Signaling in sensor networks for sequential detection

Sequential detection problems in sensor networks are considered. The true state of nature/true hypothesis is modeled as a binary random variable H with known prior distribution. There are N sensors making noisy observations about the hypothesis; N =1,2,ldots,N denotes the set of sensors. Sensor i can receive messages from a subset Pi subset N of sensors and send a message to a subset Ci subset N. Each sensor is faced with a stopping problem. At each time t, based on the observations it has taken so far and the messages it may have received, sensor i can decide to stop and communicate a binary decision to the sensors in Ci, or it can continue taking observations and receiving messages. After sensor i's binary decision has been sent, it becomes inactive. Sensors incur operational costs (cost of taking observations, communication costs etc.) while they are active. In addition, the system incurs a terminal cost that depends on the true hypothesis H, the sensors' binary decisions and their stopping times. The objective is to determine decision strategies for all sensors to minimize the total expected cost.