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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.6.NQ22260


1Dr. Sachin S. Bere, 2Dr. Rahul A. Patil, 3Prof.Kanika Chauhan, 4Prof. Deepak R. Derle


Wireless Sensor Networks (WSN) is built with the purpose of monitoring distant areas in a variety of settings and using a variety of applications. The primary challenges that the WSN faces are those of energy efficiency and fault recovery. The WSN calls for fault node recovery and clustering that is efficient with energy in order to make the most of the energy supply device that is comprised of battery-powered sensors and increase the network's lifespan. Therefore, the purpose of this research is to build a hybrid method using K-means clustering in order to lower the amount of energy that is used by WSN sensors and to increase their lifespan. The hybrid algorithm is a method that incorporates both fault node recovery and energy efficient clustering techniques into a single solution. Utilizing Grade Diffusion (GD) in conjunction with the Genetic Algorithm allows for the discovery of fault nodes to be carried out (GA).. The proposed method is carried out on the MATLAB platform, and it is contrasted with the existing methods, which include Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Hierarchical Clustering Approach (HHCA), Novel Energy Aware Hierarchical Cluster (NEAHC), and Heuristic


Energy efficient, grade diffusion, fault node, sensors, genetic algorithm, energy consumption

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