Residual Energy Based Cluster-head Selection in WSNs for IoT Application

TL;DR

This paper proposes a residual energy-based cluster head selection algorithm for wireless sensor networks in IoT applications, significantly improving network lifetime, throughput, and energy efficiency compared to LEACH.

Contribution

It introduces a novel cluster head election scheme that considers residual energy and optimal cluster head count, enhancing energy conservation in IoT sensor networks.

Findings

60% increase in throughput

66% extension of network lifetime

64% higher residual energy

Abstract

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.