|Stochastic Systems Group|
A promising feature of emerging wireless sensor networks is the opportunity for each node to process data about "locally" sensed activity and then communicate relevant information output, altogether in a manner that supports "globally" effective decision-making. We consider a global objective of solving problems of detection, assuming each sensor node receives noisy measurements directly related to only its local environment. Within a usual Bayesian formulation, we present a simple two-node example for which our analysis exposes a fundamental tradeoff between costs due to decision errors and those due to communication overhead. Not surprisingly, this tradeoff is especially apparent when the activity local to one node is strongly correlated with activity local to the other node. It is easily argued that this tradeoff persists in networks with large numbers of sensor nodes, but we reveal that certain implicit assumptions underlying our otherwise appealing, two-node analysis can become impractical. We close with some ideas for future work, where the goal is to satisfy a more applicable set of assumptions for large sensor networks yet retain a quantifiable performance/communication tradeoff for distributed detection problems.
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