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Toward a Distributed Potential Game Optimization to Sensor Area Coverage Problem

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Listed:
  • Jun Huang

    (The College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Shushan District, Hefei 230037, China)

  • Jie Chen

    (The College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Shushan District, Hefei 230037, China)

  • Rongcheng Dong

    (The College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Shushan District, Hefei 230037, China)

  • Xinli Xiong

    (The College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Shushan District, Hefei 230037, China)

  • Simao Xu

    (The College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Shushan District, Hefei 230037, China)

Abstract

The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based greedy learning (MGL) algorithm is proposed, which can ensure convergence to Nash equilibrium. Compared with existing representative algorithms, our proposed algorithm performs the best in terms of average coverage, best value, and standard deviation within within a suitable time. In addition, increasing memory length helps to generate a better Nash equilibrium.

Suggested Citation

  • Jun Huang & Jie Chen & Rongcheng Dong & Xinli Xiong & Simao Xu, 2025. "Toward a Distributed Potential Game Optimization to Sensor Area Coverage Problem," Mathematics, MDPI, vol. 13(17), pages 1-11, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:17:p:2709-:d:1730621
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    References listed on IDEAS

    as
    1. Chen Guo & Yinghua Song, 2025. "Multi-Subject Decision-Making Analysis in the Public Opinion of Emergencies: From an Evolutionary Game Perspective," Mathematics, MDPI, vol. 13(10), pages 1-26, May.
    2. Xinmin Zhou & Wenhao Rao & Yaqiong Liu & Shudong Sun, 2024. "A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information," Mathematics, MDPI, vol. 12(7), pages 1-22, March.
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