  In many modern applications, large-scale sensor networks are used to perform
statistical inference tasks. In this paper, we propose Bayesian methods for
multiple change-point detection using a sensor network in which a fusion center
(FC) can receive a data stream from each sensor. Due to communication
limitations, the FC monitors only a subset of the sensors at each time slot.
Since the number of change points can be high, we adopt the false discovery
rate (FDR) criterion for controlling the rate of false alarms, while minimizing
the average detection delay (ADD). We propose two Bayesian detection procedures
that handle the communication limitations by monitoring the subset of the
sensors with the highest posterior probabilities of change points having
occurred. This monitoring policy aims to minimize the delay between the
occurrence of each change point and its declaration using the corresponding
posterior probabilities. One of the proposed procedures is more conservative
than the second one in terms of having lower FDR at the expense of higher ADD.
It is analytically shown that both procedures control the FDR under a specified
tolerated level and are also scalable in the sense that they attain an ADD that
does not increase asymptotically with the number of sensors. In addition, it is
demonstrated that the proposed detection procedures are useful for trading off
between reduced ADD and reduced average number of observations drawn until
discovery. Numerical simulations are conducted for validating the analytical
results and for demonstrating the properties of the proposed procedures.
