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arXiv 1105.6065cs.NI
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Ad Hoc无线传感器网络中的时延最优事件检测

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

Premkumar Karumbu, Venkata K. Prasanthi M., Anurag Kumar

备注：
To appear in ACM Transactions on Sensor Networks. A part of this work was presented in IEEE SECON 2006, and Allerton 2010
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摘要：

我们考虑了一个小范围的传感器网络的事件检测，在该网络中，节点定期采样，然后竞争在一个{ \em随机接入网络}发送其测量数据包的融合中心。我们考虑在融合中心的两个程序来处理测量。假设贝叶斯设置;即，融合中心具有关于改变时间的先验分布。在第一个过程中，融合中心处的决策算法是\emph{network-oblivious}，并且仅当在采样时刻进行的测量的完整矢量可用时才做出决策。在第二个过程中，融合中心的决策算法是\emph{network-aware}，并在测量值到达时处理测量值，但按时间因果顺序进行处理。在这种情况下，决策统计也取决于网络延迟，而在网络无关的情况下，决策统计不取决于网络延迟。这产生了贝叶斯变化检测问题，在随机网络延迟和决策延迟之间进行权衡;较高的采样率减少了决策延迟，但增加了随机访问延迟。在周期采样下，在网络不经意的情况下，最优停止规则的结构与无网络时相同，最优变化检测时延分解为网络时延和无网络时的最优判决时延.在网络感知的情况下，最优停止问题被分析为一个部分可观察的马尔可夫决策过程，其中需要保持网络中的队列和延迟的状态。一个足够的统计决策被发现是网络状态和后验概率的变化已经发生给定的测量接收和网络的状态。最佳制度进行了研究，使用模拟。

英文摘要：

We consider a small extent sensor network for event detection, in which nodes take samples periodically and then contend over a {\em random access network} to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center to process the measurements. The Bayesian setting is assumed; i.e., the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is \emph{network-oblivious} and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is \emph{network-aware} and processes measurements as they arrive, but in a time causal order. In this case, the decision statistic depends on the network delays as well, whereas in the network-oblivious case, the decision statistic does not depend on the network delays. This yields a Bayesian change detection problem with a tradeoff between the random network delay and the decision delay; a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network--oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network--aware case, the optimal stopping problem is analysed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient statistic for decision is found to be the network-state and the posterior probability of change having occurred given the measurements received and the state of the network. The optimal regimes are studied using simulation.

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