Signal and Data Processing Techniques for Industrial Cyber-Physical Systems

Recent advances in computing and communications have raised a constantly increasing challenge of modernizing and decentralizing industrial processes, by introducing dynamic architectures of cyber-physical systems (CPS). The resulting industrial CPS (iCPS) exploit their inherent relationship with wireless sensor networks (WSN) for providing cost-effective, scalable and easily-deployed solutions in industrial spaces, whilst formulating new paradigms for industrial data acquisition and control. Employing iCPS in industrial processes poses characteristics which are not dominant in conventional monitoring scenarios. In specific, the deployment of the WSN components is highly coupled to the objectives of the industrial process, thus introducing logical, along with spatial and temporal, correlations between data streams originated by different positions. Moreover, typical WSN imperfections, such as limited bandwidth, computational complexity, and lifetime, should be treated as inseparable part the processes responsible for minimizing the WSN maintenance efforts. Finally, the quality of sensing is strongly coupled to the objectives and the performance of the industrial control laws, necessitating for intensified reliability and robustness in signal processing and data acquisition, as the complexity of the industrial process increases. The objective of this book chapter is to present a framework of signal and data processing for treating different layers of information abstraction in an iCPS. By taking into account the limitations and imperfections of WSN employed for iCPS, we will focus on three complementary, yet equally important aspects: (a) signal processing-driven performance optimization for industrial WSN; (b) in-network signal processing techniques for distributed field estimation; (c) high-level data management and analysis for detecting abnormal behavior in the recorded iCPS data. The developed algorithms aim at formulating an integrated framework for signal and data processing in iCPS. With a strong emphasis on providing the essential theoretical background, the efficacy of the resulting framework will be ultimately evaluated in different aspects of real-life iCPS, designed for the autonomous monitoring of water treatment plants. It is anticipated that the methods herein presented and the accompanying discussions on the obtained results, will yield novel directions for iCPS standardization.