Open Access

ARTICLE

An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

I. Adumbabu^1,*, K. Selvakumar²

1 Department of Electronics & Communication Engineering, Annamalai University, Chidambaram, 608002, India
2 Department of Information Technology, Annamalai University, Chidambaram, 608002, Tamilnadu, India

* Corresponding Author: I. Adumbabu. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1939-1956. https://doi.org/10.32604/iasc.2023.029489

Received 04 March 2022; Accepted 14 April 2022; Issue published 19 July 2022

Abstract

A wireless sensor network (WSN) is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions. In WSN, the location of the base station is critical. Although base stations are fixed, they may move in response to data received from sensor nodes under specific conditions. Clustering is a highly efficient approach of minimising energy use. The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper. It has been established that integrating mobile sinks into wireless sensor networks extends their longevity. Thus, this research proposes an optimal clustering and routing technique for optimising the energy usage and lifetime of WSNs. To minimise energy consumption, this research employs movable and stationary sink nodes. The K-Medoid clustering model is used to generate the initial number of nodes in the various clusters. After that, the cluster head is chosen using a hybrid Interval Type-2 Fuzzy technique that takes three aspects into account: residual energy, node centrality, and neighbourhood. A highly efficient backup cluster head (CH) collecting system can provide in significant energy savings while also prolonging the system’s life. Finally, better Reinforcement learning combined with a Genetic algorithm routing protocol is used to ensure effective data delivery. The suggested approach’s efficacy is evaluated in comparison to earlier approaches utilising residual node energy, delay or average delay, packet delivery ratio, throughput, network longevity, average energy consumption, and multiple alive nodes. In experiments, the proposed strategy outperforms existing strategies.

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Keywords

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