Soft Computing Techniques for Dependable Cyber-Physical Systems

Abstract

Cyber-physical systems (CPSs) were envisaged as a way to manipulate the objects in the physical world through computer intelligence. This is usually done by providing a communication bridge between actuation and computing elements. This sought after control is hampered not only by the unavoidable certainty found in the physical world but also by the limitations of contemporary communication networks. 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 manage the unreliability of CPS by using techniques, including fuzzy systems, neural networks, evolutionary computation, probabilistic reasoning, and rough sets. 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 also identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey of the use of various soft computing techniques for making CPS dependable. This paper is timely due to the increasingly central role that CPSs are beginning to play in modern societies and the need to leverage all the relevant methodologies and tools (such as those provided by soft computing) for the development of highly dependable CPS.

Keywords

• Cyber-physical-systems
• soft computing
• machine learning
• smart systems
• communication networks
• dependability