On Evaluating Energy Efficient Algorithms for Internet of Things Networks

Abstract

Internet of Things (IoT) is a new paradigm pulling great interest in the modern wireless Communications domain. However, in some scenarios, the performance of IoT network is limited by energy constrained devices. For improve the energy efficiency of IoT network, researchers have suggested different approaches based on clustering, where the cluster heads (CHs) selection has significant effect on the network performance. In this paper, we review and compare different energy-efficient clustering protocols for IoT network, i.e Low-Energy Adaptive Clustering Hierarchy $(LEACH)$, Particle Swarm Optimization $(PSO)$ and Genetic Algorithms $(GA)$. Besides we investigate the extent of their effectiveness to prolong network lifetime. The results obtained from the implementation in MATLAB show that GA performs better than PSO and LEACH in improving energy consumption and also increasing the number of live nodes within different rounds.