Why Are Embedded Systems the Intelligence in Smart IoT Ecosystems

Author photo: Dick Slansky
By Dick Slansky


Industrial Internet of Things (IIoT) solutions have been emerging at a much faster rate than initially forecast.  This is related to the digital transformation of industry, business, and society at large. Digital transformation increases the amount of data generated and Embedded Systemscommunicated by several orders of magnitude. One of the sources of data expected to grow significantly is from embedded intelligence; the smart component of connected IoT ecosystems.

Significant milestones in digital transformation over the last few decades include: analog telephony going digital, television becoming digital, and digital camera technology replacing film. The next major milestone will be the migration of analog functions that monitor (sensors) and manage the physical world to digital functions involving data communications and software telemetry. The development of embedded intelligence in sensors and edge devices represents the “smart” aspect of IoT.

Embedded systems have been an integral component of systems engineering for many years. Industries such as aerospace, automotive, machinery, and industrial controls depend on embedded software to provide the functionality that enable products to perform more complex tasks to help improve operational performance. With the advent of IoT, and smart connected ecosystems, the number of devices within these ecosystems will explode, with smart sensors numbering in the billions, and intelligent systems in the millions. This will have a profound effect on the embedded systems market and the providers of both software and hardware that will supply this market.

Embedded Systems and Intelligence at the Edge

Embedded systems target a specific device, machine, or product with embedded software that enhances the functionality of the targeted device; often independent of external systems. Typically, a computer chip for special-purpose computing is embedded into the targeted device.  This embedded chip will include a real-time operating system (RTOS) and specific applications. The intelligent device uses this embedded microprocessor and software to process data and derive actionable information.  This embedded intelligence can be designed for specific tasks and optimized to reduce the size and cost of the device, machine, or product and Embedded Systemsincrease its reliability and performance.

Since intelligent sensors are a component of IIoT, more intelligence will be required at the edge for the physical devices, machines, and equipment that manufacturing OEMs need to run production systems and provide service in the field. These physical devices and sensors are indeed the “things” in the IIoT that will provide the data that drives the function of the connected ecosystems.  In many respects, they are at the edge of the “digital thread.”

While industrial production, asset management, and field service applications represent one of the largest sources of Big Data today, much of this data is unstructured.  It must be transformed into meaningful and actionable information before it can be applied to areas like condition monitoring, predictive analytics, and operational intelligence.

As we move up the IIoT stack from the edge devices to connectivity, to edge computing, the data are transformed through data element analytics, aggregation, and normalization. To implement concepts such as the digital twin and apply predictive and prescriptive analytics, it will become necessary to process more of the unstructured data into actionable information at the edge device.  This means that smart systems are going to become an order of magnitude smarter than they are now. Embedded intelligence will be the key to the next generation of smarter edge devices in industrial environments.

The IIoT and the ecosystems that emerge, will provide platforms for devices to generate and share far greater quantities of data than ever before to enable more sophisticated control and management of processes, machinery, and maintenance schedules. Traditional data gathering approaches such as SCADA, in which passive sensors channel raw data back to a central controller, are already giving way to IIoT solutions that can offer faster response time, more efficient data gathering capacity, and Big Data-enabled services, such as predictive maintenance and autonomous self-optimization.

Advanced Analytics Require Smarter Edge Devices

Analytics performed on site in production systems or in the Cloud can identify trends and patterns that human operators or industrial Embedded Systemsanalysts are often unable to detect. Smarter edge devices can provide information gleaned from raw unstructured data aggregated and analyzed at the device level and then accessed by the analytics engines to support best practices and optimized processes.

Embedded intelligence whether in the form of smart sensors or designed into the product, machine, equipment, or asset will be a critical component of the analytics process.   Even though much advanced analytics is being performed in the Cloud, the effectiveness of the digital twin concept largely depends on the degree of intelligence available at the edge.


Embedded systems technology, which has been around for decades, has steadily advanced in terms of applications and sophistication, making it vital to a wide range of industries.  However, the embedded systems market is poised to expand significantly with the proliferation of IIoT and widespread digital transformation taking place across industry. Manufacturers that want to be a part of this transformation and understand the business value of the digital enterprise and smart connected ecosystems enabled by IIoT also need to understand the importance of established embedded systems technology to this smart connected world.

Several sessions at the upcoming ARC Industry Forum in Orlando, Florida, will cover IIoT, digital transformation, predictive analytics, and related topics.  If you would like to learn what your peers are doing in these areas and participate in the conversation, please plan on joining us at the Forum.  


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Keywords: Intelligent Devices, IIoT, IoT, Digital Transformation, Smart Edge Devices, Analytics, Embedded Analytics, ARC Advisory Group.

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