Industrial devices must operate safely, reliably, and with minimal maintenance over long lifecycles. As connectivity increases, cybersecurity requirements also grow, since external threats can directly impact devices. At the same time, remote update and remote maintenance capabilities are becoming a baseline requirement in modern factory environments.
As a result, selecting the best-fitting or right edge device has become more complex and depends on additional criteria such as robustness against harsh environmental conditions, low power consumption, efficient thermal management, and high I/O bandwidth for deterministic real-time computing. These factors directly affect operation, maintenance, and replacement over the entire device lifecycle.
However, modern edge architectures are no longer defined by hardware alone. Increasingly, long-term success depends on how well hardware, AI capabilities, device management, and lifecycle orchestration are integrated into a complete ecosystem. Therefore, system designers should define the relevant selection criteria early in the project. This blog provides a design checklist to support informed decision-making and long-term project success.
Physical Integrity – Surviving Industrial Environments
Deploying devices in industrial environments places specific demands on system design, including:
- Extended operating temperature range, typically from −20°C to +85°C
- High protection against dust and water (IP65 or higher)
- Robustness against shock and vibration
- EMC-compliant design of shielding, grounding, and I/O filtering
- Galvanic isolation or a wide DC input range to handle power fluctuations
These requirements are closely linked to device size, performance, and thermal management. Long-term ease-of-installation and maintainability must also be considered early on. Fanless edge computing systems are particularly well suited here as sealed enclosures with integrated heat dissipation enable reliable operation in harsh conditions while minimizing maintenance efforts.
I/O and Connectivity – Bridging the Physical-Digital Divide
Depending on the application, system designers must precisely define requirements for I/O interfaces and network connectivity. Key factors include the number of interfaces, bandwidth, range, and latency.
For sensing and control, interfaces mainly differ in distance, data rate, and real-time capability:
- MIPI-CSI: high bandwidth, low latency, short internal distances
- Gigabit Ethernet: long cable lengths and flexible integration, but higher latency
- Serial interfaces: robust and deterministic, suitable for low data rates
In network communications, Ethernet and Wi-Fi are most common:
- Ethernet: high reliability, deterministic behavior, and industrial real-time extensions
- Wi-Fi: flexible installation, more susceptible to interference, less deterministic
- Other networks like BT, LoRA, or ZigBee: short- and long-range technologies available, low power consumption, low data rates, BT requires licensing of the product with BT SIG
Storage and expansion interfaces mainly differ in performance and scalability:
- SATA: reliable, but bandwidth-limited
- M.2 and mPCIe: high data rates, flexible expansion
- USB: universal connectivity, high bandwidth, hot-plug capability, multiple speeds and complexity of implementation between USB4, USB3, and USB2
For video, display, and audio applications, resolution, latency, and signal integrity determine interface selection:
- DisplayPort and HDMI: high resolutions and frame rates
- LVDS and eDP: for internal displays with low EMC emissions
- High-Definition Audio (HDA), SoundWire, and I2S: low-latency digital audio transmission, sound quality vs. complexity trade-offs
By clearly specifying I/O and networking requirements, developers can select an edge computing approach that best matches both application needs and long-term requirements.
The Software Stack – OS, SDKs, and AI Optimization
Reliable operation of industrial edge applications requires a mature software foundation. The choice of operating system (OS), middleware, and frameworks determines the portability of application-specific functions. To avoid limiting future hardware changes or scaling, the software architecture should be decided early to promote flexibility and reuse.
Several aspects must be considered when selecting the software platform:
- OS: Windows or Linux, prebuilt images vs. custom distributions
- Containerization: enables modular updates without downtime
- SDKs: accelerate deployment of AI models such as OpenVINO on heterogeneous hardware
- Edge AI strategy: from inference-only to training and continuous updates at the edge
Low-latency edge AI inference enables real-time processing of diverse workloads directly at the edge, such as visual quality inspection, anomaly detection, or predictive maintenance. Local data processing keeps sensitive information within the system, reducing latency while improving data privacy and security.
Still, inference performance alone is not sufficient. Industrial edge AI requires orchestration, version control of AI models, workload management, and seamless integration between development and deployment environments.
With the Clea ecosystem, SECO provides an integrated software and service platform that combines edge AI, device management, and industrial internet of things (IIoT) data orchestration, enabling fast deployment of scalable and secure industrial applications. Through SECO’s Application Hub, developers can leverage AI algorithms and connect with Clea to deploy AI applications, distribute updates, monitor behavior in the field, and continuously optimize models across device fleets.
