  In this paper, we consider the problem of quickest change point detection and
identification over a linear array of $N$ sensors, where the change pattern
could first reach any of these sensors, and then propagate to the other
sensors. Our goal is not only to detect the presence of such a change as
quickly as possible, but also to identify which sensor that the change pattern
first reaches. We jointly design two decision rules: a stopping rule, which
determines when we should stop sampling and claim a change occurred, and a
terminal decision rule, which decides which sensor that the change pattern
reaches first, with the objective to strike a balance among the detection
delay, the false alarm probability, and the false identification probability.
We show that this problem can be converted to a Markov optimal stopping time
problem, from which some technical tools could be borrowed. Furthermore, to
avoid the high implementation complexity issue of the optimal rules, we develop
a scheme with a much simpler structure and certain performance guarantee.
