the processes that make progress possible. As the construction industry continues to navigate labor challenges, safety priorities, and rapidly evolving technologies, the focus should always remain on how the future of work will continue to take shape in the construction industry.

Behind every autonomous machine, predictive maintenance tool, or immersive training module is an IoT foundation that has been steadily connecting equipment, people, and processes for more than a decade. This editor has not only witnessed that journey but has seen how the IoT legacy we built over the past decade is now powering the next wave of AI‑driven, worker‑centric innovation—reshaping the construction jobsite in realtime.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #CONEXPOCONAGG2026 #CONEXPO2026

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The State of the Market

Skanska recently released its Winter 2026 Construction Trends Report, which offers a look at how the industry is entering the new year after a challenging 2025 defined by cost pressures, uneven demand, and continued uncertainty. We see after growth in 2023 and 2024, persistent labor shortages, tariff uncertainty, and elevated construction costs moderated activity in 2025.

There are a few areas that are seeing greater, faster growth than others. Skanska says the standout growth drivers are data centers and large tech-related megaprojects, powered by ongoing demand for AI (artificial intelligence), cloud, and data infrastructure. The large amounts of capital these projects attract are sustaining the engineering and construction pipeline. Institutional construction—particularly healthcare, education, and public facilities—are also projected to outpace broader nonresidential activity. Softer markets include residential and cyclical commercial segments, such as retail, office, and more.

Looking to the future, Skanska suggests consensus forecasts point to slight gains in total construction spending in 2026—flat to low single-digit growth—as private investment remains cautious, and economic and policy uncertainty persist.

Of course, Skanska’s report is only one example. Many construction companies are releasing market condition reports. Another example comes from DPR Construction, which recently release its Q1 2026 Market Conditions Report. We see there is moderate optimism for the healthcare and manufacturing markets, which are expected to see steady activity and potential expansion. In contrast, sectors such as higher education and commercial office are projected to decline in 2026, reflecting shifting priorities and changing market conditions.

Delivery Shifts for Construction

This report from DPR Construction agrees with the first from Skanska that in 2025 data center projects became a major focus for the construction industry—and suggests this trend is set to continue in 2026. Perhaps this is a bit of an obvious statement, but it bears repeating and greater analysis since the projections are huge. Deloitte estimates by 2035, power demand from AI-driven data centers could increase more than thirtyfold.

The bigger story here is that this will ultimately end up changing how owners go to market. DPR Construction suggests owners are shifting from traditional cost-focused strategies to prioritizing speed to market. We are also seeing a change in how projects are planned, procured, and executed. The DPR report suggests there are some key drivers that could ultimately impact other key markets including:

1. Early development of strategic partnerships that could reshape procurement processes and planning approaches. Think more agile and innovative project delivery models.
2. Proactive supply chain management and prefabrication decisions will be essential to identify risks and opportunities.
3. Prioritizing BIM (building information modeling) and digital twins as foundational elements for project delivery is no longer a nice-to-have; it is a must-have.
4. Embracing flexibility and innovative delivery models will be key. We could finally see a faster rise of more collaborative models where risks are shared among the stakeholders.

While the DPR report didn’t come right out and say IPD (integrated project delivery), the underlying collaborative effort is apparent in the research. Here at Constructech, we have long been talking about the rise of IPD in the construction industry, and now with the need for quick delivery of data center projects at the forefront, what we learned more than 10 years ago could become applicable more today than ever before.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #datacenter #IPD

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From the state of Washington to New Jersey, new collaborations are demonstrating how education is becoming a central strategy for responsible AI adoption.

What’s Happening in Washington State

To get started, let’s travel to the state of Washington. Here we see the University of Washington and Microsoft have announced an expansion to their decades-long partnership, with a focus on accelerating AI discovery to prepare students and workers to use AI responsibly.

This partnership will expand internships and applied research opportunities for students. It will also develop community AI literacy programs, including a foundational AI course for working Washingtonians. We also see it will launch a new initiative to connect staff and students with real-world research opportunities at Microsoft.

Additionally, beginning this fall, the University of Washington and Microsoft will launch a new collaboration on Microsoft’s Redmond campus that will co-develop select courses and learning experiences for Microsoft employees, while enabling university students to learn alongside industry professionals.

What’s Happening in Indiana

Moving east to Indiana, we see Purdue University and Google Public Sector are deepening their long-standing collaboration, with a partnership aimed to advance AI-enabled education, accelerate AI innovation, and expand AI workforce development.

This multiyear strategic commitment provides students, faculty and researchers with Google Cloud’s full AI-optimized tech stack and high-performance computing power. With a commitment to Google Partnership for Accelerated Research program, Purdue students, faculty, researchers, and staff can access Google Cloud’s AI enterprise tools and software.

The university will also receive access to Google DeepMind’s co-scientist, which is a multi-agent AI system built with Gemini to help scientists generate novel hypotheses and research proposals. Also, the creation of the Google AI Hub space within Purdue’s Hall of Data Science and AI will serve as a campus space where students and researchers connect to spark hands-on collaboration and breakthrough innovation.

This extends Purdue’s broad strategy of AI, which includes five functional areas including:

1. Learning with AI
2. Learning about AI
3. Researching AI
4. Using AI
5. Partnering in AI

This is yet another example of collaboration that will lead to greater education in artificial intelligence at the university level.

What’s Happening in New Jersey

Let’s make one more jump east, this time to New Jersey. In February, NJIT (New Jersey Institute of Technology) announced applications are open for an ambitious expansion of their workforce development partnership with Verizon.

Expected to launch in early April, this will provide no-cost, high-impact training in artificial intelligence, cybersecurity, and IT to eligible New Jersey residents. The objective here is to bridge the digital skills gap.

Central to this new phase is the launch of a Cybersecurity Community of Practice, a collaborative ecosystem where participants, industry experts, and NJIT graduate students engage in peer-to-peer learning and mentorship.

The program offers a comprehensive curriculum, including certification prep, microcredential, and cybersecurity community of practice. Most training is offered online and laptops/internet are available for qualifying participants to assist with access.

These are just three examples of partnerships that aim to equip students, workers, and communities with the credentials and experience needed to work in a high-tech, AI-driven world. I have been sounding the alarm for years now. We need to invest in our workforce. People are—and always will be—the key to good AI strategies.

Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #futureofwork #digitaltransformation #worker

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Robots used for security at major venues are not new. Back in 2018, I met B-3PO (“she” was intentionally designated as a female) at New York’s LaGuardia Airport Terminal B.

As I was entering the terminal, she rolled up to me and stopped. I had a “conversation” with her (and found out later that the robot had a realtime connection to a real NYC police officer (a lady) that could make the conversation seem “real”).

It was an experiment and did not last long, but in 2026, with the price-point of robots decreasing while their capabilities are expanding, I expect to see more of these security deployments going forward.

Robot dogs are not a new configuration. Boston Dynamics’ “Spot” is a good example and it has been around for many years, being first introduced in 2016. That’s a decade ago!

Long before Spot arrived, there was Aibo. I worked for Sony for many years, and I attribute my interest in robotic dogs to Aibo, which was introduced to the world in 1999 as a “pet.” It was quite advanced for the time, having cameras and sensors enabling it to “learn” behaviors over time and respond to voice commands. It made a great Christmas gift for the children, if you could afford it.

It showed that robot dogs could be emotional companions, not just machines. As of 2018, AI (artificial intelligence) and cloud integration were added.

There’s a saying that a dog is a man’s best friend, and I think that we culturally relate to dogs far more deeply than any other animal in general.

As long as non-military robot dogs can’t shoot you, they will make entertaining augmentations to your personal security at public events. Your kids will love it!

About the Author

Tim Lindner develops multimodal technology solutions (voice / augmented reality / RF scanning) that focus on meeting or exceeding logistics and supply chain customers’ productivity improvement objectives. He can be reached at linkedin.com/in/timlindner.

The post Woof! All Bark and No Bite (for Now) first appeared on Connected World.

This is where new additive manufacturing and 3D printing research enters the equation. Purdue University engineer Monique McClain is developing new methods to control materials’ behaviors throughout the manufacturing process. Professor McClain specializes in the early manufacturing stages such as selecting binders with unique properties to hold energetic particles together and determine how they are mixed.

As an example, a study from Professor McClain looked at adhesion between two polymers with different mechanical properties—think a stiff thermoplastic and a soft elastomer—that have been combined into one structure.

Here is how this can help in manufacturing:

• Enable the two materials to blend and hold together.
• Give more options for controlling behavior.
• Improve safety.

Looking to the future, additive manufacturing will give researchers the freedom to experiment with complex geometries and tune specific properties such as burn rate and blast shape. This is simply one example of research being done in the area.

The post Success Stories: Additive Manufacturing Evolves first appeared on Connected World.

How smart are our cities? Maybe not quite smart enough, but they will be smarter in the future.

Smart cities contain several key areas of research. Let’s look at the most research figures in 2024 from Berg Research:

• Smart-street lighting: 27.9 million units (excluding China)
• Smart parking: 1.47 million units (in ground and surface mounted)
• Smart-waste collection: 1.56 million units (new on bins or retrofitted)
• Urban air quality monitoring: 206,000 units

Another key area is smart-city surveillance, which is measured in dollars rather than units. Berg Insight suggests this market, which includes both fixed and mobile video and audio surveillance solutions, reached a global market value of € 13.6 billion in 2024. This market is anticipated to grow at a rate of 15.6% through 2029.

Looking to the future, the smart-street lighting market will reach 74.5 million units in 2029, which is a 21.8% growth rate. Smart-parking sensors will see slower growth of 18.4%, while smart waste sensor technology market will be the fastest growing at 22.3%. Urban air quality monitoring will reach 633,000 units in 2029.

Outside China, Europe has emerged as the leading smart city technology adopter while North America is the second largest market. The Middle East and Asia-Pacific regions meanwhile are the fastest growing markets for smart city technology. It is certainly a market to continue to watch.

The post Fact of the Week – 2/23/2026 first appeared on Connected World.

“Utilities need to make a difficult decision,” says Dominique Meyer, CEO of Looq AI. He says they ultimately need to decide whether these poles need to be replaced or not—and making that decision can cost a lot of time and money. Meyer tells me directly that North America’s distribution poles are even more substantial than earlier estimates, placing the total near 400 million.

No doubt, utilities are under mounting pressure to meet state requirements for wildfire mitigation and storm hardening. As a result, accurate pole data has become a cornerstone of grid reliability. Yet much of this data is still gathered and processed through slow, manual, and inconsistent workflows—often requiring two people to collect the data. With the rise of AI (artificial intelligence) much of this is set to change.

Looq aims to solve the challenge of spotty, inaccurate, and unreliable data, according to Meyer, by creating a full geometric engineering grade model of every single asset. qPole enables distribution designers and engineers to transform simple field captures into accurate engineering-ready asset models.

Traditional field capture alone takes roughly 15 minutes per pole. Backoffice processing previously added another 15 minutes per pole, as engineers manually validate data. Now, that is all beginning to change. As one example of new technology, the qPole AI-assisted processing is completed in about 5-7 minutes per pole and automatically detects and models each structure and its equipment.

Meyer equates the average saving of 23 minutes per pole to represent unlocking an estimated 19 million work hours saved annually in the United States alone.

“We are enabling designers, backoffice work, to be way more effective,” says Meyer. “We’re essentially increasing their efficiency by over 60% in the backoffice, and that means that because there are not enough people that do this kind of work, those people that do it get more efficient. The industry feels a huge pain and relief around that.”

The time savings is only one component of benefit for engineers. Field measurements are also accurate to under a centimeter, which helps derive correct construction requirements, avoiding overbuilt or unnecessary projects, ultimately saving money in the long run.

“That’s the real magic behind qPole is that automation piece in matching components to geometric and image perimeters,” says Meyer.

Candidly, this is the type of innovation we need to build stronger, more resilient infrastructure here in the United States. If we want to raise our nearly failing grade, we must take swift action. Or we’ll see the report card virtually unchanged in three years.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #utilities #energy #utility 

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If you also paying close attention to the themes here on my Connected World blog then you might recognize that I have spent some considerable time looking at the basics of quantum. This month, I have  taken a deeper dive into quantum in manufacturing and in medical and additionally into finance and telco. Over on our Constructech blog, I have been looking at quantum in construction.

Here’s the hard reality: Optimization problems in global telecommunications networks represent large combinatorial search spaces that grow exponentially with network size, making them computationally intensive to solve. This is one of the perfect use cases for quantum computing to help solve. Simply, trying quantum can really explore massive decision spaces that classical computers, let’s say, get stifled on.

With this in mind, let’s consider a new announcement. Classiq, Comcast, and AMD, recently put out a new trial aimed at improving internet delivery by leveraging quantum algorithms to supercharge network routing resilience. This partnership will address a big network design challenge: identifying independent backup paths for network sites when implementing network maintenance and change management.

Unpacking the Trial

This effort signifies an interesting new shift. The objective here is that if a network site is taken offline for routine maintenance and a second site fails, network traffic could be rerouted without any disruption or degradation to customer connectivity.

This is, of course, easier said than done. To achieve this outcome, operators must identify unique backup paths that are fast, resilient to simultaneous link failures, and optimized for the lowest latency delivery, a task that becomes exponentially harder to identify as networks grow.

Enter quantum. This trial applied technologies to test whether quantum algorithms could identify unique network backup paths across change management scenarios. With the GPU-accelerated simulations, the teams were able to iterate rapidly and validate algorithm behavior, together with runs executed on quantum hardware to assess implementation success.

This is only one example of quantum in telecommunications. Quantum opens the door to new opportunities—and we are beginning to see some good use cases emerge.

Final Thoughts on Quantum

As we wrap up our month reporting on quantum and as we look ahead to what comes next, we must recognize quantum is no longer a far-off concept. We are now beginning to move into real-world applications across many vertical markets.

While still early, many of these use cases point to an interesting market shift. Quantum is becoming a more practical tool for enhancing resilience, optimizing performance, and future-proofing our world. As applications continue to expand, we will have new ways to solve complex challenges. Quantum could be the key to all of this.

Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #futureofwork #digitaltransformation #quantum #connectivity #connection

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The algorithm uses a mathematical tool called the Frobenius norm to measure differences between grids of numbers, effectively acting as a “distance formula” for large data tables. By rotating a reference dataset and comparing it to measured data, the algorithm identifies the rotation that produces the smallest difference, revealing the most likely direction of the signal.

Simulations show the method works especially well with high-resolution data and large datasets. The project began with simulated neutrino data to locate nuclear reactors, and further studies are underway.

Here is how this can help:

· Reveal information about nuclear reactors, the sun, and faraway cosmic events.

· A clear mathematical foundation for extracting direction.

· Scale with technological improvements.

Looking to the future, this formula could be applied in many fields such as astronomy, medical imaging, weather mapping, and more. It is ideal for systems that rely on pattern recognition. Certainly, it will be something to keep an eye on in the future.

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Are tech budgets up or down? Gartner suggests technology budgets are set to rise for 75% of CFOs.

Nearly half are planning increases of 10% or more.

Perhaps one of the more interesting points in the recent study is that the numbers point to an interesting trend to watch: AI (artificial intelligence) adoption is moving from pilot to scale.

Let’s consider some of the numbers that contribute to this trend.

· 60% of CFOs plan to increase finance function AI investments by 10% or more in 2026.

· 24% expect gains of between 4% and 9%.

· 47% are allocating just 1% to 5% of finance technology spend on AI.

Why is this shift occurring? The numbers suggest there are three top priorities that emerge among the research:

1. Automate

2. Shorten cycles

3. Control Costs

Confidence is definitely growing among organizations when it comes to artificial intelligence, as many are beginning to see the benefits of. Time will certainly tell how the rate of adoption pans out.

The post Fact of the Week – 3/02/2026 first appeared on Connected World.

Many of you may remember NIST (National Institute of Standards and Technology) released a very telling paper in 2004: Cost Analysis of Inadequate Interoperability in the U.S. Capital Facilities Industry.

The research unpacked that the cost of inadequate interoperability in the U.S. capital facilities industry is roughly $15.8 billion per year. Back two decades ago, billion was the big word. What followed after this report was a slow unpacking of the challenges that exist when disparate systems exist in large, complex industries.

What progress has been made in the last two decades? Let’s jump forward a bit to 2021, when Autodesk and FMI Corp., unveiled its study of more than 3,900 professionals on their data practices in 2020. The research found bad data—meaning inaccurate, incomplete, inaccessible, inconsistent, or untimely data—may have cost the global construction industry $1.85 trillion in 2020. Yes, now we are talking trillions with a T.

What is needed is a new infusion of innovation and a spirit of collaboration in the construction industry. This is precisely why we do the Constructech Top Products awards program every year. It is an opportunity for our team to intimately engage with a panel of judges including analysts, professors, consultants, and experts, to scope the landscape of innovation in construction.

We continue to see new advances among those named to the list. Consider the recent example of Intuit Enterprise Suite. In February, the company announced the launch of the new AI-powered Construction Edition for Intuit Enterprise Suite.

Certainly, the AI capabilities will bring new opportunities for construction, but it is the end-to-end nature of the product that is interesting. The new solution brings project, financial, and operational workflows together in one place, helping customers streamline operations, improve cash flow, and deliver realtime visibility into performance to drive profitable growth at scale.

Of course, this is only one example. The technology named to the Constructech Top Products are some of the best in the industry, offering capabilities to solve many of the challenges the industry faces today, such as the labor shortage.

The common thread across this year’s Constructech Top Products is not simply that they are powered by AI, leverage the cloud, or deliver enhanced dashboards, it is that they are intentionally designed to serve the needs of today’s contractor.

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #interoperability

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While 1.13 million women worked in the construction industry in 2006, that total fell to just 802,000 in 2012. What happened during that time to make the numbers drop so sharply? Simply, the 2008 Great Recession. However, since 2012, the number of female construction employees has increased. In 2024, women represented 11.2% of the construction workforce, which is the highest share in two decades, according to the NAHB (National Assn. of Home Builders).

As Peggy Smedley always says, the construction industry is cyclical in nature. There will always be downturns, but the construction industry weathers the storm, and while there are certainly changes that happen, the industry often comes back stronger than before. Because the truth is construction is essential. There will always be something that is needed.

Construction workers are also essential. In fact, with a labor shortage, the industry needs more workers than before. The industry also needs diversity in thoughts, opinions, and ideas. This is why different generations, different genders, and different races are key.

Women in Construction

Last year, Peggy Smedley penned an interesting blog about women in the workforce. The statistics show women make up nearly 47% of the overall U.S. workforce, yet in construction they only represent about 11% of workers. Even fewer of those women are in the skilled trade. While it is clearly an improvement, there is still much work to be done.

Perhaps the first step is understanding why the gap exists. A survey by PWC (Professional Women in Construction) New York from last year shows that women are drawn to construction for solid reasons: competitive pay, career advancement, professional development, strong benefits, and job security.

In fact, the industry boasts one of the lowest gender pay gaps, with women earning roughly 95% of what their male peers make, notably better than the national average.

Perhaps the second step then is getting this messaging out to the general public and building awareness about the opportunities for women in construction. This is where Women in Construction Week enters the conversation. This tradition of Women in Construction Week dates back more than six decades. Back in 1960, Amarillo Mayor A.F. Madison proclaimed the first “Women in Construction Week” to honor the founding of NAWIC (National Assn. of Women in Construction) and recognize the growing contributions of women in the field. Since that time, the movement has grown and evolved.

In 1998, NAWIC moved WIC Week to the first full week of March to align with Women’s History Month and Intl. Women’s Day. Now, the event includes national campaigns, regional programs, and chapter-led events across the United States that includes jobsite tours, panel discussions, mentorship sessions, media outreach, and community service projects.

This year, Women in Construction week also aligns with CONEXPO-CON/AGG 2026, which is being held March 3-7 in Las Vegas, Nev. With all of this converging at the same time, there is a bigger conversation that is happening around women in the construction industry.

The bigger question becomes: Are we really truly making a difference with all this messaging? It seems the numbers are finally pointing to some growth as it relates to women in the construction industry, but is that growth happening fast enough?

Want to tweet about this article? Use hashtags #construction #IoT #sustainability #AI #5G #cloud #edge #futureofwork #infrastructure #WICWEEK2026 #CONEXPOCONAGG

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Program continues Netmore’s drive to transform IoT market fragmentation into a new world of connections

Netmore Group, the leading network operator and platform provider for Massive IoT, today announced the launch of the Netmore Pulse partner program, a comprehensive program designed to provide partners with early insight into opportunities, accelerate new use cases, and turn local expertise into scalable, joint success.

The program, available worldwide, addresses longstanding industry fragmentation by combining Netmore’s network services and commercial leadership with solutions and devices proven capable to scale in markets demanding predictability and high service levels. Program participants are positioned as a qualified, low-risk choice for large-scale IoT projects, while end customers searching for trusted, high-performing partners now gain a powerful advantage by knowing solutions and hardware are ready to run at scale on the Netmore platform.

