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<p>Directional sensor network(DSN) utilize directional sensors with a limited field of view for focused monitoring. This directionality enhances energy efficiency by reducing unnecessary sensing and data transmission. These sensor enables precise monitoring of specific areas while minimizing data redundancy. Optimizing sensor orientation is crucial for maximizing area coverage in randomly deployed networks, particularly in inaccessible terrains. Effective orientation ensures maximum coverage, enhances monitoring efficiency, and reduces the need for costly redeployments in challenging environments. This paper proposes a novel Hybrid Firefly-Coyote Optimization Algorithm (HF-COA) to address this problem. A grid-based mathematical framework is proposed to evaluate area coverage. The proposed HF-COA achieves superior performance in maximizing area coverage due to its effective balance of exploration and exploitation. Experimental results demonstrate that HF-COA achieves superior coverage performance compared to standalone FA and COA, under varying sensor parameters. The proposed method offers a reliable solution for enhancing monitoring efficiency in complex and dynamic environments.</p>

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Hybrid meta-heuristic strategy based on Firefly and Coyote optimization for area coverage enhancement in Directional Sensor Network

  • 期刊论文

  • 发表日期：

    2025年9月16日

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  • P. Surya Bharathi,
  • R. Pavithra

摘要

Directional sensor network(DSN) utilize directional sensors with a limited field of view for focused monitoring. This directionality enhances energy efficiency by reducing unnecessary sensing and data transmission. These sensor enables precise monitoring of specific areas while minimizing data redundancy. Optimizing sensor orientation is crucial for maximizing area coverage in randomly deployed networks, particularly in inaccessible terrains. Effective orientation ensures maximum coverage, enhances monitoring efficiency, and reduces the need for costly redeployments in challenging environments. This paper proposes a novel Hybrid Firefly-Coyote Optimization Algorithm (HF-COA) to address this problem. A grid-based mathematical framework is proposed to evaluate area coverage. The proposed HF-COA achieves superior performance in maximizing area coverage due to its effective balance of exploration and exploitation. Experimental results demonstrate that HF-COA achieves superior coverage performance compared to standalone FA and COA, under varying sensor parameters. The proposed method offers a reliable solution for enhancing monitoring efficiency in complex and dynamic environments.