Soft Computing Techniques for Dependable Cyber-Physical Systems

TL;DR

This paper reviews how soft computing techniques like fuzzy systems, neural networks, and evolutionary algorithms can enhance the dependability of Cyber-Physical Systems by addressing communication and physical uncertainties.

Contribution

It offers a comprehensive survey of soft computing methods applied to improve CPS dependability, including case studies and future research directions.

Findings

Soft computing techniques can mitigate communication uncertainties.

Neural networks and fuzzy systems enhance CPS reliability.

Future research should focus on integrated soft computing approaches.

Abstract

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.