With over 200 ecosystem existing partners and the unification of world-leading partner ecosystems through its recent acquisition of Actility, Netmore’s introduction of the Pulse program is a natural evolution to a more systematic approach of driving innovation and streamlining the deployment of end-to-end IoT solutions.

Empowering Partners to Grow

The Netmore Pulse program is organized around three partner tracks: Ecosystem, Channel, and Institutional. Based on tier and commitment levels, the program provides participants with the structure, support, and resources they need to grow their IoT business efficiently. Key benefits include:

• Structured partner tiers with transparent criteria and commercial alignment
• Dedicated onboarding, training and technical enablement resources
• Partner Management and Operations support
• Joint go-to-market activities
• Recognition and future data-driven tools that reward and support strong performance

“IoT is scaling rapidly, and collaboration is the key to sustainable growth,” said Frederik Oliver, VP Growth at Netmore. “Our ambitions are clear: to deliver the world’s leading IoT platform, achieve global scale as a cost leader, and be the easiest IoT network provider to work with. Together with our partners, we are creating the world’s leading IoT ecosystem that will shape industries and deliver impact worldwide.”

Netmore’s Pulse Program is earning praise from early access participants for its clear collaboration framework, practical enablement materials, and focus on accelerating LoRaWAN adoption and joint growth.

Craig Herret, Managing Director, Alliot (Europe’s leading IoT distributor specializing in LoRaWAN solutions), noted: “Reliable connectivity is critical to ensuring successful deployments for our partners. Netmore’s Pulse Program provides a clear framework, robust onboarding and training, and excellent support, making it easier to package and resell end-to-end solutions. This is set to be an exciting year for growth and LoRaWAN acceleration.”

Felipe Gutierre, Alliance Manager at TagoIO (a global IoT application enablement platform provider managing millions of data points), added: “The Netmore Pulse Program makes it straightforward to integrate connectivity into our solutions and go-to-market. The materials are practical, the model is clear, and the team is easy to work with. We’re very positive about the opportunities ahead.”

Alexandre Russo, Telecom Manager at TC Tec (a Brazilian telecom provider scaling LoRaWAN deployments in Latin America), emphasized: “Netmore’s partner program adds structure and predictability from onboarding to delivery. The high-quality training and support help our teams move faster. We´re optimistic about scaling our joint business this year and into the future.”

Join the Netmore Pulse Program Today!

Dedicated to leading the transformation of the LPWAN ecosystem, Netmore powers some of the most advanced IoT solutions globally. Companies interested in partnering with Netmore to accelerate the adoption of IoT solutions across industries can learn more about the Netmore Pulse partner program and apply at www.netmoregroup.com/partners.

The post Netmore Launches Pulse Partner Program for IoT Growth appeared first on IoT Business News.

Deutsche Telekom launches world’s first multi-orbit IoT roaming

• Deutsche Telekom is world’s first network operator to offer IoT connectivity via both GEO and LEO satellites
• Seamless, reliable NB-IoT coverage across terrestrial mobile and satellite networks
• Innovative applications realized on standard hardware

Deutsche Telekom has reached another milestone in global connectivity: as the world’s first mobile network operator, it now enables multi-orbit roaming for the Internet of Things (IoT).

The new solution ensures that IoT devices can transmit their data seamlessly and worldwide— either via terrestrial mobile networks or via satellite, depending on the situation.

Multi-orbit roaming has now been demonstrated using a commercial NB-IoT device that operates across geostationary (GEO) and low earth orbit (LEO) satellites as well as terrestrial networks.

The solution connects Deutsche Telekom’s global IoT network (NB-IoT and LTE-M) with satellite services from several partners: Skylo, being DT’s first satellite service provider, provides coverage in geostationary orbit, while Sateliot and OQ Technology handle radio connectivity to LEO satellites.

Jens Olejak, Head of Satellite IoT at Deutsche Telekom IoT, says:

“This establishes Deutsche Telekom as the leading global network operator offering IoT connectivity across multiple satellite orbits, both technically and commercially.”

Additionally, in the second half of 2026, Deutsche Telekom’s partner Iridium’s NTN Direct will become available to DT’s business customers for IoT applications.

Iridium’s LEO constellation, known for its proven reliability and truly global coverage, will further enhance Deutsche Telekom’s non-terrestrial roaming footprint.

More coverage, more resilience, more flexibility

Multi-orbit combines the strengths of different satellite types.

Due to their fixed position at an altitude of approximately 36,000 kilometers, GEO satellites allow continuous coverage and enable real time, stable connections.

LEO satellites, on the other hand, move quickly but can provide better coverage at high latitudes and in mountainous regions, as well as enabling lower latency and higher data rates.

Together, GEO and LEO create reliable IoT connectivity even in the most remote regions.

Early Adopter Program: Prototyping the next generation of IoT

After its initial Early Adopter Program with Skylo in 2024, Deutsche Telekom launched a second prototyping initiative for Satellite IoT in 2025.

The Multi-Orbit Early Adopter Program focuses on developing IoT solutions that combine terrestrial mobile and satellite connectivity across GEO and LEO.

The program brings together 15 companies and five research institutions and is supported by partners including Sateliot, OQ Technology, Skylo, Nordic Semiconductor and KYOCERA AVX.

Three examples from the program illustrate the added value of multi-orbit roaming:

Remote Asset Management for Critical Infrastructure Operations (Datakorum)

The Spanish technology company Datakorum is using next-generation connectivity to support the remote operation of critical infrastructure assets worldwide.

Its solution enables real-time monitoring of key parameters such as quality, pressure, and system status across water, energy, and oil and gas infrastructure—even in remote areas without mobile coverage.

Beyond monitoring, operators can remotely control field equipment such as valves and actuators via radio links, improving response times and operational efficiency.

To ensure resilience in mission-critical environments, the solution leverages LEO satellites as a backup connectivity layer.

Datakorum has integrated terrestrial and non-terrestrial radio technologies into a single product based on Nordic Semiconductor’s nRF9151 module.

Maritime tracking & EU regulation (EMA / BlueTraker):

Under the brand BlueTraker, the Slovenian company EMA provides tracking solutions for fishing vessels and merchant ships.

“Hybrid connectivity” – the combination of satellite and mobile networks – ensures that vessels can reliably report their position and status even on the open sea.

This is particularly important given new EU regulations: in the future, even small vessels under twelve meters in length will be required to have a vessel monitoring system (VMS) installed on board.

Deutsche Telekom’s satellite NB-IoT (NB-NTN) option is a cost-effective and scalable standard solution for this purpose, enabling even large fleets of small boats to be networked without the need for expensive special technology.

Autonomous AI-Vision-Sensor (MountAIn):

With IBEX, French company MountAIn is bringing intelligent image processing to remote regions that have not yet been reliably connected.

Autonomous AI vision sensor processes image data directly on site (“edge AI”) and detects events such as forest fires, safety-related incidents in industrial facilities, or risks to critical infrastructure in real time.

The possibility of NB-IoT satellite connections ensures that warning messages and operating data are reliably available even in remote regions, providing the resilience required for safety-critical applications.

Since only relevant status and alarm data is transmitted, the solution also works with a narrowband internet connection.

Technical background

Deutsche Telekom and its partners validated multi-orbit connectivity on commercially available standard hardware.

