Energy Efficiency Optimization in Clustered Wireless Sensor Networks via Machine Learning Algorithms

Abstract

In recent years, wireless sensor networks (WSNs) have had a significant role, and the era has focused most importantly on the technical limitations with respect to sensor networks. WSNs are very helpful in the worldwide distribution, for example, for environmental monitoring purposes. WSNs are used to collect data from and make inferences about the environments or objects they are sensing. Individual network nodes have the burden of formulating tasks as no intrusion detection can be implemented because it’s a wireless network. The nodes of a WSN are sensor devices and count on battery power to function. With respect to minimum power resources, each node has a task to do, which includes methods for minimize energy consumption. In WSNs, clustering nodes can minimize WSN power consumption. The WSN nodes are parted into clusters. A cluster head is one where one node from each cluster is selected. This helped with reducing power consumption. The cluster head will change periodically due to the diminishing power of the cluster head. Many routing algorithms are designed. Hierarchical routing protocols are involved in reducing energy consumption. It is not possible to transmit information to a base station for the purpose of processing because of bandwidth and energy-related limitations. Therefore, machine learning (ML) algorithms are implemented in WSNs. They help decrease data communications and make use of the features of WSNs. The chapter aims at showing that ML is the practical solution to all mentioned constraints.