Data-Efficient Quickest Change Detection in Sensor Networks
Data-Efficient Quickest Change Detection in Sensor Networks
复制标题
传感器网络中数据高效、最快的变化检测
DOI：
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复制
发表时间：
2014
影响因子：
5.4
通讯作者：
V. Veeravalli
中科院分区：
文献类型：
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作者：
T. Banerjee;V. Veeravalli
考虑一个传感器网络，其中在每个传感器上观察到一系列随机变量。在每一个时间步骤中，经过处理的观测结果从传感器传送到一个称为融合中心的公共节点。在某个未知的时间点上，传感器节点的未知子集上的观测分布发生了变化。目标是在虚警率、每个传感器的观测成本以及传感器与融合中心之间的通信成本约束下，尽可能快地检测到分布的变化。针对上述问题提出了极大极小公式，并提出了分布式算法，在每个传感器上使用开关观测控制和审查来满足数据约束。结果表明，当虚警率趋于零时，所提出的算法对于所提出的公式是渐近最优的。所提出的算法的渐近最优性意味着，与使用所有观测值进行决策的方案相比，可以跳过任意但固定的数据部分而不会对渐近性能造成任何损失。通过数值研究还表明，所提出的算法明显优于基于分数抽样的算法，其中使用文献中的经典算法，并且通过确定性或随机跳过固定分数的观测值来满足对观测值成本的约束，独立于观测过程。
A sensor network is considered where at each sensor a sequence of random variables is observed. At each time step, a processed version of the observations is transmitted from the sensors to a common node called the fusion center. At some unknown point in time the distribution of observations at an unknown subset of the sensor nodes changes. The objective is to detect the change in distribution as quickly as possible, subject to constraints on the false alarm rate, the cost of observations taken at each sensor, and the cost of communication between the sensors and the fusion center. Minimax formulations are proposed for the above problem and distributed algorithms are proposed in which on-off observation control and censoring is used at each sensor to meet the constraints on data. It is shown that the proposed algorithms are asymptotically optimal for the proposed formulations, as the false alarm rate goes to zero. The asymptotic optimality of the proposed algorithms implies that an arbitrary but fixed fraction of data can be skipped without any loss in asymptotic performance as compared to the scheme where all the observations are used for decision making. It is also shown, via numerical studies, that the proposed algorithms perform significantly better than those based on fractional sampling, in which the classical algorithms from the literature are used and the constraint on the cost of observations is met by skipping a fixed fraction of observations either deterministically or randomly, independent of the observation process.