Published by Andrew Foster on LinkedIn

A recent article in IoT Evolution World titled “AIoT in 2026: From Edge Intelligence to Agentic Systems” makes a compelling point: we are moving beyond simply connecting devices and collecting data. The next phase of industrial transformation is about intelligence at the edge—systems that don’t just observe, but act.

We couldn’t agree more.

For years, industrial IoT initiatives focused primarily on visibility. Get the data. Normalize it. Analyze it in the cloud. That approach delivered insights—but not always outcomes. Industrial environments operate in milliseconds, not minutes. Decisions about machine health, process optimization, quality control, and safety cannot depend on a round-trip to a distant data center.

That’s why edge computing has become foundational to industrial applications such as manufacturing, building automation and renewable energy.

Edge intelligence brings compute power to where data is generated—on factory floors, in substations, across energy assets, and within building networks. Instead of sending raw data upstream, we make the data accessible, actionable and manageable at the edge. This reduces latency, lowers bandwidth costs, and dramatically increases system resilience.

But in 2026, the conversation has evolved even further. AI at the edge is reshaping expectations.

Not long ago, it was common to aggregate data in a central system, analyze it there, and send reports or dashboards back to the plant or factory floor. That approach still has value, but it often lags behind what operations teams actually need. Today, for example, manufacturers are placing machine learning and AI capabilities directly on edge systems, alongside the equipment itself. Maintenance models manage developing issues in the moment, vision systems evaluate quality as products move through production, and optimization routines are fine-tuned processes on the fly. Intelligence is woven directly into the way the plant runs.

Making that work takes more than powerful devices on the factory floor. It depends on edge software that can scale, integrate cleanly with existing systems, and operate reliably in demanding industrial environments.

Factory environments aren’t clean, greenfield IT stacks. They’re a mix of aging PLCs that have been running for decades, newer smart sensors, proprietary fieldbus protocols, and increasingly tight cybersecurity controls layered on top.

Bringing AI to the edge in that setting isn’t as simple as deploying a container and walking away. It means coordinating applications across multiple sites, handling remote updates without disrupting operations, integrating cleanly with existing OT and IT systems, and ensuring performance remains predictable and stable. In short, it requires software that’s designed specifically for how industrial operations actually run—not how we wish they did.

That’s where we see the true opportunity.

At IOTech, we view edge computing as the operational backbone. It’s the layer that securely bridges OT and IT. It’s the platform that enables industrial data normalization and contextualization. And increasingly, it’s the runtime environment where AI models execute safely and reliably at scale.

Manufacturers are under pressure to increase productivity, reduce downtime, improve energy efficiency, and enhance cybersecurity—all while navigating workforce shortages. Centralized architectures alone cannot meet those demands. Distributed intelligence is becoming a competitive requirement.

What excites us most is that we are just beginning to unlock the full potential of edge-native AI. As models become more efficient and orchestration technologies mature, we will see greater autonomy across industrial systems. Not in a science-fiction sense—but in practical, measurable improvements in uptime, yield, and operational agility.

Edge computing is now far beyond the experimental phase. It’s becoming the foundational infrastructure that enables AI-driven industrial operations.

The companies that recognize this shift—and build their digital strategies around distributed intelligence—will define the next era of industrial innovation.

The post From Edge Intelligence to Autonomous Industry: Why 2026 Is a Tipping Point appeared first on IOTech Systems.

Autrhor: Andrew Foster, IOTech Product Director

When we first introduced the IOTech Alarm Service, our goal was clear: deliver a unified,
standards-based approach to alarm management that works across complex industrial edge
environments. Since becoming generally available last year, the Alarm Service has
continued to evolve — and today we’re excited to showcase one of the most significant
enhancements yet: a powerful new User Interface designed to make alarm management
simpler, faster, and more intuitive.

The new Alarm Service UI provides a clear, consolidated view of alarms across the edge.