Nordic Semiconductor’s nRF9151 is the first 3GPP-compliant cellular IoT module to support terrestrial NB-IoT/LTE-M as well as NB-NTN over GEO and LEO.

In tests, the module established a direct connection via Sateliot’s LEO satellites using a Deutsche Telekom SIM card—demonstrating that roaming between terrestrial mobile networks and LEO satellites works.

In addition, connectivity via Skylo (GEO) is already operationally used by customers, while integration with OQ Technology (LEO) has also been validated as part of partner activities.

Iridium (LEO) is currently being integrated and validated and will become available later this year.

For satellite connectivity, the antennas used must support the relevant 3GPP satellite frequency bands n249, n255 and n256.

These frequency bands are a prerequisite for operating NB-NTN over GEO and LEO satellites.

Suitable antenna solutions are already available from manufacturers such as KYOCERA AVX, enabling device manufacturers to build on existing components today and develop new multi-orbit NB-IoT solutions.

The post Deutsche Telekom unveils multi-orbit IoT roaming appeared first on IoT Business News.

Enterprise IoT connectivity is no longer a simple trade-off between “cheap LPWA” and “fast cellular.” In 2026, device makers and large-scale IoT operators must build connectivity strategies that survive network sunsets, uneven roaming realities, growing security expectations, and the arrival of new middle-tier 5G options such as RedCap (Reduced Capability).

This article breaks down broadband IoT vs. narrowband IoT from an enterprise architecture perspective, and proposes a decision framework to choose (and combine) LTE-M, NB-IoT, LTE Cat 1 bis, full LTE/5G, RedCap/eRedCap, and emerging satellite NTN layers.

Defining the battlefield: what “narrowband” and “broadband” mean in 2026

Narrowband IoT typically refers to LPWA cellular technologies optimised for low throughput, low power, and deep coverage—most notably NB-IoT and LTE-M. These technologies are designed for massive sensor fleets, long battery life, and low-cost modules, at the expense of data rate and (often) roaming consistency.

Broadband IoT covers higher-throughput cellular options such as LTE Cat 4/6, 5G eMBB, and private cellular variants used for cameras, gateways, moving assets with heavy telemetry, and devices that need frequent firmware updates or richer data flows.

In between sits the most interesting strategic battleground for 2026: mid-tier IoT, where enterprises want more throughput than LPWA, but cannot justify the cost/power footprint of full 5G. This is precisely where 5G RedCap (3GPP Release 17) is positioned.

Market reality check: coverage and ecosystem maturity still matter more than specs

On paper, it’s tempting to map requirements to a “perfect” radio technology. In real deployments, enterprises still get burned by three recurring issues:

• Footprint and local availability: NB-IoT and LTE-M coverage varies widely by country and operator strategy. A network being “launched” does not automatically mean it is suitable for your roaming footprint.
• Roaming and operational scale: global fleets need predictable onboarding, profile management, and lifecycle support—especially when devices are deployed for 8–15 years.
• Network sunsets: 2G/3G shutdowns continue to force migrations and redesigns. Even if your new design is “future-proof,” it still must survive the transition period—country by country.

The core enterprise lesson: connectivity decisions are as much about supply chain and operational control as they are about radio performance.

Technology map for enterprise IoT in 2026

NB-IoT: ultra-low power and deep coverage, with constraints

NB-IoT remains a strong choice for metering, simple sensors, and reporting-based assets where payloads are tiny and latency is non-critical. Its strengths—coverage extension and power efficiency—are unmatched for many low-data devices. But enterprises must validate roaming, latency tolerance, and firmware update strategy early (because “it supports updates” is not the same as “updates are operationally safe at scale”).

LTE-M: the LPWA option when mobility and interactivity matter

LTE-M is often positioned as “NB-IoT plus mobility.” For wearables, moving assets, and devices needing more interactive behaviour, LTE-M can be a better fit—especially when the product roadmap includes richer telemetry, voice features, or more frequent updates. The catch: LTE-M availability is not universal, so you must validate footprint and long-term operator support per region.

LTE Cat 1 bis: the quiet workhorse for global scale

Many enterprises in 2026 increasingly treat LTE Cat 1 bis as a pragmatic baseline where NB-IoT/LTE-M coverage or roaming is uncertain. Cat 1 bis can offer a more straightforward global story than LPWA in some footprints, with acceptable power profiles for externally powered devices or “battery plus energy-optimised design” scenarios.

5G RedCap (Release 17): the emerging mid-tier option

RedCap (Reduced Capability) was standardised in 3GPP Release 17 to reduce 5G device complexity (bandwidth, antennas, and other capabilities) while retaining significantly higher data rates than LPWA options—making it relevant for richer telemetry, industrial sensors with heavier payloads, and devices that need frequent secure updates.

However, enterprises should treat RedCap as a transition technology with real-world caveats: network readiness varies, module availability differs by region, and certification/roaming realities can lag marketing timelines.

eRedCap (Release 18 direction): why operators care

Beyond classic RedCap, the industry is framing eRedCap as part of the longer migration path that eventually allows operators to simplify networks and reclaim spectrum. For enterprises, the key takeaway is not the buzzword—it’s that your device roadmap should assume multiple technology generations during a single product lifetime.

Satellite NTN enters the enterprise playbook (as an overlay, not a replacement)

Non-terrestrial networks (NTN) are becoming more concrete for enterprise IoT—particularly via standards-aligned approaches and partnerships that blend terrestrial cellular with satellite. Enterprises increasingly want a single operational model where remote coverage is handled as an overlay to existing cellular estates.

In parallel, operators are pushing “multi-orbit” and roaming narratives, suggesting a future where satellite connectivity is managed more like a policy and profile choice than a separate device class—though enterprise buyers should remain cautious and validate commercial terms, regulatory constraints, and device power budgets.

A practical decision framework for enterprises

Instead of choosing “one technology,” enterprises should segment connectivity into connectivity tiers aligned to product families and operating environments.

Connectivity strategy patterns that work in 2026

1) Build a “two-lane” portfolio: LPWA lane + scalable mid-tier lane

For many enterprises, the winning architecture is a dual strategy:

• LPWA lane (NB-IoT / LTE-M) for low-data, long-life sensor classes.
• Scalable lane (Cat 1 bis today, RedCap tomorrow) for devices whose data needs grow over time, or where global operations and update cycles require more headroom.

This reduces long-term redesign risk and lets you graduate product families without rewriting the whole platform.

2) Treat firmware updates as a first-class connectivity requirement

Security and compliance expectations keep rising, and “secure by design” increasingly implies repeatable update capability. If your product will need frequent patches, LPWA may still work—but you must design update workflows (delta updates, staged rollout, backoff policies, telemetry gating) to avoid bricking devices or exhausting batteries.

3) Plan explicitly for legacy shutdowns and spectrum refarming

2G/3G sunsets remain a forcing function, and they expose weak asset inventories and poor provisioning hygiene. Enterprises should maintain a continuously updated device census (model, modem category, carrier profile, firmware baseline) and include “migration triggers” in contracts and operating plans.

