Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm‐based clustering for guaranteeing energy stability and network lifetime in WSN

Author:

Rajarajeswari Palaniappan1ORCID,Shyamala Chandrasekaran2,Mohana Shivashankar1

Affiliation:

1. Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamil Nadu India

2. Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India

Abstract

SummaryWireless sensor networks (WSNs) plays an indispensable role in the human life by supporting a diversified number of applications that includes military, environment monitoring, manufacturing, education, agriculture, etc. However, the sensor node batteries cannot be replaced under its deployment in an unattended or remote area due to their wireless existence. Cluster‐based routing is significant in handling the issue of energy stability and network lifetime. The meta‐heuristic algorithms‐based cluster head (CH) selection is determined to be highly promising for attaining the objective of CH selection that results in acquiring an optimal network performance. In this paper, a Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm (HGICSOA)‐based clustering scheme is proposed for attaining potential CH selection and guarantee significant sink mobility‐based data transmission. The capability of GHOA that controls the rate of exploitation and exploration degree is utilized for CH selection. It specifically adopted OBL‐based GHOA for optimal CH selection based on the objective function, which is formulated using node density, residual energy, and distance between sensor node and sink. It incorporated new CSOA for mobility‐based data transmission for increasing population diversity. It also utilized the benefits of ICSOA with a predominant local search strategy for achieving better sink mobility‐based data transmission. Simulation and statistical results confirmed that the proposed HGICSOA is better in attaining maximum energy stability by 17.21% and improved network lifetime by 23.82%, compared to the benchmarked schemes used for investigation. Moreover, the prevention rate of worst sensor nodes selected as CH is improved by 21.38%, better than baseline approaches.

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

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1. Energy based multi objective golden jackal optimization for cluster based routing in wireless sensor network;Soft Computing;2024-07-24

2. Minimum cascade repair method for mobile network nodes failure under time–frequency feature fusion;Mobile Networks and Applications;2024-04-03

3. Hybrid Intelligent Fusion-Based Perspectives for WSN-IOT;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

4. Enhancement of Network Lifetime by Decreasing Energy Consumption in WSN using Goat Fish Algorithm;2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON);2023-08-05

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