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Open AccessArticle
Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks
by
Halimjon Khujamatov
Halimjon Khujamatov 1
,
Mohaideen Pitchai
Mohaideen Pitchai 2
,
Alibek Shamsiev
Alibek Shamsiev 3,
Abdinabi Mukhamadiyev
Abdinabi Mukhamadiyev 1,*
and
Jinsoo Cho
Jinsoo Cho 1,*
1
Department of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Gyeonggi-do, Republic of Korea
2
Department of Computer Science and Engineering, National Engineering College, Kovilpatti 627011, Tamilnadu, India
3
Department of Data Communication Networks and Systems, Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi, Tashkent 100200, Uzbekistan
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(13), 4406; https://doi.org/10.3390/s24134406 (registering DOI)
Submission received: 14 May 2024
/
Revised: 5 July 2024
/
Accepted: 5 July 2024
/
Published: 7 July 2024
Abstract
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.
Share and Cite
MDPI and ACS Style
Khujamatov, H.; Pitchai, M.; Shamsiev, A.; Mukhamadiyev, A.; Cho, J.
Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks. Sensors 2024, 24, 4406.
https://doi.org/10.3390/s24134406
AMA Style
Khujamatov H, Pitchai M, Shamsiev A, Mukhamadiyev A, Cho J.
Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks. Sensors. 2024; 24(13):4406.
https://doi.org/10.3390/s24134406
Chicago/Turabian Style
Khujamatov, Halimjon, Mohaideen Pitchai, Alibek Shamsiev, Abdinabi Mukhamadiyev, and Jinsoo Cho.
2024. "Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks" Sensors 24, no. 13: 4406.
https://doi.org/10.3390/s24134406
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