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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

Hybrid algorithm for fault node recovery and energy efficiency in wireless sensor networks

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pp. 2347–2367Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1831XML
Received:
18 Sep 2024
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1831
Pages:
2347–2367

Abstract

The Wireless Sensor Networks (WSNs) are designed for the monitoring of remote areas in various places with a variety of different applications. The main challenges with the WSN are energy efficiency and fault recovery. In order to optimize the network lifetime of the WSN, fault node recovery and energy efficient clustering are required in order to efficiently utilize the energy supply device of battery-powered sensors. The purpose of this paper is to develop a hybrid algorithm that combines the K-means clustering technique with fault node recovery in the WSN in order to reduce energy consumption and extend sensor lifetimes. A hybrid algorithm combine’s fault node recovery with energy-efficient clustering methods in order to reduce energy usage. We are using Grade Diffusion (GD) with Genetic Algorithm (GA) to detect fault nodes. In complex or large WSNs, K-means clustering can be used to reduce the complexity of the hybrid algorithm in order to reduce its complexity. As the hybrid algorithm is used for identifying fault nodes and replacing nodes with neighbor nodes, it is primarily used for the computation of grade values. With the proposed fault node recovery and energy efficient clustering methods, the energy consumption of each node can be minimized and the network lifetime can be improved as well. Using the MATLAB platform, we compared the suggested method to several existing ones including “Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Hierarchical Clustering Approach (HHCA), Novel Energy Aware Hierarchical Cluster (NEAHC), and Heuristic Algorithm for Clustering Hierarchical Protocol (HACH)”, among others in terms of residual and consumption energy, as well as receiving packet data.

Keywords

Genetic algorithmsEnergy-efficientFaulty nodesGrade diffusionGenetic algorithmsSensor networksEnergy-efficient

Subject Classifications

68M18

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