Designed for Clarity and Control

The new UI has been built with operators and engineers in mind. It provides a clear, consolidated view of alarms from across your edge environment, making it easy to understand what’s happening at a glance. From active and acknowledged alarms to historical events and alarm routing management, everything is accessible through a clean, modern interface that reduces complexity and improves situational awareness.
Common alarm operations — such as acknowledgment, confirmation, shelving, and suppression — are now just a few clicks away. It’s also very easy to create and manage
alarm routes to all supported endpoints, including email, SMS, Telegram, MQTT, and more. This streamlines workflows, reduces response times, and helps ensure that critical alarms are handled consistently and effectively.

Managing and responding to alarms, including viewing alarm history, is quick and intuitive.

More Than Just a UI

Alongside the new interface, the Alarm Service itself has continued to mature. Built on the
OPC UA Alarms & Conditions standard, it provides a robust, technology-agnostic foundation
for alarm normalization, lifecycle management, and intelligent routing to external systems.
Whether deployed with Edge Central, EdgeX Foundry, or embedded into custom solutions, the Alarm Service is designed to scale with your operational needs.

Watch the Demo

To bring it all together, we’ve created a short demo video that walks through the Alarm Service UI and shows how easily users can configure, view, manage and route alarms across multiple sources. 

Watch the video to see how the IOTech Alarm Service helps you stay in control of your
edge systems.

The post Watch the IOTech Alarm Service in Action appeared first on IOTech Systems.

It’s an exciting time to work at the edge. Our relatively quiet, yet critical, sphere of computing is waking up. Why? It’s due to the demand for useful data, the newfound usability of AI, and the continued success of open source and commercial edge platforms finding customer deployments.

Looking forward into 2026, I see several drivers that will significantly impact and propel edge computing forward. These will be pulled by progress in usable AI and the need for operational efficiency.

Below are six predictions for the edge computing market in 2026. These are drawn directly from our team’s work, customer investments, and trend-spotting.

Prediction 1: The conversation will start with “AI managing the edge” before shifting to “AI at the edge”.

In 2026, we will move past the debate over whether we can use AI at the edge. We have first-hand knowledge that the usage and deployment is ramping up. That is not the prediction. The prediction is that the earliest production use cases will focus on the day-to-day operational work required to keep large edge deployments running smoothly, rather than the high-concept applications people tend to imagine.

Unlike process control data, AI managing the edge can begin with a cloud host, which avoids an up-front capital investment in edge hardware. It also doesn’t require the heavy validation cycles that traditional AI projects demanded, where the verification work often outweighed the benefit. And it sidesteps the site-by-site customization that used to consume more effort than the original technology development.

The biggest advantage, though, is that this kind of automation can be adopted in small steps. Organizations can build confidence and maturity as they go, using AI to monitor and address edge health issues, verify configuration correctness, surface security vulnerabilities, and handle routine maintenance tasks such as log analysis, upgrades, and device triage.

The most successful adopters will be experimenting with agentic (autonomous) capabilities to further streamline operations.

Prediction 2: 2026 is the year that messy industrial data finally becomes usable at scale.

There is a little wordplay here. Obviously, data is already being used at scale today. But the prediction is that we’ve reached a tipping point where we can finally unlock messy data for secondary use cases.

Part of any effort to break down data silos has struggled to find and label intrinsic meaning that can be understood beyond a given silo.  This, in turn, hampered early efforts to create cost-effective, multi-source analytical and AI tools, digital twins, or even the simple act of data consolidation to explore other uses.

These efforts have historically been hampered by the extremely high manual cost of identifying, sorting, and tagging this data to make it consumable by additional systems. This has been an accumulation of technical debt for so long that breaking through the debt seemed perpetually aspirational.

However, the same generative AI technologies transforming other sectors are being applied to identify meaning in device and point names that would typically require extensive manual evaluation. It’s not a perfect panacea: when there is no signal in the noise, even a skilled human will struggle with the task.  Yet once expectations are settled, then even reducing the manual workload by 80% is transformative and justifies continued investments in the capability.

