In the era of Industry 4.0 (I4.0), Cyber Physical Production Systems (CPPS) and upcoming industrial transformations, there's a great impulse for smarter, more connected, and adaptable industries. To support this shift, our industrial devices need to be upgraded. They should not only do their usual tasks reliably but also support new technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins (DT), etc. seamlessly. This is where cognition in devices becomes significant. This paper showcases an innovative cognitive system design for edge devices for control, drawing inspiration from the well-established concept of cognitive control. This approach highlights the symphonious existence of conscious (controlled) and unconscious (automatic) processing. The system architecture demonstrates the concurrent processing of real-time tasks, like control loops, field devices, etc., and non-real-time tasks such as AI, ML, Neural Network (NN) models, DT models, etc. within the edge device. This corresponds to the unconscious and conscious cognitive functions in human beings. This paper outlines the characteristics of such cognitive devices. The potential technologies that can help in achieving these characteristics like virtualization, multi-core edge devices, concurrency, etc. have also been explored. Additionally, a proof-of-concept demonstration use case has been presented that exhibits the implementation of the Open Cognitive Control Systems (OCCS) architecture in edge devices. This work sets a stepping stone towards making industrial devices smarter.

The technology readiness levels of cloud infrastructure and edge devices have increased significantly in recent years. This means that companies now have a growing number of computing environments at their disposal that could be suitable for the computing and control tasks involved in their production processes. Consequently, the need arises to select the best computing environment based on objective criteria and metrics. In order to create the basis for such a decision-making process, first an overview of the definitions and a comparison between edge and cloud environments is provided. Then a decision-making framework is derived that aims to facilitate this selection process. This is accomplished by providing a set of criteria that can be detailed to analyze a given use case. This analysis is then represented in a custom radar chart, which offers decision-makers a compact but meaningful representation of the decision space. Finally, the limits and future potentials of such a decision-making framework are discussed.

With the advent of the fourth industrial revolution i.e. Industry 4.0, plants and factories are becoming smarter and interconnected. The transitions demand vertical integration and seamless connectivity. For this purpose, there is a need for semantic communication between various devices including the heavily resource-constrained field devices. To address this, a real-time capable hardware-based implementation of a well-established semantic communication protocol, i.e. OPC Unified Architecture was designed and developed. This chip-based implementation is power-efficient and compact, making it suitable for the field level. The chip was analyzed and incorporated in a demonstrator as a proof of concept of its integration at field level in a plant module of the process industry. Various opportunities are also examined where the chip could be utilized to deliver benefits to existing and future technologies.

Industrial plants suffer from a high degree of complexity and incompatibility in their communication infrastructure, caused by a wild mix of proprietary technologies. This prevents transformation toward Industry 4.0 and the Industrial Internet of Things. Open platform communications unified architecture (OPC UA) is a standardized protocol that addresses these problems with uniform and semantic communication across all levels of the hierarchy. However, its adoption in embedded field devices, such as sensors and actuators, is still lacking due to prohibitive memory and power requirements of software implementations. We have developed a dedicated hardware engine that offloads processing of the OPC UA protocol and enables the realization of compact and low-power field devices with OPC UA support. As part of a proof-of-concept embedded system, we have implemented this engine in a 22-nm FDSOI technology, representing the first ASIC implementation of an OPC UA server. We measured performance, power consumption, and memory footprint of our test chip and compared it with a software implementation based on open62541 and a Raspberry Pi 2B. Our OPC UA hardware engine is 50 times more energy efficient and only requires 36 KiB of memory. The complete system consumes only 24 mW under full load, making it suitable for low-power embedded applications.