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Strengthening barrier-coverage of static sensor network with mobile sensor nodes

机译:使用移动传感器节点增强静态传感器网络的障碍覆盖范围

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A wireless sensor network (WSN) provides a barrier-coverage over an area of interest if no intruder can enter the area without being detected by the WSN. Recently, barrier-coverage model has received lots of attentions. In reality, sensor nodes are subject to fail to detect objects within its sensing range due to many reasons, and thus such a barrier of sensors may have temporal loopholes. In case of the WSN for border surveillance applications, it is reasonable to assume that the intruders are smart enough to identify such loopholes of the barrier to penetrate. Once a loophole is found, the other intruders have a good chance to use it continuously until the known path turns out to be insecure due to the increased security. In this paper, we investigate the potential of mobile sensor nodes such as unmanned aerial vehicles and human patrols to fortify the barrier-coverage quality of a WSN of cheap and static sensor nodes. For this purpose, we first use a single variable first-order grey model, GM(1,1), based on the intruder detection history from the sensor nodes to determine which parts of the barrier is more vulnerable. Then, we relocate the available mobile sensor nodes to the identified vulnerable parts of the barrier in a timely manner, and prove this relocation strategy is optimal. Throughout the simulations, we evaluate the effectiveness of our algorithm.
机译:如果没有入侵者无法被WSN检测到,则无线传感器网络(WSN)可以在目标区域内提供障碍物覆盖。近年来,障碍物覆盖模型受到了广泛关注。实际上,由于许多原因,传感器节点可能无法检测到其感测范围内的物体,因此,这种传感器屏障可能会存在时间漏洞。对于边界监视应用的WSN,可以合理地假设入侵者足够聪明,可以识别出要穿透的障碍物的这些漏洞。一旦发现漏洞,其他入侵者就有很好的机会连续使用它,直到由于安全性提高而使已知路径变得不安全为止。在本文中,我们研究了无人飞行器和人类巡逻等移动传感器节点增强廉价和静态传感器节点WSN的障碍物覆盖质量的潜力。为此,我们首先根据来自传感器节点的入侵者检测历史,使用单个变量一阶灰色模型GM(1,1),以确定障碍物的哪个部分更容易受到攻击。然后,我们将可用的移动传感器节点及时地重新定位到已识别的障碍的脆弱部分,并证明这种重新定位策略是最佳的。在整个仿真过程中,我们评估了算法的有效性。

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