This attack on technical debt will finally advance aspirations around ontologies and tagging, connecting systems, and enabling us to build more valuable use cases from the unlocked data.

Prediction 3: 2026 is going to be the year of the small model at the edge.

This prediction is based on practicality and economics.  We’ll let the cloud parties keep building new atomic power plants to fuel the training of frontier LLMs. At the edge, we can’t build out this kind of capability on spec, but if we reduce our expectations and tighten the scope of the problems being addressed, we find that small models are proving far more valuable (see prediction #2, for example).

Remembering the requirements that drove the edge, namely, keeping data local for privacy, secrecy, or legal considerations, means that we can’t really send all of this data to a cloud-hosted LLM.  Nor can we afford the (upfront) high costs of GPUs to run frontier models at the edge.  It is also difficult to address the limitations in available power and heat dissipation, which make it quite unreasonable to deploy hardware sufficient for the largest models when the return on investment is not yet proven.

There is a natural fit between solving a known problem—such as parsing log data or analyzing error messages—with a small language model trained on a single input modality.  We don’t need audio, speech, graphics, and video to debug a dataflow or to scan the health of a container. These smaller models can be trained with cloud resources and then deployed on modest, if not existing, hardware investments. They deliver immediate value and help break the daunting return-on-investment paradoxes that have plagued AI deployments for years.

Prediction 4: Edge platforms will serve as the substrate, the foundational layer that future AI deployments require.

In the semiconductor world, the substrate is the piece of fiberglass embedded with wiring to map the highly valuable chip to the motherboard to which it will be attached.  It’s boring, but it’s important because it connects the engine to all of the I/O that drives the CPU.

To extend the analogy to our domain, the edge platforms (basically Linux servers of various sizes) need to be able to map the AI workloads to the data and the organizations that need it.  These platforms are the picks and shovels to fuel the AI gold rush. Edge platforms need to be able to deploy, update, manage versions, debug, diagnose, and otherwise function across a dynamic, rapidly changing environment as the frontier expands forward.

Organizations need to consider the substrate they are building on. They need to ensure they choose a path that makes it easy to normalize and access data, to repeatedly configure dataflows, workflows, and analytics engines and AI models. They also need to ensure that they are resting their future on a solid foundation.

Prediction 5: Organizations will stop tolerating multiple, duplicate edge platform investments and move toward company-wide standardization.

There is a growing awareness (driven by team size challenges) of the inefficiency caused by multiple teams owning duplicate platforms to handle the foundational aspects of edge computing.  It isn’t a criticism of how they ended up there, since each team made a series of reasonable cost and benefit decisions along the way. But the sum result is often a waste of resources to maintain duplicate platforms instead of using those resources to build value-added capabilities.

We’ve seen repeated instances of this pattern and feel confident that it will continue.  Organizations will shift their resources and investments to modular platforms that make efficient work of deploying edge workloads — using open solutions like IOTech’s Edge Central or EdgeX Foundry. This allows these organizations to focus instead on building the higher-level, differentiating value that their customers expect.

Prediction 6: AI will become a practical ‘genius persona’ knowledge source for technicians, helping to bridge the knowledge gap left by retiring experts.

We are suffering a knowledge gap as individuals with decades of experience are leaving the workforce. Combining some of the predictions shared above, the pieces are coming together to deliver AI models that combine large volumes of textual training data, as well as live data from running systems. Therefore, a technician can query the model as if chatting with a senior teammate.

This offers an optimistic rebuttal to the situation in which senior technologists gain productivity by using LLMs like a “team of interns”—but not actually training a team of interns—thereby depriving future team growth. The need is for models to augment the senior technicians as a knowledge source for training people on how to succeed in their daily jobs.

This is also an interesting twist to the long story of digital twins, whose exactness made them too expensive to deploy in a messy world with messy data. But now they can start to express their purpose via interactive industrial AI models.

