Energy aware clustering protocol using chaotic gorilla troops optimization algorithm for Wireless Sensor Networks

Energy efficiency is treated as a challenging problem in Wireless Sensor Networks (WSNs) which involves limited non-replaceable and non-rechargeable inbuilt batteries. Optimal utilization of available energy in the sensor nodes is an effective way to improve the lifetime of the WSN with assured qual...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-03, Vol.83 (8), p.23853-23871
Main Authors: Sivakumar, Deena, Devi, S. Suganthi, Nalini, T.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Energy efficiency
Energy management
Heuristic methods
Multimedia Information Systems
Optimization
Optimization algorithms
Performance enhancement
Quality assurance
Quality of service architectures
Rechargeable batteries
Sensors
Special Purpose and Application-Based Systems
Wireless sensor networks
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Summary:Energy efficiency is treated as a challenging problem in Wireless Sensor Networks (WSNs) which involves limited non-replaceable and non-rechargeable inbuilt batteries. Optimal utilization of available energy in the sensor nodes is an effective way to improve the lifetime of the WSN with assured quality of service (QoS). Clustering can be employed as an efficient approach for enhancing network lifetime and scalability. Since clustering is considered an NP-hard problem, several metaheuristic algorithms are utilized for accomplishing energy efficiency. With this motivation, this study proposes an energy-aware clustering protocol utilizing a chaotic gorilla troops optimization algorithm (EACP-CGTOA) for WSN. The proposed EACP-CGTOA model derives a CGTOA by replacing the population initiation with circle chaotic mapping to explore the solutions with a high convergence rate and sensitivity. The CGTOA helps to increase the population diversity and overall performance of the optimization algorithm. Besides, the EACP-CGTOA model derives a fitness function involving three input parameters namely distance to neighbours (DTN), distance to base station (DBS), and energy ratio (ER). To ensure the enhanced performance of the EACP-CGTOA technique, a wide range of simulations were carried out and the outcomes are examined under several aspects. The experimental results ensured that the network lifetime and energy efficiency are considerably improved by the EACP-CGTOA model over the existing methods.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16487-3