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WIRELESS SENSOR NETWORKS FOR SECURITY: ISSUES AND CHALLENGES

机译:无线传感器网络的安全性:问题和挑战

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摘要

In this chapter, the sensing coverage area of surveillance wireless sensor networks is considered. The sensing coverage is determined by applying Neyman-Pearson detection and defining the breach probability on a grid-modeled field. Using a graph model for the perimeter, Dijkstra's shortest path algorithm is used to find the weakest breach path. The breach probability is linked to parameters such as the false alarm rate, size of the data record and the signal-to-noise ratio. Consequently, the required number of sensor nodes and the surveillance performance of the network are determined. For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the present energy constraints of these small sensing and wireless communicating devices, a. common trend is to put some of them into a dormant state. We adopt a mutual information based metric to select the most informative subset of the sensors to achieve reduction in the energy consumption, while preserving the desired accuracies of the target position estimation.
机译:在本章中,将考虑监视无线传感器网络的感测覆盖范围。通过应用Neyman-Pearson检测并在网格建模的字段上定义突破概率来确定感测范围。 Dijkstra的最短路径算法使用图形模型作为周界,以找到最弱的违规路径。违反概率与诸如误报率,数据记录大小和信噪比之类的参数相关联。因此,确定了所需的传感器节点数和网络的监视性能。对于目标跟踪应用,小型无线传感器可以提供精确的信息,因为它们可以在现象附近部署和操作。这些传感设备有机会相互协作,以改善目标定位和跟踪精度。分布式数据融合体系结构提供了协作跟踪框架。由于这些小型感测和无线通信设备的当前能量限制,普遍的趋势是使其中一些处于休眠状态。我们采用基于互信息的度量标准来选择传感器中信息最丰富的子集,以实现能耗的降低,同时保留目标位置估计的所需精度。

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