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<p>Directional sensors play a critical role in enabling precise data collection and effective monitoring within wireless sensor networks (WSNs). Despite their effectiveness in tasks like surveillance and environmental observation, challenges remain in prolonging network life and ensuring multi-coverage, particularly Q-coverage. Multi-coverage issues arise in over-provisioned (excess sensors) and under-provisioned (insufficient sensors) environments, leading to inefficient resource utilization or coverage gaps. Achieving effective multi-coverage necessitates strategic resource allocation to maintain comprehensive monitoring while avoiding redundancy. This paper addresses the Q-coverage optimization problem in directional sensor networks with adjustable orientations by assessing whether the environment is over-provisioned or under-provisioned and dynamically determines the operational status (active or inactive) and orientation of each sensor to achieve two main objectives: maximizing network coverage balancing in under-provisioned environments and minimizing the number of active sensors in over-provisioned settings. To meet these goals, we introduce LSHADE-navigated generation search (LSHADE-NGS), a novel enhancement of the LSHADE algorithm designed to navigate each generation toward more promising search spaces. The proposed algorithm integrates several innovative components: a refined initialization method to improve the diversity of the starting population, a heuristic adjustment JADE mutation (HA-JADE) to dynamically adjust mutation strategies, and a greedily-jumped binomial crossover mechanism on the directional array (GJ-Bi) to enhance convergence speed. The algorithm’s effectiveness is evaluated using multiple metrics, including the Q-balancing index, distance index, coverage quality, power consumption, and the count of active sensors. The experimental results reveal significant enhancements in solution quality achieved by LSHADE-NGS, underscoring the method’s superiority over existing approaches and illustrating its distinct advantages compared to the conventional LSHADE framework.</p>

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LSHADE-NGS: enhancing Q-coverage in directional sensor networks through navigated generation search

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Binh Huynh Thi Thanh,
  • Cuong Van Duc,
  • Son Nguyen Van,
  • Quan La Van,
  • Hanh Nguyen Thi

摘要

Directional sensors play a critical role in enabling precise data collection and effective monitoring within wireless sensor networks (WSNs). Despite their effectiveness in tasks like surveillance and environmental observation, challenges remain in prolonging network life and ensuring multi-coverage, particularly Q-coverage. Multi-coverage issues arise in over-provisioned (excess sensors) and under-provisioned (insufficient sensors) environments, leading to inefficient resource utilization or coverage gaps. Achieving effective multi-coverage necessitates strategic resource allocation to maintain comprehensive monitoring while avoiding redundancy. This paper addresses the Q-coverage optimization problem in directional sensor networks with adjustable orientations by assessing whether the environment is over-provisioned or under-provisioned and dynamically determines the operational status (active or inactive) and orientation of each sensor to achieve two main objectives: maximizing network coverage balancing in under-provisioned environments and minimizing the number of active sensors in over-provisioned settings. To meet these goals, we introduce LSHADE-navigated generation search (LSHADE-NGS), a novel enhancement of the LSHADE algorithm designed to navigate each generation toward more promising search spaces. The proposed algorithm integrates several innovative components: a refined initialization method to improve the diversity of the starting population, a heuristic adjustment JADE mutation (HA-JADE) to dynamically adjust mutation strategies, and a greedily-jumped binomial crossover mechanism on the directional array (GJ-Bi) to enhance convergence speed. The algorithm’s effectiveness is evaluated using multiple metrics, including the Q-balancing index, distance index, coverage quality, power consumption, and the count of active sensors. The experimental results reveal significant enhancements in solution quality achieved by LSHADE-NGS, underscoring the method’s superiority over existing approaches and illustrating its distinct advantages compared to the conventional LSHADE framework.