Abstract The quickest change detection problem is studied in a general context of monitoring a large number K of data streams in sensor networks when the “trigger event” may affect different sensors differently. In particular, the occurring event might affect some unknown, but not necessarily all, sensors and also could have an immediate or delayed impact on those affected sensors. Motivated by censoring sensor networks, we develop scalable communication-efficient schemes based on the sum of those local cumulative sum (CUSUM) statistics that are “large” under either hard, soft, or order thresholding rules. Moreover, we provide the detection delay analysis of these communication-efficient schemes in the context of monitoring K independent data streams and establish their asymptotic statistical properties under two regimes: one is the classical asymptotic regime when the dimension K is fixed, and the other is the modern asymptotic regime when the dimension K goes to Our theoretical results illustrate the deep connections between communication efficiency and statistical efficiency.

中文翻译：

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传感器网络中通信高效最快检测方案的渐近统计特性

摘要 在“触发事件”可能对不同传感器产生不同影响时，在监测传感器网络中大量 K 数据流的一般环境中研究了最快变化检测问题。特别是，发生的事件可能会影响一些未知但不一定是全部的传感器，并且还可能对那些受影响的传感器产生直接或延迟的影响。受审查传感器网络的启发，我们基于那些在硬、软或顺序阈值规则下“大”的本地累积总和 (CUSUM) 统计数据的总和，开发了可扩展的高效通信方案。此外，我们在监控 K 个独立数据流的背景下提供了这些通信高效方案的检测延迟分析，并在两种机制下建立了它们的渐近统计特性：