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Robust scheduling for target tracking using wireless sensor networks

机译:使用无线传感器网络进行目标跟踪的鲁棒调度

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摘要

A wireless sensor network (WSN) is a group of sensors deployed in an area, with all of them working on a battery and with direct communications inside the network. A fairly common situation, addressed in this work, is to monitor and record data with a WSN about vehicles (planes, terrestrial vehicles, boats, etc) passing by an area with damaged infrastructures. In such a context, an activation schedule for the sensors ensuring a continuous coverage of all the targets is required. Furthermore, the collected data, in order to be treated, have to be transmitted to a base station in the area, near the sensors. In this work, the future monitoring missions of the network are also taken into account, as well as the energy consumption of the current mission. We also consider that the spatial trajectories of the targets are known, whereas the speed of the targets along their trajectories are estimated, and subject to uncertainty. Hence, the main objective is to seek solutions that can withstand earliness and tardiness from the previsions. We propose a formulation of the problem with three different objectives and a solution method with experiments and results. The objectives are treated in a lexicographic order as follows (i) maximize the robustness schedule to cope with the advances and delaqui leys of the targets, (ii) maximize the minimum of monitoring time we can guarantee in priority areas, (iii) maximize the amount of energy left in the sensor batteries. We propose new upper bounds on the robustness measure, that are exploited by the solution approach whose complexity is shown to be pseudo-polynomial. The solution approach is based on a preprocessing step called discretisation, and the resolution of a series of linear programs. (C) 2020 Elsevier Ltd. All rights reserved.
机译:无线传感器网络(WSN)是一组部署在一个区域中的传感器,它们全部依靠电池工作,并在网络内部进行直接通信。在本工作中解决的一个相当普遍的情况是,使用WSN监视和记录有关经过基础设施受损区域的车辆(飞机,陆地车辆,船只等)的数据。在这种情况下,需要用于传感器的激活时间表,以确保连续覆盖所有目标。此外,为了被处理,所收集的数据必须被发送到传感器附近的区域中的基站。在这项工作中,还考虑了网络的未来监视任务以及当前任务的能耗。我们还认为目标的空间轨迹是已知的,而目标沿其轨迹的速度是估计的,并且存在不确定性。因此,主要目的是寻求能够抵御这些规定的提早和拖延的解决方案。我们提出了具有三个不同目标的问题的表述以及具有实验和结果的解决方法。目标按字典顺序处理如下:(i)最大化鲁棒性时间表以应对目标的进展和延迟,(ii)最大化我们可以保证在优先领域中的最小监视时间,(iii)最大化目标传感器电池中剩余的电量。我们提出了鲁棒性度量的新上限,其解决方案方法利用了其上限,其复杂性被证明是伪多项式。解决方案方法基于称为离散化的预处理步骤,以及一系列线性程序的分辨率。 (C)2020 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Computers & operations research》 |2020年第4期|104873.1-104873.14|共14页
  • 作者

  • 作者单位

    Univ Angers LERIA F-49045 Angers France;

    Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China;

    Univ Paris 09 LAMSADE CNRS UMR 7243 F-75016 Paris France;

    Univ Bretagne Sud Lab STICC CNRS UMR 6285 F-56321 Lorient France;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Linear programming; Sensor network; Robust optimization; Target tracking;

    机译:线性规划;传感器网络;强大的优化;目标跟踪;

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