Hybrid algorithm for fault node recovery and energy efficiency in wireless sensor networks
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 ene...
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| Vydané v: | Journal of information & optimization sciences Ročník 45; číslo 8; s. 2347 - 2367 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
2024
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| ISSN: | 0252-2667, 2169-0103 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. |
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| ISSN: | 0252-2667 2169-0103 |
| DOI: | 10.47974/JIOS-1831 |