In many modern applications, large-scale sensor networks are used to perform statistical inference tasks. In this article, we propose Bayesian methods for multiple change-point detection using a sensor network in which a fusion center (FC) can receive a data stream from each sensor. Due to communication limitations, the FC monitors only a subset of the sensors at each time slot. Since the number of change points can be high, we adopt the false discovery rate (FDR) criterion for controlling the rate of false alarms, while aiming to minimize the average detection delay (ADD) and the average number of observations (ANO) communicated until discovery. We propose two Bayesian detection procedures that handle the communication limitations by monitoring the subset of the sensors with the highest posterior probabilities of change points having occurred. This monitoring policy aims to minimize the delay between the occurrence of each change point and its declaration using the corresponding posterior probabilities. One of the proposed procedures is more conservative than the second one in terms of having lower FDR at the expense of higher ADD. It is analytically shown that both procedures control the FDR under a specified tolerated level and are also scalable in the sense that they attain ADD and ANO that do not increase asymptotically with the number of sensors. In addition, it is demonstrated that the proposed detection procedures are useful for trading off between reduced ADD and reduced ANO. Numerical simulations are conducted for validating the analytical results and for demonstrating the properties of the proposed procedures.

中文翻译：

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用于减少通信的多变化点检测的贝叶斯方法

在许多现代应用中，大规模传感器网络用于执行统计推理任务。在本文中，我们提出了使用传感器网络进行多变点检测的贝叶斯方法，其中融合中心 (FC) 可以从每个传感器接收数据流。由于通信限制，FC 在每个时隙仅监视传感器的一个子集。由于变化点的数量可能很高，我们采用错误发现率 (FDR) 准则来控制误报率，同时旨在最小化平均检测延迟 (ADD) 和平均观察次数 (ANO)，直到发现。我们提出了两个贝叶斯检测程序，它们通过监视具有最高后验概率发生变化点的传感器子集来处理通信限制。该监控策略旨在使用相应的后验概率最小化每个变化点的发生与其声明之间的延迟。在以较高的 ADD 为代价降低 FDR 方面，提议的程序之一比第二个更保守。分析表明，这两个程序都将 FDR 控制在指定的容忍水平下，并且在它们获得不随传感器数量渐近增加的 ADD 和 ANO 的意义上也是可扩展的。此外，证明了所提出的检测程序可用于在减少的 ADD 和减少的 ANO 之间进行权衡。进行数值模拟以验证分析结果并证明所提出程序的特性。