We propose a quickest change detection problem over sensor networks where both the subset of sensors undergoing a change and the local post-change distributions are unknown. Each sensor in the network observes a local discrete time random process over a finite alphabet. Initially, the observations are independent and identically distributed (i.i.d.) with known pre-change distributions independent from other sensors. At a fixed but unknown change point, a fixed but unknown subset of the sensors undergo a change and start observing samples from an unknown distribution. We assume the change can be quantified using concave (or convex) local statistics over the space of distributions. We propose an asymptotically optimal and computationally tractable stopping time for Lorden's criterion. Under this scenario, our proposed method uses a concave global cumulative sum (CUSUM) statistic at the fusion center and suppresses the most likely false alarms using information projection. Finally, we show some numerical results of the simulation of our algorithm for the problem described.

翻译：我们建议对传感器网络进行快速变化检测,因为正在变化的传感器子集和当地变化后分布都未知。 网络中的每个传感器都观察到一个局部离散时间随机过程, 使用一个限定的字母。 最初, 观测是独立的, 与已知的改变前分布没有其他传感器的相同分布( i. d. ) 。 在一个固定但未知的变化点, 一个固定但未知的传感器子集会发生变化, 并开始从未知的分布中观察样本。 我们假设该变化可以使用分布空间的 concave( or convex) 本地统计数据进行量化。 我们为Lorden 的标准提出一个非同步的最佳和可计算可移动的截停时间。 在此情况下, 我们拟议的方法在聚变中心使用一个相全球累积总和( CUSUM) 统计, 并利用信息预测来抑制最有可能发生的错误警报。 最后, 我们展示了我们模拟所描述的问题的算法的一些数字结果 。