4) Use eSIM/eUICC (and eventually iSIM) to reduce operator lock-in—carefully

Enterprises want flexibility, but the operational reality is nuanced: profile orchestration, bootstrap connectivity, and troubleshooting can add complexity. Treat eSIM/iSIM as a strategic capability when your business model truly depends on multi-operator agility—not as a checkbox.

5) Add NTN as a policy layer for “coverage exceptions”

For many verticals—utilities, logistics, environmental monitoring, maritime—NTN is becoming a realistic overlay option rather than a niche satellite-only device class. Enterprises will increasingly buy “coverage completeness” as part of an IoT connectivity service.

What to watch from 2026 onward

• RedCap commercialisation pace: module availability, operator enablement, and certification programmes will determine how quickly RedCap becomes a default mid-tier choice.
• Satellite IoT standardisation and roaming: partnerships are accelerating, but enterprise buyers should separate pilots from scalable commercial reality.
• Supply-chain geopolitics in modules and chipsets: cellular module market dynamics can influence long-term BOM assumptions.

Bottom line: connectivity strategy is now a lifecycle strategy

In 2026, the most resilient enterprise IoT connectivity strategies are portfolio-based, not technology-singleton decisions. Narrowband IoT (NB-IoT/LTE-M) remains indispensable for low-data fleets. Broadband cellular is still required for high-data devices and gateways. The strategic battleground is the middle: Cat 1 bis and RedCap-style options that balance throughput, cost, and operational scalability—while NTN overlays begin to close the “coverage gaps” that have historically forced expensive bespoke designs.

If there is one enterprise takeaway: choose connectivity the way you choose a supply chain—with redundancy, clear migration paths, and an honest view of operational constraints. The radio spec is only the beginning.

The post Broadband IoT vs. Narrowband IoT: Enterprise Connectivity Strategies for 2026 and Beyond appeared first on IoT Business News.

LoRa Alliance releases 2025 End of Year Report highlighting LoRaWAN’s next growth phase in Massive IoT.

The LoRa Alliance®, the global association behind the open LoRaWAN®standard for low-power wide-area networks (LPWANs), today announced the release of its 2025 End of Year Report. The report highlights a defining year in which LoRaWAN moved into its next growth phase, scaling from widespread adoption to becoming a foundational connectivity layer for Massive IoT across utilities, cities, buildings, industry, agriculture, and critical infrastructure worldwide.

Key trends identified in the report include:

• LoRaWAN reached 125 Million deployed devices globally, achieving a 25% compound annual growth rate (CAGR), underscoring accelerating adoption and long-term market momentum.
• Large-scale deployments continue to expand, with multi-million-device networks operated by Alliance members including ZENNER, Actility, Netmore, The Things Industries, and Veolia, alongside rapid growth in high-volume, single-use deployments such as agriculture tracking and safety systems.
• Utilities remain the largest deployment vertical, led by smart water, while LoRaWAN now leads as the top wireless technology for smart building and facility management, reflecting its position as proven infrastructure rather than experimental technology.
• Non-terrestrial network (NTN) LoRaWAN connectivity continues to advance, supported by regulatory progress in Europe and growing collaboration between terrestrial and satellite networks.
• The LoRa Alliance ecosystem expanded to 360 members, reflecting increased industry alignment around LoRaWAN as the leading LPWAN standard for scalable, long-life IoT deployments, with 57 new members joining in 2025 alone, underscoring strong collaboration.
• The LoRa Alliance surpassed 625 certified devices, with continued enhancements to certification, interoperability testing, and self-certification programs to support large device portfolios and faster time-to-market.

The report also highlights continued evolution of the LoRaWAN standard to support scale, efficiency, and regulatory alignment, including new data rates to improve network capacity and battery life, expanded regional spectrum support, and growing interoperability across public, private, community, and satellite-enabled networks.

“2025 marked a clear inflection point for LoRaWAN,” said Alper Yegin, CEO of the LoRa Alliance.

“We are now seeing sustained, exponential growth driven by real-world deployments at scale. LoRaWAN has firmly established itself as essential infrastructure for Massive IoT, complementing cellular, Wi-Fi, and Bluetooth.”

Additional highlights from the 2025 End of Year Report include:

• The launch of the LoRaWAN Success Story Database, creating the industry’s most comprehensive public repository of real-world LoRaWAN deployments and reinforcing market confidence through proof, not promises.
• Expanded regulatory engagement, including European approval for satellite-to-low-power device communications and continued advocacy to protect critical unlicensed spectrum globally.
• Strong global engagement, with the Alliance’s digital community exceeding 90,000 followers and subscribers, amplifying member successes and accelerating ecosystem visibility worldwide.

Looking ahead, the Alliance will continue focusing on scaling deployments, expanding regulatory alignment, strengthening interoperability, and increasing ecosystem collaboration as LoRaWAN becomes an increasingly integral part of the global connectivity stack, standing alongside Wi-Fi, cellular, and Bluetooth.

The post LoRaWAN Enters Next Growth Phase as Massive IoT Scales appeared first on IoT Business News.

Telit Cinterion to Present CMB100 Demo and Advanced eSIM Innovation at MWC Barcelona 2026

• Experience the latest in rapid device prototyping, showcasing the CMB100 embedded modem and NExT eSIM featuring GSMA SGP.32-ready profile download, swapping and deleting for seamless activation testing.
• Explore advanced connectivity solutions that simplify global deployments, network access and data management, with improved visibility, security and control.

Telit Cinterion, an end-to-end IoT solutions enabler, will highlight its newest advancements in connectivity and global SIM activation at MWC Barcelona 2026, taking place March 2 to 5. Attendees visiting stand 5B32 will see how Telit Cinterion helps OEMs prototype and scale mission-critical IoT worldwide with its portfolio of enterprise grade communication modules, embedded connectivity, and AI-powered edge intelligence.

Featured Highlights at MWC Barcelona 2026:

• CMB100 Embedded Modem with NExT eSIM– A live demonstration showing the full eSIM lifecycle, including profile download, swapping, and deletion, paired with temperature and humidity measurements and CMB100 capabilities such as location and radius reporting.

• NExT eSIM Flex – Simplifying global deployments by eliminating the need to manage physical SIM inventory, reducing operational complexity, and accelerating time to market with flexible subscription management and broad carrier interoperability. It enables fast, remote profile updates that keep devices current and compliant as connectivity requirements evolve throughout the device lifecycle.

• Nokia Cognitive Digital Mining Demo: Showcasing the Nokia Cognitive Digital Mining (CDM) platform, powered by Telit Cinterion advanced modules, to deliver real‑time edge intelligence and SLA‑driven multi‑access networking for next‑generation mining operations and mission-critical networks.

• ME310M1 – One of several Telit Cinterion modules incorporated in the Nokia CDM demo and slated for Skylo certification later this year, demonstrates our continued partnership in NTN innovation.

These innovations demonstrate how Telit Cinterion is helping enterprises and operators enhance reliability, reduce operational costs, and deploy next‑generation IoT and edge‑intelligent applications across critical industries.

