Optimizing WSNs for CPS Using Machine Learning Techniques

Abstract

Progress in wireless systems has enabled the creation of low-cost, ergonomic, multi-functional, miniature sensing devices. These devices come together in large numbers creating wireless sensor networks (WSNs), which serve for sensing, collecting, analyzing, and sending detected data to a base station. Problems arise, however, due to the limitations of sensor nodes (SNs), incorrect aggregation of data, redundant and similar data problems, data security and reliability, and some others related to WSN topology. This chapter proposes a novel method for solving WSNs problems to improve cyber-physical systems (CPS). As WSN is of increasing interest in CPSs, the authors put forward an approach for reconstructing WSNs. For traditional methods are not able to cope with such problems, this study takes up rendering WSNs more functional through artificial intelligence (AI) techniques which are considered to develop smart SNs through “intelligent computing,” “deep learning,” “self-learning,” and “swarm learning” ability on the network to improve functionality, utility, and survivability of WSNs.