Conclusion

In short, 2026 won’t be defined by putting massive AI models everywhere. It will be defined by using targeted, practical AI to finally make sense of messy data, stabilize our platforms, and close the expertise gap. The organizations that focus on strengthening their edge foundations, standardizing their platforms, and adopting AI where it removes daily friction will see the biggest returns. Those investments set the stage for the higher-value applications everyone talks about but haven’t yet been able to deliver.

The post 2026 will be the year that AI is deployed at the Edge appeared first on IOTech Systems.

New Solution Provides Unified, Standards-Based Alarm Management at the Edge

San Francisco (November 20th, 2025) – IOTech Systems, the leader in open edge computing, today announced the general availability of its Edge Alarm Service, a powerful new solution designed to revolutionize how industrial organizations monitor, manage, and respond to alarms across complex edge environments.

As industrial edge systems grow in scale and diversity—spanning a wide variety of different devices, protocols, and operational technologies—alarm management has become a critical challenge and difficult to unify. IOTech’s Edge Alarm Service addresses this head-on with a standards-based, and technology-agnostic approach to alarm normalization, routing, and control.

“Our new Alarm Service empowers users with unmatched control over their edge alarms, delivering flexibility and interoperability across complex industrial systems,” said Keith Steele, CEO of IOTech. “Built to scale, integrate, and adapt across any edge environment, it enables customers to respond to operational challenges with greater speed, clarity, and confidence.”

Built on Industry Standards for Maximum Compatibility

The Edge Alarm Service is built on the OPC UA Alarms & Conditions standard and aligned with IEC 62682. This ensures broad compatibility across industrial systems—from OT devices (e.g. BACnet, Modbus, etc.) and PLCs to other sources of alarm data such as rules engines and analytics components. It can be deployed with IOTech Edge Central, integrated with open-source EdgeX Foundry, or embedded within custom frameworks, enabling seamless alarm unification in virtually any industrial environment.

Leveraging an event-driven architecture, the service evaluates alarm data in real-time, generating standardized alarm events that can be viewed and managed via a dedicated UI or a comprehensive REST API.

Open API Access and Smart Routing

IOTech’s Edge Alarm Service is designed for developers and integrators, with its powerful REST API providing full lifecycle access to alarm data and operations, including:

• Monitoring and managing alarms throughout their lifecycle
• Querying current and historical alarm states
• Managing alarms with functions such as acknowledgment, confirmation, shelving, and suppression
• Subscribing to alarm events for efficient data-driven applications
• Secure authentication and access control

The service also enables intelligent alarm routing to multiple endpoints, including Email, SMS, MQTT, Webhooks, and Telegram, ensuring critical alerts reach the right systems or personnel at the right time—enhancing responsiveness and operational safety.

Easy Configuration and Scalable Architecture

Alarms can be defined through simple configuration files or the REST API, supporting everything from basic thresholds to advanced multi-level models such as “high-high” critical events. With an architecture designed for scalability, the Alarm Service can process alarm data from many independent concurrent sources and is ready to grow with your edge deployment.

Intuitive Alarm Management UI

For end-user customers or operational teams who choose not to layer their own management tooling on top of the REST API, IOTech provides a modern, intuitive web-based interface. Designed with usability in mind, the UI offers clear visualization, streamlined navigation, and real-time interaction, making alarm monitoring and management fast, efficient, and accessible to all levels of operators.

About IOTech

IOTech Systems is a leading provider of edge computing solutions, delivering high-performance industrial IoT platforms that power intelligent operations at the edge. The company’s solutions provide seamless integration between sensors, devices, applications, and the cloud—enabling real-time data acquisition, edge analytics, and scalable management across diverse industrial environments. Built on an open-source foundation and aligned with industry standards, IOTech champions vendor neutrality and interoperability to provide customers maximum flexibility and choice. IOTech empowers these organizations to harness the full potential of their edge infrastructure. Learn more at www.iotechsys.com.

