Category: Know How

What exactly is Windows IoT Enterprise?

Windows IoT Enterprise is an operating system for industrial applications and embedded PCs. It is suitable for all devices on which desktop apps and Win32 applications are to run. Windows IoT Enterprise has all the functions of the Windows Professional versions and also has so-called embedded lockdown features. These are used to secure the device and protect it from unwanted attacks. They also offer various branding options and can, for example, make the Windows operating system completely invisible behind the application. IoT also offers long-term support through the LTSC versions.
The current versions are Windows 10 IoT Enterprise LTSC 2021 and Windows 11 IoT Enterprise LTSC 2024.

What advantages does Windows 10 IoT Enterprise offer?

Long-term stability

A major advantage of Windows IoT LTSC is the long-term support, consisting of security and quality updates over 10 years, no disruptive function updates that can destabilize systems and high compatibility for existing software.

This is crucial for devices that run for many years, such as production systems, kiosk systems or ATMs. Function updates only come every 2-3 years and are carefully tested to minimize risks.

Customization options and embedded features

Windows IoT Enterprise enables extensive customization:

• Unbranded Boot: Hide boot logo or error messages
• Shell Launcher: Open an application automatically after startup
• Assigned Access: Configure devices for a single purpose

These lockdown mechanisms not only ensure user-friendliness, but also increase the security and stability of the system.

Multilingualism and internationality

Several language packages can be installed with MUI (Multilingual User Interface). This facilitates use in international projects and enables flexible language selection per user or location, as well as over 30 language packages (including regional variants).

License advantages

Another advantage is Windows IoT licensing. It is specially optimized for embedded systems, which saves costs and simplifies use on different devices. Devices can be licensed individually without losing features.

Who is Windows IoT Enterprise suitable for?

In principle, Windows 10 or 11 IoT is suitable for all industrial applications: From kiosk systems, POS devices and ATMs to production systems and industrial control systems to digital signage players or medical devices – to name just a few examples.

If you are not sure whether Windows IoT Enterprise is right for you and your application, we can provide you with a free trial of the desired PC with a Windows IoT test version at any time.

Edge AI on the upswing: market trends, growth and profitability

As Gartner predicts, the majority of all corporate data processing now takes place outside central data centers, a clear indication of the growing importance of distributed intelligence at the edge of the network.

At the same time, market research is forecasting strong growth in the edge AI market. Studies by Fortune Business Insights and other analyses show that the market for AI-based edge solutions is expanding at high growth rates. Driven by the increasing demand for real-time processing, Industry 4.0 use cases such as autonomous production lines and predictive maintenance.

These facts are bringing the cost-benefit ratio of edge AI into the focus of IT specialists and management. Where is it worth running AI workloads and how quickly will investments in special hardware, such as mini PCs, pay off?

Cost components of edge and cloud

The economic evaluation of Edge AI often focuses on the comparison between investment costs and operating costs:

*Costs incurred for the transfer of data volumes via networks (Internet, cloud services, hosting).

As a result, analyses show that local data processing at the edge can lead to a significant reduction in total operating costs in the long term. Especially where high volumes of data need to be continuously generated and analyzed.

ROI drivers: strategic advantages of edge AI

1. Latency & real-time response
   One of the biggest advantages of edge AI implementations is the reduced latency. Applications in manufacturing, robotics or autonomous systems require response times in the millisecond range. If data has to be sent to the cloud, delays occur that are intolerable for cycle times or safety functions. Local systems such as mini PCs process sensor data directly where it is generated, often delivering decision results in under 50 ms.
   
   
2. Predictive maintenance – less downtime, more productivity
   Predictive maintenance is one of the most important Industry 4.0 applications and is used by many companies to avoid unplanned downtime. Bitkom studies show that companies are already increasingly relying on AI-supported analyses to monitor machine statuses and proactively schedule maintenance, for example.
   Even if there are different figures for savings, the economic effect is clear: predictive algorithms can significantly reduce downtimes and maintenance costs, and even more efficiently if the analysis is carried out directly on edge systems instead of in the cloud.
   
   
3. Data security & data sovereignty
   An often underestimated advantage is control over sensitive company data. Edge AI users minimize the need to transfer raw data to third-party cloud infrastructures, which is a plus for data protection, compliance and data sovereignty, especially in regulated industries. Local data processing limits potential attack surfaces and facilitates compliance with company guidelines.

Using edge AI sustainably with the right industrial PCs

A good edge AI cost-benefit ratio is not only achieved through AI software, but also through the choice of suitable hardware. After all, investments only pay off quickly and sustainably if the edge infrastructure used is powerful, scalable and economical. In practice, our spo-comm solutions show how this balancing act can be achieved – from entry-level to sophisticated AI scenarios.

CORE 5 Ultra – Compact entry-level AI system

The CORE5 Ultra represents a robust and compact entry into industrial edge AI. With a modern Intel® Core™ Ultra processor and integrated NPU, this mini PC is ideal for basic inference and automation tasks directly at the data source. It processes sensor data locally and energy-efficiently without a permanent cloud connection and with minimal running costs.

