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A factor graph approach to link loss monitoring in wireless sensor networks

机译:一种用于无线传感器网络中链路丢失监视的因子图方法

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摘要

The highly stochastic nature of wireless environments makes it desirable to monitor link loss rates in wireless sensor networks. In a wireless sensor network, link loss monitoring is particularly supported by the data aggregation communication paradigm of network traffic: the data collecting node can infer link loss rates on all links in the network by exploiting whether packets from various sensors are received, and there is no need to actively inject probing packets for inference purposes. In this paper, we present a low complexity algorithmic framework for link loss monitoring based on the recent modeling and computational methodology of factor graphs. The proposed algorithm iteratively updates the estimates of link losses upon receiving (or detecting the loss of) recently sent packets by the sensors. The algorithm exhibits good performance and scalability, and can be easily adapted to different statistical models of networking scenarios. In particular, due to its low complexity, the algorithm is particularly suitable as a long-term monitoring facility.
机译:无线环境的高度随机性使得需要监视无线传感器网络中的链路丢失率。在无线传感器网络中,网络流量的数据聚合通信范例特别支持链路丢失监视:数据收集节点可以通过利用是否接收到来自各种传感器的数据包来推断网络中所有链路的链路丢失率。无需出于推理目的而主动注入探测数据包。在本文中,我们基于因子图的最新建模和计算方法,提出了一种用于链路丢失监视的低复杂度算法框架。所提出的算法在接收到传感器最近发送的数据包(或检测其丢失)后,迭代更新链路损耗的估计值。该算法表现出良好的性能和可伸缩性,并且可以轻松地适应网络场景的不同统计模型。特别地,由于其低复杂度,该算法特别适合作为长期监视工具。

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