  A sensor network is considered where a sequence of random variables is
observed at each sensor. At each time step, a processed version of the
observations is transmitted from the sensors to a common node called the fusion
center. At some unknown point in time the distribution of the observations at
all the sensor nodes changes. The objective is to detect this change in
distribution as quickly as possible, subject to constraints on the false alarm
rate and the cost of observations taken at each sensor. Minimax problem
formulations are proposed for the above problem. A data-efficient algorithm is
proposed in which an adaptive sampling strategy is used at each sensor to
control the cost of observations used before change. To conserve the cost of
communication an occasional binary digit is transmitted from each sensor to the
fusion center. It is shown that the proposed algorithm is globally
asymptotically optimal for the proposed formulations, as the false alarm rate
goes to zero.
