{"title":"Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms","authors":"Mohamadhosein Behzadi, Homayun Motameni, Hosein Mohamadi, Behnam Barzegar","doi":"10.1049/wss2.70011","DOIUrl":null,"url":null,"abstract":"<p>Efficient resource management remains a critical challenge in wireless sensor networks (WSNs) due to the constrained nature of sensor nodes. This paper proposes a novel hybrid clustering protocol to address this issue, aiming to optimise energy consumption, extend network lifetime and enhance scalability. Our approach combines the improved version of binary dragonfly algorithm (IVBDA) for cluster head (CH) selection and the Mamdani fuzzy inference system for effective cluster formation. After CH selection and cluster formation, a multi-hop routing mechanism transmits data packets within the WSN. To validate the performance of the proposed protocol, extensive simulations are conducted on various network topologies, evaluating metrics such as average energy consumption, live node count, network lifetime, and packet reception at the base station (BS). Comparative analyses with existing clustering protocols and other metaheuristic algorithms, including binary particle swarm optimisation (BPSO), binary whale optimisation algorithm (BWOA) and binary dragonfly algorithm (BDA), demonstrate the superior performance of the proposed hybrid approach in terms of energy efficiency, network longevity and overall WSN performance. The improved version of BDA shows faster convergence than BPSO, BWOA and BDA, as ascertained by examining the multi-objective fitness function. This paper contributes significantly to the development of efficient clustering protocols and showcases the potential of hybrid metaheuristic and fuzzy inference techniques for optimising resource allocation in WSNs. The proposed protocol outperforms other protocols in network lifetime and overall performance, indicating its potential to be a valuable solution for resource management in WSNs. The evaluation of metaheuristic algorithms highlights the importance of considering convergence speed in optimising energy-efficient clustering.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"15 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70011","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Wireless Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/wss2.70011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Efficient resource management remains a critical challenge in wireless sensor networks (WSNs) due to the constrained nature of sensor nodes. This paper proposes a novel hybrid clustering protocol to address this issue, aiming to optimise energy consumption, extend network lifetime and enhance scalability. Our approach combines the improved version of binary dragonfly algorithm (IVBDA) for cluster head (CH) selection and the Mamdani fuzzy inference system for effective cluster formation. After CH selection and cluster formation, a multi-hop routing mechanism transmits data packets within the WSN. To validate the performance of the proposed protocol, extensive simulations are conducted on various network topologies, evaluating metrics such as average energy consumption, live node count, network lifetime, and packet reception at the base station (BS). Comparative analyses with existing clustering protocols and other metaheuristic algorithms, including binary particle swarm optimisation (BPSO), binary whale optimisation algorithm (BWOA) and binary dragonfly algorithm (BDA), demonstrate the superior performance of the proposed hybrid approach in terms of energy efficiency, network longevity and overall WSN performance. The improved version of BDA shows faster convergence than BPSO, BWOA and BDA, as ascertained by examining the multi-objective fitness function. This paper contributes significantly to the development of efficient clustering protocols and showcases the potential of hybrid metaheuristic and fuzzy inference techniques for optimising resource allocation in WSNs. The proposed protocol outperforms other protocols in network lifetime and overall performance, indicating its potential to be a valuable solution for resource management in WSNs. The evaluation of metaheuristic algorithms highlights the importance of considering convergence speed in optimising energy-efficient clustering.
期刊介绍:
IET Wireless Sensor Systems is aimed at the growing field of wireless sensor networks and distributed systems, which has been expanding rapidly in recent years and is evolving into a multi-billion dollar industry. The Journal has been launched to give a platform to researchers and academics in the field and is intended to cover the research, engineering, technological developments, innovative deployment of distributed sensor and actuator systems. Topics covered include, but are not limited to theoretical developments of: Innovative Architectures for Smart Sensors;Nano Sensors and Actuators Unstructured Networking; Cooperative and Clustering Distributed Sensors; Data Fusion for Distributed Sensors; Distributed Intelligence in Distributed Sensors; Energy Harvesting for and Lifetime of Smart Sensors and Actuators; Cross-Layer Design and Layer Optimisation in Distributed Sensors; Security, Trust and Dependability of Distributed Sensors. The Journal also covers; Innovative Services and Applications for: Monitoring: Health, Traffic, Weather and Toxins; Surveillance: Target Tracking and Localization; Observation: Global Resources and Geological Activities (Earth, Forest, Mines, Underwater); Industrial Applications of Distributed Sensors in Green and Agile Manufacturing; Sensor and RFID Applications of the Internet-of-Things ("IoT"); Smart Metering; Machine-to-Machine Communications.