容错分布式最快变化检测
Byzantine Fault Tolerant Distributed Quickest Change Detection
AI总结：
研究在连续和离散时间下，多传感器在存在拜占庭故障时的分布式最快变化检测问题，提出低复杂度分布式方案和集中化分组方案以恢复性能。
摘要：
我们引入并解决了在连续和离散时间设置中，容错分布式最快变化检测的问题。多个传感器依次观察环境中的随机信号并发送观测到控制中心，以确定观测统计行为是否发生变化。假设信号在传感器间独立同分布，未知子集的传感器会发送任意修改甚至人为生成的信号。证明CUSUM统计量在存在单个不诚实传感器时性能显著下降，检测延迟与平均虚警率呈线性关系。为此，提出一个完全分布式低复杂度检测方案，证明其可恢复对数尺度性能，并提出集中化分组方案进一步减少检测延迟。
英文摘要：
We introduce and solve the problem of Byzantine fault tolerant distributed quickest change detection in both continuous and discrete time setups. In this problem, multiple sensors sequentially observe random signals from the environment and send their observations to a control center that will determine whether there is a change in the statistical behavior of the observations. We assume that the signals are independent and identically distributed across sensors. An unknown subset of sensors are compromised and will send arbitrarily modified and even artificially generated signals to the control center. It is shown that the performance of the the so-called CUSUM statistic, which is optimal when all sensors are honest, will be significantly degraded in the presence of even a single dishonest sensor. In particular, instead of in a logarithmically the detection delay grows linearly with the average run length (ARL) to false alarm. To mitigate such a performance degradation, we propose a fully distributed low complexity detection scheme. We show that the proposed scheme can recover the log scaling. We also propose a centralized group-wise scheme that can further reduce the detection delay.
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