The era of extensive digitalization marked by the fourth industrial revolution has ushered in significant advancements in technologies like automation, artificial intelligence (AI), and the Internet of Things (IoT). These innovations are revolutionizing smart industries like manufacturing, smart energy systems (SESs), and the automotive industry. Industry 4.0 (I4.0) and the subsequent Industry 5.0 (I5.0) emerged as comprehensive representations of the physical world in the information world, with goals to establish smart factories and promote human-machine coexistence. However, the implementation of I4.0 and I5.0 applications faces challenges related to engineering efficiency, interoperability, and efficient service discovery and binding.

This thesis seeks to address these challenges by exploring potential strategies to develop an efficient System of Systems (SoS) that comprises individual, autonomous systems collaborating to achieve a shared goal. This research examines methods to enhance the efficacy of SoS by refining its engineering procedures, promoting interoperability between standardized protocols and heterogeneous systems, and employing dynamic adaptation mechanisms. It aims to achieve automatic service discovery and interoperability between diverse industrial standards and systems across the different domains within the smart industry by integrating the Eclipse Arrowhead Framework. This IoT framework facilitates secure and seamless communication and collaboration among devices, machines, and systems.

Moreover, this work delves into saving energy consumption in distributed SoS environments. This is achieved through the Demand Response (DR) mechanism in SESs combined with the Eclipse Arrowhead framework. In addition, the thesis examines challenges in automotive testing, specifically in Vehicle-in-the-Loop (VIL) testing environments, which are distributed SoS systems requiring efficient communication, diverse hardware and system interoperability, realistic simulation, and scalable system integration with minimal cost and resource demand. The research also explores flexible methods for integrating heterogeneous environment models into VIL frameworks and proposes a standardized service-oriented vehicle data communication framework to improve interoperability, operational efficiency, and scalability.

The overarching objective is to pave the way for flexible production processes characterized by minimal resource waste, optimized energy consumption, and sustainable solutions. Through this endeavor, the thesis contributes to shaping a more efficient, interoperable, and sustainable smart industrial landscape in the context of Industry 4.0 and beyond.