Soft Computing Techniques for Dependable Cyber-Physical Systems
This is an incremental review paper that addresses dependability issues for researchers and practitioners in CPS, focusing on summarizing existing methods rather than introducing novel solutions.
This paper tackles the problem of vulnerabilities and unreliability in Cyber-Physical Systems (CPS) by reviewing soft computing techniques, such as fuzzy systems and neural networks, to enhance dependability, but it does not present new experimental results or concrete numbers.
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable.