Metaheuristic Load-Balancing-Based Clustering Technique in Wireless Sensor Networks

Abstract

The resource-constrained nature of wireless sensor networks engenders the development of energy-efficient network operations. To mitigate the prime concern of developing an energy-efficient network, clustering of the nodes has emerged as a very effective tool. If executed intelligently, clustering can not only help in obtaining even load distribution among the network nodes but also help in having the enhanced network lifetime and scalability. In this work, a Metaheuristic Load-Balancing-Based Clustering Technique (MLBCT) in wireless sensor networks has been proposed which formulates the energy-balanced clusters based on the differential evolution technique to improve the network lifetime. To ensure the formation of balanced clusters, several metrics like nodes’ proximity, nodes’ distribution, and energy distribution across the sensing field have been considered. Moreover, to facilitate the even load distribution among the cluster members, a randomized rotation of cluster head is implemented. The supremacy of the proposed scheme is confirmed through an extensive set of simulations against the state-of-art schemes. Simulation results reflect an average gain of 51.85% in network lifetime under the variable network configurations in an ideal environment. Moreover, a thorough statistical analysis is performed to prove the efficacy of the proposed fitness function by obtaining confidence intervals under two different network scenarios with variable node counts.

Reinforcement Optimization Algorithm for Mobile Robot Sensor Networks Drive Motion Improvement

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“…In [21], [22], the adaptability of sensor algorithms to any type of network is idealized to be 0 %. Table III presents the algorithm proposed implemented with various network types [16]- [22] regardless of the map environment. The adaptability of the proposed algorithms varies from -10 % to 10 %, which shows their practical feasibility.…”

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

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A Systematic Study of Clustering Techniques for Energy Efficiency in Wireless Sensor Networks

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