  The problem of quickest change detection with communication rate constraints
is studied. A network of wireless sensors with limited computation capability
monitors the environment and sends observations to a fusion center via wireless
channels. At an unknown time instant, the distributions of observations at all
the sensor nodes change simultaneously. Due to limited energy, the sensors
cannot transmit at all the time instants. The objective is to detect the change
at the fusion center as quickly as possible, subject to constraints on false
detection and average communication rate between the sensors and the fusion
center. A minimax formulation is proposed. The cumulative sum (CuSum) algorithm
is used at the fusion center and censoring strategies are used at the sensor
nodes. The censoring strategies, which are adaptive to the CuSum statistic, are
fed back by the fusion center. The sensors only send observations that fall
into prescribed sets to the fusion center. This CuSum adaptive censoring
(CuSum-AC) algorithm is proved to be an equalizer rule and to be globally
asymptotically optimal for any positive communication rate constraint, as the
average run length to false alarm goes to infinity. It is also shown, by
numerical examples, that the CuSum-AC algorithm provides a suitable trade-off
between the detection performance and the communication rate.
