In this paper, we propose a new technique for the enhancement of target detection in Wireless Sensor Networks (WSNs) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. We introduce the algorithm; Fail Silent Pair (FSP) to calculate global decision in the fusion center. The FSP algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. Also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. The detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. While the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. We introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique.

中文翻译：

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使用对计数和伪数据包减少WSN中相关恶意传感器节点的影响

在本文中，我们提出了一种用于增强无线传感器网络（WSN）中目标检测的新技术，其中传感器节点负责对有关给定目标的存在或不存在做出二进制决策，并将输出报告给融合中心。我们介绍算法；失败沉默对（FSP），以在融合中心计算全局决策。FSP算法将所有传感器节点随机分布成对，然后考虑同一局部决策的对。此外，我们提出了新的检测和预防方法，以减少依赖的恶意传感器节点的影响。该检测方法基于对带有假包的可疑传感器节点的欺骗，以检测恶意传感器节点的子集，这些节点窃听其他传感器节点数据包时，会将其本地决策用作构建智能决策的参考。预防方法允许融合中心使用已确定的策略纠正某些恶意传感器节点的本地决策，从而有助于提高全局决策的准确性。除了模拟实验以验证我们的技术外，我们还提供数学分析来验证我们的方法。