TMOALO: A Sensor Node Load Balancing Approach for Power Internet of Things Networks

The improvement of service quality (QoS) and network lifetime extension are identified as urgent issues due to the widespread adoption of wireless sensor networks (WSNs) in Power Internet of Things (PIoT). During the secondary deployment phase, a typical multi-objective optimization challenge is con...

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Vydané v:Journal of physics. Conference series Ročník 3022; číslo 1; s. 12008 - 12015
Hlavní autori: Chen, Yanling, Zhu, Yuanjiao, Wei, Jingyi, Sun, Zheng, Jia, Dingyi, Zhang, Yao, Li, Jingsong, Li, Zegui, Li, Jingyun, Chen, Wenbin, Qu, Xin, Zhou, Jie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.05.2025
Predmet:
Algorithms
Internet of Things
Multi-objective Ant Lion Optimization Algorithm
Multiple objective analysis
Nodes
Nonlinear control
Optimization
Power Internet of Things
Quality of service architectures
Search process
Secondary Deployment
Sensors
Tabu Search
Wireless Sensor Network
Wireless sensor networks
ISSN:1742-6588, 1742-6596
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Shrnutí:The improvement of service quality (QoS) and network lifetime extension are identified as urgent issues due to the widespread adoption of wireless sensor networks (WSNs) in Power Internet of Things (PIoT). During the secondary deployment phase, a typical multi-objective optimization challenge is constituted by how to maximize area coverage while the node movement distance is minimized. Therefore, a novel Tabu-based Multi-Objective Ant Lion Optimization algorithm (TMOALO) is proposed to address this challenge. Initially, a dynamic tabu search operator is designed to prevent the algorithm from being trapped in local optima, and thus the global search capability is enhanced. Subsequently, a layered elite preservation framework is introduced to ensure that high-quality solutions are effectively retained, whereby population diversity is increased. Furthermore, a nonlinear adaptive step-size control regulator is developed to optimize step adjustments, through which the stability and efficiency of the search process are improved. Experiments on a PIoT simulation platform demonstrate that the proposed TMOALO algorithm outperforms conventional methods like MALO, NSGA-II, and MOPSO, achieving an 11.005% increase in area coverage and a 4.118-meter reduction in sensor node movement. These results confirm its superiority in multi-objective optimization.
Bibliografia:ObjectType-Article-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/3022/1/012008