Cluster-head selection in WSNs using modified MADM approach by considering conflicting parameters for IoT applications

Abstract

Internet of Things (IoT) devices with limited energy and storage resources can access sensing services from wireless sensor networks (WSNs), which are collections of specialized transducers. Power consumption becomes one of the most important design considerations in WSN because battery replacement or recharge in sensor nodes is almost impossible. For the energy-constrained network, clustering algorithms are crucial for power conservation. By carefully balancing the network’s demand, a cluster head (CH) can lower energy usage and extend lifespan. The primary topic of this study is an effective CH election mechanism that alternates the CH position among nodes with higher energy levels than the others. In order to accomplish balanced load clustering in WSN, the method in this study takes into account a total of seven such factors and coordinates to select the optimal CHs from among them. To choose the set of CHs that can most effectively meet the coordination criterion from the available possibilities, modified MADM techniques are used. The improved version performs better, according to simulation analysis.