Quickest detection in censoring sensor networks

Abstract

The quickest change detection problem is studied in a general context of monitoring a large number of data streams in sensor networks when the “trigger event” may affect different sensors differently. In particular, the occurring event could have an immediate or delayed impact on some unknown, but not necessarily all, sensors. Motivated by censoring sensor networks, scalable detection schemes are developed based on the sum of those local CUSUM statistics that are “large” under either hard thresholding or top-r thresholding rules or both. The proposed schemes are shown to possess certain asymptotic optimality properties.