Trapping Mobile Targets in Wireless Sensor Networks: An Energy-Efficient Perspective
Trapping Mobile Targets in Wireless Sensor Networks: An Energy-Efficient Perspective
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DOI：
10.1109/tvt.2013.2254732
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发表时间：
2013-03
影响因子：
6.8
通讯作者：
Jiming Chen;Junkun Li;T. Lai
中科院分区：
文献类型：
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作者：
Jiming Chen;Junkun Li;T. Lai
移动的目标检测是无线传感器网络的一个重要应用。事实上，这是相当昂贵的，要求每个部分的感兴趣区域（RoI），以覆盖在一个大规模的无线传感器网络的目标检测。陷阱覆盖已经被提出来权衡传感器部署的成本和传感性能。它限制了目标在不被发现的情况下可以移动的最远距离，而不是提供对该区域的完全覆盖。然而，结果不能直接应用于一个真实的无线传感器网络，因为在实际场景中的传感器的检测模式遵循概率感知模型。此外，陷阱覆盖模型没有考虑目标的各种运动速度，这对捕获目标很重要。为了将移动的目标捕获的概念推广到真实的的大规模无线传感器网络中，本文从理论上分析了传感器网络中移动的目标的检测概率，并定义了概率陷阱覆盖率，它限制检测概率小于阈值的移动的目标的最大位移。提出了圆图理论，该理论可广泛应用于陷阱覆盖、屏障覆盖等入侵检测领域。我们进一步研究了如何调度传感器，以最大限度地提高网络的生命周期，同时保证概率陷阱覆盖的实际问题。针对该问题提出了一种局部化协议，并从理论上分析了该协议的性能。该协议得到的寿命下限接近最优寿命的一半。为了评估我们的设计，我们进行了大量的模拟，比较我们的算法与最先进的解决方案，并证明我们的算法的优越性。
Mobile target detection is a significant application in wireless sensor networks (WSNs). In fact, it is rather expensive to require every part of the region of interest (RoI) to be covered in a large-scale WSN for target detection. Trap coverage has been proposed to trade off between sensing performance and the cost of sensor deployments. It restricts the farthest distance that a target can move without being detected rather than providing full coverage to the region. However, the results cannot be directly applied in a real WSN since the detection pattern of a sensor in practical scenarios follows a probabilistic sensing model. Moreover, the trap coverage model does not consider the various moving speeds of targets, which is important for trapping targets. To extend the concept of mobile target trapping into a real large-scale WSN, we analyze the detection probability of a mobile target in the sensor network theoretically and define probabilistic trap coverage in this paper, which restricts the farthest displacement of a mobile target with a detection probability less than the threshold. We develop the theory of circle graph, which can be generally applied in the area of intrusion detection such as trap coverage and barrier coverage. We further study the practical issue of how to schedule sensors to maximize the lifetime of a network while guaranteeing probabilistic trap coverage. A localized protocol is proposed to solve the problem, and the performance of the protocol is theoretically analyzed. The lower bound of lifetime acquired by the protocol is nearly half the optimum lifetime. To evaluate our design, we perform extensive simulations to compare our algorithm with the state-of-the-art solution and demonstrate the superiority of our algorithm.