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Improved Particle Swarm Algorithm and Its Application in Sensor Network Optimization

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Exploration of Novel Intelligent Optimization Algorithms (ISICA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1590))

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Abstract

The application of wireless sensor network in industry and agriculture has been very popular, but it has limitations due to the influence of geographical location and other environmental factors, and the problem of low network coverage caused by the random deployment of sensor nodes, resulting in waste of resources. To improve network coverage. In this paper, an improved particle swarm optimization PSO (IPSO) is proposed, in which a linear decreasing inertia weight and a contraction factor are used to enhance the PSO. The experimental results show that the improved PSO has faster convergence speed, and can effectively improve the signal coverage of the sensor network.

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Acknowledgement

This work is supported by the Natural Science Foundation of Guangdong Province of China with the Grant No.2020A1515010784; Guangdong Youth Characteristic Innovation Project (2021KQNCX120) and Project of Guangdong Provincial Department of Education(2020KTSCX166).

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Authors and Affiliations

  1. College of Computer Science, Guangdong University of Science and Technology, Dongguan, 523000, Guangdong, China

    Yi Wang & Jun Nie

Authors
  1. Yi Wang
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  2. Jun Nie
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Corresponding author

Correspondence to Yi Wang .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. The University of Aizu, Aizu-Wakamatsu, Japan

    Yong Liu

  3. South China Agricultural University, Guangzhou, China

    Wenxiang Wang

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Wang, Y., Nie, J. (2022). Improved Particle Swarm Algorithm and Its Application in Sensor Network Optimization. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_1

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  • DOI: https://doi.org/10.1007/978-981-19-4109-2_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4108-5

  • Online ISBN: 978-981-19-4109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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