The post IOTech Unveils Next-Generation Alarm Service for Industrial Edge Systems appeared first on IOTech Systems.

The post Solving the Building Data Challenge appeared first on IOTech Systems.

The post IOTech Enhances Edge Manager with Powerful Tooling and Deployment Upgrades appeared first on IOTech Systems.

Discover IOTech’s latest thought leadership and industry recognition across leading technology publications. From expert contributed articles by our team to featured coverage by industry analysts and journalists, explore how IOTech is shaping conversations around industrial edge computing, cybersecurity, and AI-driven solutions.

Technical Insights: Securing the Edge: why IIoT growth is creating a hidden security gap

June 25, 2025 | By Andrew Foster | IOT Insider

Industrial organisations are racing to modernise. Sensors, controllers, and Edge platforms are being deployed across factories, plants, and warehouses to enable real-time visibility, automated control, and smarter operations. But in the rush to connect, many have overlooked one critical factor: security.

As legacy operational technology (OT) systems are brought online to feed data into cloud analytics platforms and enterprise software, they’re entering a threat landscape they were never designed to survive. Industrial systems that operated safely for decades behind locked doors and air-gapped networks are now exposed to global attack surfaces. This shift is creating a growing risk that is catching many organisations off guard.

Read more

Expert Views: The Looming Cybersecurity Threat at the Industrial IoT Edge

June 4, 2025 | By Andrew Foster | RFID Journal 

The Industrial Internet of Things (IIoT) has transformed how industries operate, delivering efficiencies and insights previously unimaginable. Businesses across manufacturing, logistics, energy, and supply-chain sectors are now rapidly connecting legacy factory equipment, RFID-enabled assets, and operational technologies to cloud-based analytics platforms.

But as companies accelerate their adoption of these powerful technologies, they’re opening the door to a growing and often overlooked security crisis. Simply put, the industrial edge, where legacy equipment interfaces directly with modern cloud services, has become a new and urgent cybersecurity battleground.

Read more

IOTech Launches Edge Central 4.0 to Tackle Industrial AI Data Demands at the Edge

May 23, 2025 | By Stephen Mayhew | Edge Industry Review 

Edge Industry Review provides in-depth coverage of the Edge Central 4.0 launch, exploring how this advanced open edge data platform is specifically designed for AI-driven industrial edge solutions. The publication examines the platform’s optimization for demanding industrial environments, detailing its comprehensive capabilities, including real-time alarm management, scalable data storage, and enhanced security features.

The coverage delves into the key new functionalities such as Edge Alarm Service, Edge Historian Service, Python SDK, PostgreSQL as the default database, time-based scheduling, and improved access control. Edge Industry Review also highlights how Edge Central 4.0’s alignment with EdgeX Foundry’s open-source standards ensures crucial interoperability and flexibility for diverse industrial applications.

Read more

Cybersecurity: Why Industrial Edge Cybersecurity Demands a Fresh Approach

May 22, 2025 | By Andrew Foster | manufacturing.net 

Industrial environments are embracing digital transformation at unprecedented speed. Manufacturing plants, processing facilities, and critical infrastructure organizations are rapidly connecting legacy equipment to modern cloud platforms, integrating machines and sensors into vast Industrial IoT (IIoT) ecosystems. While this integration promises massive gains in efficiency and productivity, it simultaneously opens new and concerning cybersecurity vulnerabilities, particularly at the industrial edge.

Read more

Real-time Analytics News for the Week Ending May 17

May 17, 2025 | By Salvatore Salamone | RTInsights 

In this week’s real-time analytics news: Qlik made a number of announcements at its Qlik Connect 2025 conference.

IOTech Systems announced the launch of Edge Central 4.0, an updated version of its open edge data platform. Optimized for demanding industrial edge environments, Edge Central 4.0 is designed to accelerate the deployment and adoption of the next generation of AI-driven industrial edge solutions. The solution offers new capabilities, including Edge Alarm Service, Edge Historian Service, a New Python Application SDK and API, PostgreSQL as the default database, and more.

