Distributed Detection by Networked Sensors Receiving Local Measurements
from Correlated Environments
O. Patrick Kreidl
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.