“At MWC Barcelona, we’re raising the bar for how global IoT is designed, activated, and scaled. Powered by our award‑winning NExT eSIM Flex and fully digital, GSMA SGP.32‑ready eSIM provisioning, OEMs can finally deliver true single‑SKU devices – activated seamlessly in‑factory or instantly at first power‑up in the field,” said Martin Krona, President Services and Solutions at Telit Cinterion.

“Service providers gain unmatched reach and flexibility to launch applications anywhere, while mission-critical industries can rely on our resilient, always on network stack engineered for maximum uptime.”

The post Telit Cinterion Showcases CMB100 and eSIM at MWC 2026 appeared first on IoT Business News.

Latest IoT News Summary

A recent CRN article (Nov. 21, 2025) highlights eight major IoT trends shaping 2025, including AI‑driven innovation, workforce skill gaps, tariff‑related cost pressures, and increasingly complex cybersecurity demands.

Article Summary: “8 Big IoT Trends To Watch in 2025” (CRN)

Source: CRN – 8 Big IoT Trends To Watch In 2025
Direct link: https://www.crn.com/news/internet-of-things/8-big-iot-trends-to-watch-in-2025

Key Points from the Article

• AI Integration Is Accelerating IoT Growth
  Analysts note that the rapid expansion of AI capabilities is driving demand for IoT solutions, but organizations face a skills gap in integrating AI into IoT products and workflows.
• Tariffs and Global Trade Tensions Are Raising Costs
  Evolving tariff strategies—particularly from the Trump administration—are increasing the cost of raw materials, squeezing margins for IoT hardware vendors. IDC reports 60% of enterprises see rising tariffs as a threat to profitability.
• Cybersecurity Remains a Major Vulnerability
  IoT deployments often span multiple locations and device types, creating a complex security landscape. Verizon’s IoT leadership warns that IoT environments require more sophisticated security architectures than traditional IT.
• Collaboration Is Becoming Essential for Industrial IoT
  Executives argue that the future of AI‑powered industrial IoT will depend on ecosystem collaboration—hardware, software, and connectivity providers working together to support hybrid AI models at the edge.

Assessing the Completeness and Narrative of the Article

Strengths of the Article

• Broad yet timely coverage:
  The piece captures the most relevant macro‑forces shaping IoT in 2025—AI, tariffs, security, and ecosystem collaboration. These tend to be the dominant themes in current IoT discourse.
• Expert‑driven insights:
  CRN’s interviews with analysts from IDC, IoT Analytics, and executives from major IoT players add credibility and depth.
• Clear articulation of industry pain points:
  The article accurately reflects the real-world challenges IoT teams face: skills shortages, rising hardware costs, and fragmented security postures.

Where the Article Falls Short

• Limited quantitative data:
  Aside from the IDC statistic on tariffs, the article relies heavily on qualitative commentary. More data—market forecasts, adoption rates, or security incident trends—would strengthen the narrative.
• Underrepresentation of consumer IoT:
  The article focuses almost exclusively on industrial and enterprise IoT. Consumer IoT (smart home, wearables, automotive) is barely mentioned, despite being a major driver of global IoT device volume.
• Missing discussion of regulatory shifts:
  With increasing global scrutiny on data privacy, device certification, and AI governance, regulatory changes are a major IoT trend that deserved more attention.

Overall Opinion

The article is directionally accurate and timely, offering a solid snapshot of the IoT landscape in 2025/2026. However, it feels incomplete as a holistic industry overview. A more balanced narrative would include consumer IoT, regulatory frameworks, and quantitative market data. Still, for enterprise‑focused readers, it provides a valuable and credible summary of the forces shaping IoT’s near future.

Look to future articles on IoT where we hope to fill in some of the gaps in coverage identified. Share with us in the comments what you'd like to see in terms of IoT topics and coverage.

Written/published by AI Quantum Intelligence with the help of AI models.

For decades, the factory floor was a place of rigid automation—machines programmed to do one thing, over and over, in a "black box" environment. But as we move through 2026, a fundamental shift has occurred. The "dumb" robot has been replaced by Physical AI, and the static assembly line has evolved into a living, breathing ecosystem.

This transformation is driven by The Convergence: the seamless integration of the Internet of Things (IoT) as the nervous system, Artificial Intelligence (AI) as the brain, and Robotics as the hands. Together, these technologies aren't just making factories faster; they are making them "thrive" through self-healing systems and collaborative human-robot environments.

1. The Nervous System: IoT and the Data Goldmine

At the heart of the modern factory lies the Industrial Internet of Things (IIoT). Thousands of sensors now monitor everything from the vibration of a bearing to the temperature of a soldering iron in real-time.

• Real-Time Transparency: Unlike traditional manufacturing where issues were discovered after a batch was ruined, IoT provides a "glass factory" view.
• Edge Computing: In 2026, we’ve moved beyond sending all data to the cloud. Critical decisions are made at the "Edge" — directly on the factory floor—reducing latency to milliseconds.

2. The Brain: Predictive Maintenance and Agentic AI

Data is useless without intelligence. This is where Machine Learning (ML) and Agentic AI step in. We are moving from predictive maintenance (telling you when it might break) to prescriptive action (fixing it autonomously).

The "Self-Correcting" Factory: In 2026, if an AI agent detects a 0.5% drift in a robotic arm's precision, it doesn't just send an alert. It cross-references the production schedule, identifies a gap, orders a replacement part via the digital supply chain, and reroutes the workload to a backup line—all before a human operator even notices the drift.

The Impact of AI on Downtime (2026 Projections): Metric | Improvement with AI | Unplanned Downtime | Reduced by 35–45% | Maintenance Costs | Reduced by 25–30% | Equipment Lifespan | Increased by 20%

3. The Hands: The Rise of Humanoids and Cobots

The physical manifestation of this convergence is the new generation of Collaborative Robots (Cobots) and Industrial Humanoids. As seen at CES 2026, companies like Hyundai and Boston Dynamics are deploying robots like Atlas not to replace humans but to "partner human progress."

• Human-Centered Design: These robots are equipped with tactile sensors and 360-degree vision, allowing them to work safely alongside humans without safety cages.
• Versatility: Unlike old-school robots fixed to the floor, Autonomous Mobile Robots (AMRs) navigate complex warehouses, picking and transporting goods with the dexterity of a human worker.

4. The Digital Twin: Simulation Before Execution

One of the most powerful tools in the “AI and Quantum Intelligence” toolkit is the Digital Twin. Before a single physical bolt is turned, the entire factory is built in a virtual environment.

• "Simulate-then-Procure": Manufacturers now use high-fidelity simulations to test "what-if" scenarios. What if we increase line speed by 10%? What if the supply of a specific resin is delayed?
• Virtual Commissioning: Using tools like NVIDIA Omniverse or Siemens Digital Twin Builder, engineers can train AI models in a virtual world so that when the physical robot is switched on, it already "knows" its environment.

Conclusion: Together We Learn, Together We Thrive

The convergence of AI, IoT, and robotics is not just a technical milestone; it is a cultural one. It represents a shift from "man vs. machine" to a collaborative era where technology handles the dangerous, repetitive, and mundane, freeing humans to focus on strategy, design, and innovation.

As we continue to share these learning resources, remember that the goal of a smart factory is not just output—it’s the creation of a resilient, sustainable, and human-centric future.