Read more

IOTech’s Latest Edge Central Release Powers AI-Driven Industrial Edge Solutions

May 16, 2025 | By Chantal Polsonetti | ARC Advisory Group 

ARC Advisory Group provides comprehensive coverage of IOTech’s Edge Central 4.0 launch, analyzing how the platform is specifically optimized for demanding industrial edge environments and its strategic role in accelerating the deployment and adoption of next-generation AI-driven industrial edge solutions. The coverage emphasizes IOTech’s significant contribution as a core ecosystem contributor to EdgeX Foundry’s open-source interoperability standards, while exploring the platform’s versatile applications across diverse industrial sectors, including building automation, renewable energy, manufacturing/Industry 4.0, and transportation.

Read more

IOTech Systems Unveils Game-Changing Edge Data Platform

May 15, 2025 | By Roshini Bains | Eureka Magazine 

Eureka Magazine features detailed coverage of IOTech’s Edge Central 4.0 announcement, highlighting the platform’s sophisticated design for industrial edge environments and its seamless alignment with EdgeX Foundry’s open-source interoperability standards. The article showcases IOTech’s extensive industry expertise and broad market reach, spanning critical sectors such as building automation, renewable energy, manufacturing/Industry 4.0, and transportation, while positioning the company as a key player in the industrial edge computing landscape.

Read more

The post IOTech Thought Leadership & Media Coverage appeared first on IOTech Systems.

Author: James Butcher, IOTech Product Manager & EdgeX Foundry Chairman

EdgeX 4.0 “Odesa” marks a significant step forward for the EdgeX Foundry community. Alongside key feature enhancements, this release brings measurable improvements in memory efficiency and deployment flexibility. The newly published 4.0 performance report highlights the real-world impact of the latest architectural changes, offering developers and integrators a clear view of what’s changed — and why it matters.

At IOTech, we’re proud not only to contribute to the EdgeX community but also to offer Edge Central, a commercially supported platform built on EdgeX, which incorporates these latest innovations and delivers them as a hardened, enterprise-ready product.

What’s New in EdgeX 4.0?

As a reminder, EdgeX 4.0 – released in March this year – introduced several foundational updates:

• PostgreSQL replaced Redis as the default database, delivering more robust persistence capabilities.
• Core Keeper replaced Consul, providing a lightweight configuration service.
• OpenBao replaced Vault as the default secret store, aligning with the project’s focus on open-source maintainability
•  A new Time-Based Scheduling Service, for more precise control of edge operations.
• A new Python API, to broaden accessibility to a wider community.

These changes are all aligned with the community’s goal of providing and maintaining a modular, production-ready edge platform. Read my full 4.0 release blog for more details.

If you’re looking for a fully QA tested and commercially supported version of this cutting edge technology, Edge Central already includes these upgrades — along with key features such as advanced tooling, enterprise integration capabilities, and edge management.

Key Performance Highlight

The EdgeX community gathers detailed performance metrics for each release. Given the microservices nature of the platform, users can choose exactly which services to deploy. For analysis, the community benchmarks two deployment modes of the platform:

• Full/Typical Deployment: most services, including security, are enabled.
• Minimal Deployment:  streamlined for resource-constrained environments.

In a nutshell, here’s how EdgeX 4.0 compares to the previous 3.1 (Napa) release:

Full (Typical) Deployment

• Runtime memory usage reduced by 38% (from 335 MB to 206 MB).
• Docker image footprint reduced by ~10%, now ~996 MB.
• Startup time increased slightly with full security (from 38s to 40s).

Minimal Deployment

• Startup time improved (17s from 22s).
• CPU and memory usage increased slightly (71 MB from 38 MB) – attributed mainly due to PostgreSQL’s larger baseline footprint

Latency

• API response times with the API gateway rose marginally (from ~31ms to ~37ms).
• Response times without security improved slightly (from ~2.4ms to ~2ms).
• Data latency tested with the virtual device remains stable (around ~4–6ms), with no measurable overhead from security.

