Probabilistic coverage methods in people‐centric sensing

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  • Ahmed Asaad
    Graduate School of Information Science, Nara Institute of Science and Technology Mathematics Department, Faculty of Science, Al-Azhar University
  • Yasumoto Keiichi
    Graduate School of Information Science, Nara Institute of Science and Technology Japan Science and Technology Agency, CREST.
  • Yamauchi Yukiko
    Graduate School of Information Science and Electrical Engineering, Kyushu University Japan Science and Technology Agency, CREST.
  • Ito Minoru
    Graduate School of Information Science, Nara Institute of Science and Technology

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  • Probabilistic Coverage Methods in People-Centric Sensing

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Aiming to achieve sensing coverage for given Areas of Interest (AoI) over time at low cost in a People-Centric Sensing manner, we propose a concept of (α, T)-coverage of a target field where each point in the field is sensed by at least one mobile node with the probability of at least α during time period T. Our goal is to achieve (α, T)-coverage of a given AoI by a minimal set of mobile nodes. In this paper, we propose two algorithms: inter-location algorithm that selects a minimal number of mobile nodes from nodes inside the AoI considering the distance between them and inter-meeting-time algorithm that selects nodes regarding the expected meeting time between the nodes. To cope with the case that there is an insufficient number of nodes inside the AoI, we propose an extended algorithm which regards nodes inside and outside the AoI. To improve the accuracy of the proposed algorithms, we also propose an updating mechanism which adapts the number of selected nodes based on their latest locations during the time period T. In our simulation-based performance evaluation, our algorithms achieved (α, T)-coverage with good accuracy for various values of α, T, AoI size, and moving probability.

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