NOVA R680E – Industrial PC with Nvidia GPU

The NOVA R680E offers the necessary performance and expandability for more demanding AI workloads, for example in image processing, predictive maintenance or complex production analyses. Thanks to more powerful CPU options and PCIe expansion options (e.g. GPU accelerator), this industrial PC is able to run compute-intensive models directly at the edge without any data traffic to the cloud and the associated running costs.

Thanks to our spo-comm hardware qualities, strategic advantages such as low latency times, higher data security and noticeable cost savings can be realized. These are all key components of a positive edge AI cost-benefit ratio and should not be neglected. In addition, local processing ensures that companies can react more quickly to production or quality deviations and therefore work more productively.

With the combination of technically mature systems such as the CORE 5 Ultra and the NOVA R680E in combination with a well thought-out Edge AI concept, companies are relying on a basis that is not only economically convincing, but also gives them the flexibility to implement future AI projects efficiently and with our support.

What is behind predictive maintenance?

While traditional maintenance involves checking systems at fixed intervals, predictive maintenance is one step ahead. Data from the machine itself, such as temperature, vibration or power consumption, is continuously recorded and analyzed. With the help of algorithms and AI models, conclusions can be drawn as to when a component is likely to fail. This means that maintenance can be carried out exactly when it is really necessary, i.e. not too early and not too late.

According to the Industry of Things, predictive maintenance describes a data-based maintenance approach in which sensor values are analyzed and failure probabilities are calculated on this basis in order to avoid unplanned downtimes.

How does this work in practice?

The basic principle is simple: machines are equipped with sensors that continuously supply data. This data is collected, processed and analyzed either directly in the machine or via an edge system.

The procedure is usually as follows:

1. Data acquisition: Sensors measure vibrations, temperatures, voltages, etc.
2. Data transmission: The information is passed on via secure networks or IoT gateways.
3. Analysis: AI models or machine learning algorithms recognize patterns, anomalies or trends.
4. Forecast: If the system detects deviations from the normal state, a maintenance requirement is reported in good time before a failure occurs.

For these processes to function smoothly, reliable hardware is needed at the edge. After all, not all data can or should always be sent to the cloud – low latency times, data protection and real-time requirements often make local processing necessary.

This is where mini PCs like our CORE 5 Ultra come into play: thanks to the Intel® Core™ Ultra 5 125U processor (with 2 Performance Cores & 10 Efficiency Cores) and integrated NPU with 11 TOPS, it can run AI models directly on the spot. This allows sensor data to be analyzed in real time and potential faults to be detected before they occur.

Advantages for industry and users

The advantages of predictive maintenance are obvious:

• Less downtime: Unplanned downtimes are drastically reduced
• Lower maintenance costs: components are only serviced or replaced when necessary
• Longer service life: machines are protected because wear is detected at an early stage
• More efficient planning: maintenance work can be carried out in a targeted and plannable manner
• More transparency: companies gain valuable insights into the condition of their systems

Predictive maintenance can bring enormous efficiency gains, particularly in sectors such as production, mechanical engineering, energy and transportation. This makes it a central component of modern Industry 4.0 strategies.

What is needed for successful implementation?

Many companies would like to introduce predictive maintenance, but don’t know exactly where to start.

Fraunhofer IESE identifies three key success factors: good data, suitable models and well thought-out integration into existing processes.

1. Data basis: A reliable model cannot be created without high-quality and sufficient historical data.
2. Analysis models: AI or machine learning algorithms must be continuously fed with new data and validated.
3. Integration: The results must be visualized clearly and integrated into the maintenance process, ideally automatically via existing systems.

For predictive maintenance to make economic sense, companies should start small. For example, with a pilot project on a critical machine, clearly defined goals and a scalable infrastructure. The system can then be transferred to other systems step by step.

Hardware at the edge – the key to real time

A key success factor is the right hardware platform. This is because predictive maintenance applications require stable systems that run continuously, even in harsh industrial environments.

Our spo-comm industrial PCs are just right for this! They offer high computing power in the smallest of spaces, are temperature-resistant and durable in continuous operation. With integrated AI functions, for example via NUPs or optional accelerator cards, they can process sensor data directly without having to go via the cloud.

This saves bandwidth, reduces latency and makes the solution secure and independent – ideal for edge AI scenarios in industry.

Challenges and limits

As great as the advantages are, they do not come without challenges:

• Data quality: Sensor errors or incomplete data make it difficult to make accurate forecasts.
• Complexity: Different machine types and operating conditions require flexible models.
• Know-how: Implementation requires experience in data analysis, AI and industrial IT.
• Costs: Setting up a suitable infrastructure can be costly at the beginning, but in the long term it becomes automated due to less downtime.

However, companies that take a strategic approach to this issue and adapt their processes accordingly will quickly benefit from more stable workflows and lower maintenance costs.

From reaction to prevention

Predictive maintenance is more than just a buzzword, it is a real game changer for the industry. The use of modern sensor technology, AI and edge computing is turning reactive maintenance into an intelligent, data-driven process. Systems are not only monitored, but increasingly understand themselves when they need support.

With robust and powerful mini PCs such as the CORE 5 Ultra from spo-comm, such systems can be implemented reliably – right where the data is generated.