All stats relate to testing performed on a Dell 3200 Edge Gateway.

What Does This Mean?

The 4.0 metrics confirm that EdgeX remains a lightweight and efficient platform for edge computing. While PostgreSQL and OpenBao bring a larger footprint to minimal setups, they offer enhanced compatibility, durability, and manageability for enterprise-scale use cases.

Overall, EdgeX is evolving into a more robust, secure, and modern framework while maintaining its edge-friendly characteristics — particularly when carefully selecting services to match deployment constraints.

For those seeking to minimize integration time or add enterprise features, Edge Central offers a fully supported version of EdgeX — optimized for production and backed by our expert team.

 From Open Source to Enterprise-Grade

While EdgeX remains a powerful open-source option, many adopters prefer a commercial implementation that reduces operational risk and speeds time to value. The microservice architecture of EdgeX makes it ideal for vendors to extend or enhance as needed.

Edge Central, built on EdgeX, leverages this flexibility — adding hardened security options, and integrations with common industrial protocols and cloud services. It’s an ideal choice for businesses looking to scale fast without compromising on performance or control.

Get the Full Report

Access the full EdgeX 4.0 performance report on the EdgeX Wiki. Whether you’re optimizing for a minimal footprint or scaling to full-featured industrial edge deployments, the latest data provides essential insights to guide your strategy.

And if you’re ready to move from experimentation to execution, Edge Central offers a proven foundation — with all the benefits of open source, delivered as a reliable commercial solution.And if you’re ready to move from experimentation to execution, Edge Central offers a proven foundation — with all the benefits of open source, delivered as a reliable commercial solution.

The post EdgeX 4.0 Performance Revealed appeared first on IOTech Systems.

San Francisco (June 12th, 2025) – IOTech, the leader in open edge computing, announces the appointment of Christopher Timmins as Chief Commercial Officer and to the senior management team. Chris will be responsible for the company’s strategic initiatives, overseeing partnership development, and solution advancement to drive IOTech’s next phase of growth across industrial automation, renewable energy, and building automation markets.

Chris brings over 25 years of proven leadership experience from Intel Corporation, where he most recently served as General Manager of Solutions, Technologies, and Planning. His extensive expertise spans ecosystem development, strategic partnership management, business development, and operations leadership, with deep specialization in emerging technologies, Internet of Things, and open-source platforms.

“Chris’s appointment represents a significant milestone for IOTech,” said Keith Steele, CEO and co-founder of IOTech. “His exceptional track record in developing strategic partnerships and building successful technology ecosystems will be invaluable as we expand our market presence. Chris’s proven ability to transform complex technology initiatives into successful business outcomes aligns perfectly with our mission to deliver the industry’s leading open edge data platform.”

During his tenure at Intel, Chris demonstrated remarkable ecosystem-building capabilities, growing participation from zero to more than 100 companies in just 36 months while managing market opportunities exceeding $500 million. His experience collaborating with Fortune 100 companies on strategic technology initiatives positions him to expand IOTech’s partner ecosystem and enterprise customer base significantly.

“I’m excited to join IOTech at such a pivotal time in the edge computing industry,” said Chris. “Throughout my career, I’ve focused on building meaningful partnerships that drive both technological innovation and sustainable business growth. IOTech’s open approach to edge computing creates tremendous opportunities to address real-world data challenges. I look forward to working with this exceptional team to expand our strategic partnerships and accelerate adoption of our edge platform across key vertical markets.”

In his new role, Chris will lead comprehensive strategic planning efforts, develop go-to-market strategies for target industries, and build partnerships that enhance IOTech’s competitive position in the rapidly expanding edge computing market.

