Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks

作     者：Khujamatov, Halimjon Pitchai, Mohaideen Shamsiev, Alibek Mukhamadiyev, Abdinabi Cho, Jinsoo 

作者机构：Gachon Univ Dept Comp Engn Seongnam Si 1342 Gyeonggi Do South Korea Natl Engn Coll Dept Comp Sci & Engn Kovilpatti 627011 Tamil Nadu India Tashkent Univ Informat Technol Dept Data Commun Networks & Syst Tashkent 100200 Uzbekistan 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2024年第24卷第13期

页      面：4406页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government (MSIT) [RS-2023-00229801] 

主　　题：chaotic genetic algorithm clustering energy efficiency grey wolf optimizer routing sensor networks 

摘      要：As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.