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An adaptive learning grey wolf optimizer for coverage optimization in WSNs
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DOI：10.1016/j.eswa.2023.121917.png)
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摘要
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Wireless sensor networks (WSNs) are widely used emerging technologies, but they also face many challenges in practical applications. One of the most critical challenges is the coverage problem. The size of the sensor nodes' coverage determines the service quality of WSNs. Therefore, an adaptive learning grey wolf optimizer (ALGWO) is proposed to optimize the coverage problem for 2D and more complex 3D regions. In ALGWO, a dynamic opposite learning strategy with dynamic, asymmetric search is employed to prevent premature convergence and improve the exploration capability. Adaptive dimensional learning provides information on the neighborhood dimension for individuals to overcome the dependence on the first three wolves, thus improving the diversity of the population. Meanwhile, the dimensional learning of each individual adaptively performs exploration and exploitation. The performance of ALGWO is evaluated in 2D and 3D WSNs scenarios through simulations. Compared with GWO and its advanced variants, the simulation results show that the ALGWO algorithm has better coverage. In 2D scenes, ALGWO achieved the average coverage of 95.5% and the maximum coverage of 96.93%. In 3D scenes, ALGWO achieved the average coverage of 94.32% and the maximum coverage of 95.13%.
Keyword:
Wireless sensor networks
Grey wolf optimizer
Dynamic opposite learning
Dimensional learning
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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