基于元启发式算法的无线传感器网络多目标节能聚类协议

IF 2.4 Q3 TELECOMMUNICATIONS
Mohamadhosein Behzadi, Homayun Motameni, Hosein Mohamadi, Behnam Barzegar
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引用次数: 0

摘要

由于传感器节点的有限性,有效的资源管理仍然是无线传感器网络(WSNs)面临的一个关键挑战。本文提出了一种新的混合集群协议来解决这一问题,旨在优化能耗、延长网络寿命和增强可扩展性。我们的方法结合了改进版本的二进制蜻蜓算法(IVBDA)来选择簇头(CH)和Mamdani模糊推理系统来有效地形成簇。在CH选择和集群形成之后,一个多跳路由机制在WSN内传输数据包。为了验证所提出的协议的性能,在各种网络拓扑上进行了广泛的模拟,评估了诸如平均能耗、活动节点计数、网络寿命和基站(BS)的数据包接收等指标。通过与现有的聚类协议和其他元启发式算法(包括二进制粒子群优化算法(BPSO)、二进制鲸鱼优化算法(BWOA)和二进制蜻蜓算法(BDA)的比较分析,证明了所提出的混合方法在能源效率、网络寿命和整体WSN性能方面具有优越的性能。通过对多目标适应度函数的检验,证明改进后的BDA比BPSO、BWOA和BDA具有更快的收敛速度。本文为高效聚类协议的开发做出了重要贡献,并展示了混合元启发式和模糊推理技术在优化wsn资源分配方面的潜力。该协议在网络生存期和整体性能上优于其他协议,表明它有潜力成为wsn资源管理的有价值的解决方案。对元启发式算法的评价突出了在优化节能聚类时考虑收敛速度的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms

Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms

Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms

Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms

Multi-Objective Energy-Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms

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.

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来源期刊
IET Wireless Sensor Systems
IET Wireless Sensor Systems TELECOMMUNICATIONS-
CiteScore
4.90
自引率
5.30%
发文量
13
审稿时长
33 weeks
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.
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