About IOTech

IOTech Systems is a leading provider of edge computing solutions, delivering high-performance industrial IoT platforms that power intelligent operations at the edge. The company’s solutions provide seamless integration between sensors, devices, applications, and the cloud—enabling real-time data acquisition, edge analytics, and scalable management across diverse industrial environments. Built on an open-source foundation and aligned with industry standards, IOTech champions vendor neutrality and interoperability to provide customers maximum flexibility and choice. IOTech empowers these organizations to harness the full potential of their edge infrastructure. Learn more at www.iotechsys.com.

The post IOTech Appoints Proven Technology Leader Chris Timmins as Chief Commercial Officer appeared first on IOTech Systems.

Author: Andrew Foster, IOTech Product Director

We’re excited to announce the preview release of IOTech’s new Edge Alarm Service, a powerful solution designed to transform how organizations monitor and manage alarms across their edge computing environments.

As edge systems grow more complex – spanning diverse industrial devices, protocols, and operational technologies – managing alarms consistently and effectively has become a major challenge. That’s where our Alarm Service comes in: offering a unified, technology-agnostic, standards-based approach to alarm normalization, routing, and management.

The Alarm Service is built for seamless integration with IOTech’s Edge Central platform but is also fully compatible with open-source EdgeX Foundry. Thanks to its technology-agnostic design, it can also easily integrate into your own custom frameworks.

Built on Industry Standards

At its core, the Alarm Service is based on the OPC UA Alarms & Conditions standard, which itself aligns with IEC 62682, ensuring compatibility across a broad range of industrial systems. Whether working with Modbus devices, BACnet controllers, industrial PLCs, or other operational technologies, the Alarm Service can seamlessly integrate and unify your alarm management workflows.

 Watch the Demo Video

Our demo video showcases the Alarm Service monitoring data from a controller device, in this case, capturing both:

• deviation alarms (e.g., abnormal temperature readings)
• discrete alarms (e.g., a safety door opening unexpectedly)

As you’ll see, what makes this service truly powerful is its rich alarm state model — alarms can be active or inactive, acknowledged or unacknowledged, confirmed or unconfirmed, shelved, suppressed, latched, and so on.

These states allow operators to interact with alarms in a detailed, traceable way, ensuring that no critical events slip through the cracks.

Seamless API Integration

The Alarm Service is designed with integration flexibility in mind. Whether you’re building your own application or planning to use IOTech’s upcoming Alarms User Interface, you can access all alarm data and operations via a comprehensive REST API.

In the demo, we show:

• Listing all current alarms
• Acknowledging alarms with comments
• Confirming resolved conditions
• Retrieving full alarm histories

This programmatic access makes it easy to embed alarm management into your existing systems or custom dashboards.

Smart Alarm Routing to External Systems

Effective alarm management isn’t just about detection — it’s about getting the right alerts to the right people at the right time. The Alarm Service supports configurable alarm routing to various different endpoints or systems that need to be notified.

Our demo shows sending automatic Slack notifications when specific alarm conditions are met, such as:

• A moderate temperature rise triggering a medium-level alert
• A sharp, critical temperature spike triggering a high-priority alert

This flexibility ensures your team is always in the loop — whether on the shop floor, in the control room, or monitoring remotely from HQ.

Easy Configuration and Extensibility

Setting up alarms is as simple as defining them in a configuration file — from basic thresholds to complex multi-level alarm models like “high-high” critical events. With support for multiple devices and a scalable architecture, the Alarm Service is ready to grow alongside your edge deployment.

Learn More

We believe the Alarm Service is a game-changer for edge alarm management — simplifying operations, improving responsiveness, and giving organizations the confidence to scale their edge systems securely.

To explore the Alarm Service in more detail, visit our Alarm Service page or schedule a one-on-one session with our team. We’re happy to walk you through use cases, integrations, and custom demos tailored to your needs.

Thanks for reading — and stay tuned for updates on the upcoming Alarms UI and even more features!

The post Introducing IOTech’s Alarm Service appeared first on IOTech Systems.