Optimal Quickest Change Detection in Sensor Networks Using Ordered Transmissions

Quickest change detection in a sensor network is
considered where each sensor observes a sequence of random
variables and transmits its local information on the observations
to a fusion center. At an unknown point in time, the distribution
of the observations at all sensors changes. The objective is to
detect the change in distribution as soon as possible, subject to
a false alarm constraint. We consider minimax formulations for
this problem and propose a new approach where transmissions at
each time slot are ordered and halted when sufficient information
is accumulated at the fusion center. We show that the proposed
approach can achieve the optimal performance equivalent to the
centralized cumulative sum (CUSUM) algorithm while requiring
fewer sensor transmissions. Numerical results for a shift in
mean with independent and identically distributed Gaussian
observations show significant communication savings for the case
where the change seldom occurs which is frequently true in many
important applications.