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Multi-event Detection with Rechargeable Sensors

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Abstract

In this paper, we use a rechargeable sensor network to detect important events. Different from most existing works which mainly pay attention to the event detection problems of single event, we focus on multi-sensor and multi-event problems. Our goal is to maximize capture probabilities of all the events in a special area. First, we analyze the single-event case and find a multi-sensor cooperation schedule. It is shown that when each sensor’s battery capacity is large enough, the detecttion rate gained from cooperation schedule tends to be the optimal value. Then, we extend results to multi-sensor multi-event case. We use a weighted sum method to transform the multi-objective problem into a general linear programming. By considering the characteristics of this programming, a Pareto optimal solution can be found directly. We can also use weights and constraints to reflect the importance of each event, and get different Pareto optimal solutions via changing these parameters.

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Notes

  1. Because each rechargeable sensor has a low charging power, it does not have enough energy to sustain continue work. So in this paper, we always assume \({\sum }_{i=1}^{N}\alpha _{i}q_{i}c_{i}\le \delta ,\) ∀α i .

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61403347, 61374020, 61302076, 61671411, and 61611130211, by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LY17F030023, LR15F010002 and LY17F010023, by the Science Foundation of Zhejiang Sci-Tech University under Grant No. 14032007-Y and 15032086-Y.

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Authors and Affiliations

  1. School of Information Science & Technology, Zhejiang Sci-Tech University, Hangzhou, China

    Zhu Ren, Weiqiang Xu, Yanyun Dai & Lurong Jiang

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  1. Zhu Ren
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  2. Weiqiang Xu
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  3. Yanyun Dai
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  4. Lurong Jiang
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Correspondence to Weiqiang Xu.

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Ren, Z., Xu, W., Dai, Y. et al. Multi-event Detection with Rechargeable Sensors. Peer-to-Peer Netw. Appl. 10, 708–716 (2017). https://doi.org/10.1007/s12083-016-0521-9

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  • DOI: https://doi.org/10.1007/s12083-016-0521-9

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