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In wildlife conservation, monitoring species’ activities is essential for understanding habitats, migration, and population dynamics. Sensors like detectors and cameras collect data, which is gathered by base stations within their signal ranges. Base stations can adjust their power, where the power consumption p(s) relates to the sensing radius r(s) as \( p(s) = c \cdot r(s)^\alpha \) . The minimum power partial cover problem (MinPPC) seeks to minimize power while covering at least k sensors. Addressing fairness concerns, we propose the MinPPC with fairness constraints (MinPPCF), ensuring each sensor category meet a coverage threshold. We present a polynomial-time algorithm with an approximation ratio of \( O(\alpha + T) \) , where T is the number of categories.

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Minimum Power Partial Cover with Fairness Constraint

  • 会议论文

  • 发表日期：

    2025年

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  • Chensheng Ma,
  • Zhao Zhang

摘要

In wildlife conservation, monitoring species’ activities is essential for understanding habitats, migration, and population dynamics. Sensors like detectors and cameras collect data, which is gathered by base stations within their signal ranges. Base stations can adjust their power, where the power consumption p(s) relates to the sensing radius r(s) as \( p(s) = c \cdot r(s)^\alpha \) . The minimum power partial cover problem (MinPPC) seeks to minimize power while covering at least k sensors. Addressing fairness concerns, we propose the MinPPC with fairness constraints (MinPPCF), ensuring each sensor category meet a coverage threshold. We present a polynomial-time algorithm with an approximation ratio of \( O(\alpha + T) \) , where T is the number of categories.