Frequently Asked Questions

Q: What exactly is the "Convergence" in modern manufacturing? A: It is the seamless merging of three distinct technologies: IoT (the nervous system), AI (the brain), and robotics (the hands). When these work together, they move the factory from a collection of isolated machines to a living ecosystem that can learn and adapt in real-time.

Q: How does AI actually reduce factory downtime? A: Through a process called "prescriptive maintenance." Instead of waiting for a machine to break, AI monitors vibrations and temperatures. If it detects an anomaly, it can automatically adjust the machine’s load or schedule a repair during a planned break, reducing unplanned downtime by up to 45%.

Q: What is a Digital Twin and why is it important? A: Think of a Digital Twin as a "flight simulator" for a factory. It is a virtual 1:1 model of your physical equipment. By running simulations in the digital twin first, companies can find errors and optimize workflows without ever risking damage to real machines or stopping production.

Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence).

But a quieter, more profound revolution is happening concurrently. It is the fusion of AI with the Internet of Things (IoT). This is not AI in a chat window; this is AI in our homes, our cities, our factories, and our bodies.

This "AIoT" — the "Artificial Intelligence of Things" — is the network that connects and controls smart grids, autonomous supply chains, intelligent home appliances, and city-wide sensor networks. While the promised business case is one of total efficiency, predictive maintenance, and seamless convenience, the potential threats are woven into the very fabric of our physical reality. They are less about what we think and more about how we live.

Here are some of the oncoming threats as we advance in this new domain, and the profound challenge of measuring innovation against the real cost to humanity.

1. The New Economic Fragility: Beyond Job Displacement

While we discuss AI replacing white-collar jobs, AIoT is aimed squarely at the physical and logistical backbone of our economies.

• Systemic Risk as a Business Model: The primary "value" of AIoT is connecting disparate systems. An intelligent port connects to an autonomous trucking network, which connects to a smart warehouse, which connects to a regional power grid. This hyper-efficiency creates unprecedented systemic fragility.
• Example: In the past, a cyberattack might breach a company's database. In an AIoT-enabled world, a single vulnerability (like a botnet) could be exploited to halt a nation's entire food distribution network, shut down its power grid, or cause physical accidents by hacking fleets of autonomous vehicles. The financial cost is no longer just data theft; it's a complete, physical economic shutdown.
• Targeted Labour Displacement: The jobs at risk here are not just repetitive, but physical. AI-powered sensors that predict equipment failure will replace maintenance and inspection crews. Fully autonomous warehouses (like those already in use) eliminate the need for most pickers, packers, and forklift operators. This creates a parallel wave of unemployment that is distinct from the knowledge-work disruption of generative AI.
• Data-Driven Financial Exclusion: Your financial well-being may soon be tied to the data streamed from your "smart" life.
• Example: Imagine your car insurance premium being calculated in real-time based on AI's judgment of your driving, not just your accident record. Or, more insidiously, a smart appliance reporting your energy use patterns, which an algorithm correlates with "financial distress," lowering your credit score without your knowledge. This creates a new, opaque class of algorithmic poverty.

2. The Atrophy of Human Competence: The Threat to Learning

The core promise of AIoT is a "frictionless" life, where your needs are anticipated and met before you even register them. The threat here is not to what you learn, but to your capacity to learn.

• "Cognitive Offloading" as a Lifestyle: Research has already shown that over-reliance on AI tools can lead to "cognitive offloading," which correlates negatively with critical thinking. When this moves from a search engine to your entire environment, the impact is profound.
• Example: A smart kitchen doesn't just give you a recipe; it guides you step-by-step, tells you when to stir, and manages the temperature. The human is reduced to a pair of hands. This prevents the user from learning the principles of cooking, such as heat management or flavor combinations.
• The Loss of Practical Resilience: When our homes automatically manage their own energy, our cars drive themselves, and our environments are perfectly optimized for comfort, we lose the basic skills of self-sufficiency. This "friction" is where competence, problem-solving, and resilience are built. A society that offloads all practical skills to an AI network becomes dangerously dependent and fragile, unable to cope when the system inevitably fails.

3. The Erosion of the Social & Private Sphere

When the "computer" is the room you are in, the concepts of privacy and autonomy are fundamentally challenged.

• The End of Privacy as a Concept: This is not about your browser history. This is about ubiquitous, passive surveillance. Smart speakers listening to your emotional tone, smart city cameras using gait and facial analysis, and smart thermostats that know when you are home and when you are not.
• Example: This creates a powerful "chilling effect." When all public and private spaces are monitored, it changes how we interact. We may become less willing to engage in dissent, have difficult private conversations, or express unpopular opinions, leading to a sterile, conformist, and less democratic public sphere.
• Automated "Nudging" and Social Control: The AIoT world is not just a passive listener; it is an actor. It is designed to "nudge" your behavior—ostensibly for your own good.
• Example: Your smart fridge might "nudge" you away from sugary snacks. Your smart city's public transit system might "nudge" you to a different route to manage crowd flow. But who sets the rules? This technology gives its owners (corporations or governments) a direct tool to manipulate human behavior at a population scale, optimizing for profit or social conformity, not individual autonomy.
• The Decline of Social Skill: As IoT devices mediate more of our interactions, we risk a further decline in face-to-face communication. This can lead to a documented reduction in the ability to read nonverbal cues, practice empathy, and navigate the complex, un-optimized friction of human relationships.

The Measurement Dilemma: How to Value a Human?

This brings us to one final, critical question: How do we measure the business case for innovation against the real cost to human needs?

The terrifying truth is that we currently can't—because our models are dangerously one-sided.

The "business case" is easy to quantify. It is measured in:

• $ Dollars saved in labour costs.
• % Percentage points of efficiency gained.
• # Number of hours saved in a supply chain.

The "human cost" is abstract and notoriously difficult to quantify. What is the dollar value of:

• A population's critical thinking skills?
• The concept of personal privacy?
• The resilience of a community?
• The feeling of human autonomy?

Because we cannot easily measure these human costs, they are valued at zero in a traditional ROI calculation. The business case always wins.

To fix this, we must fundamentally change our "balance sheet." We need to move beyond simple ESG (Environmental, Social, Governance) checklists and create new, mandatory metrics for "human-centric" innovation.

1. Systemic Risk Audits: Businesses and governments deploying large-scale AIoT systems must be required to model and insure against the catastrophic cost of a systemic, cascading failure. This gives a hard financial number to "fragility."
2. Cognitive Impact Assessments: Just as we have environmental impact assessments, we should demand "cognitive impact" studies. Does this product automate a skill and promote passive dependence (cognitive offloading), or does it augment the user and build new skills?
3. "Autonomy & Privacy" Scores: We need a simple, readable score for all smart devices, much like a nutrition label. It would clearly state:
• What data is collected?
• What data is necessary for it to function?
• How is this data used to "nudge" you?
• Can you fully opt out and retain core functionality?

Ultimately, the true "business case" for any innovation cannot be separated from the stability and well-being of the society it serves. An innovation that generates billions in profit but creates a fragile, dependent, and socially isolated population is not an asset. It is a long-term liability, and the bill will, eventually, come due.

Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence)

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