Lifecycle and Fleet – Orchestration and Long-Term Security
Clea also offers comprehensive fleet management capabilities for industrial edge devices, enabling efficient and secure operation of distributed systems:
- Collection of application and operational data—to analyze and optimize workflows
- Monitoring of device performance—allowing early detection of maintenance needs
- Deployment of security patches and firmware-over-the-air (FOTA) updates—keeping systems up to date without requiring on-site service intervention
However, Clea’s role goes further than classical fleet management: it acts as an orchestration layer that links AI workloads, hardware resources, and operational intelligence across the entire lifecycle. Considering regulations like the EU Cyber Resilience Act (CRA), continuous security management throughout the entire lifecycle is essential. Security by design must be implemented from the start as responsibility rests with the manufacturer.
Within this context, Clea enables centralized policy enforcement, secure update mechanisms, and continuous vulnerability management across distributed edge systems—supporting compliance while reducing operational complexity. Moreover, fleet management platforms provide valuable insight into the condition, usage, and security of distributed devices and are therefore a key enabler for reliable and secure operation throughout the entire lifecycle.
Edge Computing Hardware – Different Strategies for Different Needs
Hardware selection is a constant trade-off between fast development, easy integration, and long-term maintainability. There is no one-size-fits-all solution; priorities must be balanced on a project-by-project basis. Existing production infrastructure strongly influences application functionality and lifespan. While peak performance is primarily defined by the processor, real-world capability is significantly affected by environmental conditions and the chosen hardware form-factor.
At the same time, hardware decisions must now anticipate AI deployment strategies and fleet orchestration requirements. Modern computing architectures require support for heterogeneous workloads, hardware acceleration, and secure remote lifecycle management.
| Feature | System-on-Modules (SOMs) | Single-Board Computers (SBCs) | Industrial Edge PCs |
|---|
| Design flexibility: form factor, interfaces, expandability | Very high: custom carrier boards allow maximum flexibility | Medium: fixed form factor and interfaces limit expandability | Low: closed systems with fixed interfaces |
| Upgrade and repairability | High: module can be replaced or upgraded without carrier or full product redesign | Medium: board-level replacement, limited long-term availability | Low to medium: upgrades often require full device replacement |
| Time to market and development cost | Higher initial design effort, long-term cost benefits | Fast availability, low entry cost | Ready to use, minimal development effort |
| Certification effort | Initial effort comparable to SBC. Advantage in recertification if carrier design remains unchanged | Similar initial effort to SOM-based systems. Recertification may require broader revalidation if the board is changed | System typically delivered pre-certified at product level, minimal certification effort for integrator |
| Ideal industrial applications | Product families with long lifecycles and differentiated models | Prototypes, small series, evaluation and pilot projects | Retrofit, brownfield installations, fast deployment |
| OS support | Supports custom Linux, Yocto, Windows IoT depending on integration | Typically Linux-based, with some Windows IoT support | Broad OS support incl. Windows IoT, Linux, virtualization |
| IoT enablement | High potential, but requires integration at the carrier/system level | Basic connectivity available, limited orchestration out of the box | Often ready for cloud/IoT integration and remote management |
| AI enablement | Dependent on selected SoC and accelerator integration (CPU, GPU, NPU) | Limited by board-level resources | Supports discrete accelerators, GPUs, and AI-optimized CPUs |
SECO offers a broad range of industrial-grade commercial off-the-shelf (COTS) solutions covering the entire edge computing spectrum:
- The SOM-SMARC-ASL integrates Intel Atom® x7000RE Series processors (formerly known as Amston Lake) and is based on the compact and energy-efficient SMARC form factor, ideal for scalable, fanless designs.
- The SOM-COMe-BT6-MTL, based on Intel’s Core Ultra processor family (formerly known as Meteor Lake), which incorporates hardware acceleration for AI workloads, delivers high compute performance and I/O flexibility as a powerful COM Express module for demanding industrial applications.
- The SBC-pITX-ASL integrates Intel Atom® x7000RE Series processors and provides a robust Pico-ITX single-board computer for fast implementation and space-constrained systems.
- The Palladio 500 RPL complements the portfolio as a fully integrated 13th generation Core i microprocessor (formerly known as Raptor Lake) industrial edge PC that is ready for immediate deployment and particularly well suited for retrofit and brownfield scenarios.
Together, these platforms enable short development cycles, industrial reliability, and long-term availability. Furthermore, SECO’s experts support system designers early in the decision-making process to promote long lifecycles and ensure maintainability of the application.
Conclusion
Environmental robustness, I/O, connectivity, performance and scalability form the technical foundation of industrial edge applications, which is largely defined by hardware choice. Nevertheless, long-term competitiveness is increasingly dependent on selecting the right software architecture and fleet management ecosystem to support this decision.
An architecture that combines edge AI acceleration, secure device management, application lifecycle orchestration, and scalable deployment capabilities ensures that today’s design decisions remain viable tomorrow. SECO addresses all these requirements with a broad portfolio of industrial hardware solutions, complemented by deep expertise in software, edge AI, and fleet management ecosystems such as Clea.
Contact SECO to identify the right Intel-based edge computing platform for your application and ensure long-term project success.