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Sequential detection problems in sensor networks are considered. The truestate of nature/true hypothesis is modeled as a binary random variable $H$ withknown prior distribution. There are $N$ sensors making noisy observations aboutthe hypothesis; $\mathcal{N} =\{1,2,\ldots,N\}$ denotes the set of sensors.Sensor $i$ can receive messages from a subset $\mathcal{P}^i \subset\mathcal{N}$ of sensors and send a message to a subset $\mathcal{C}^i \subset\mathcal{N}$. 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 havereceived, sensor $i$ can decide to stop and communicate a binary decision tothe sensors in $\mathcal{C}^i$, or it can continue taking observations andreceiving messages. After sensor $i$'s binary decision has been sent, itbecomes inactive. Sensors incur operational costs (cost of taking observations,communication costs etc.) while they are active. In addition, the system incursa terminal cost that depends on the true hypothesis $H$, the sensors' binarydecisions and their stopping times. The objective is to determine decisionstrategies for all sensors to minimize the total expected cost.

Signaling in sensor networks for sequential detection