  In this paper, we study data-aided sensing (DAS) for distributed detection in
wireless sensor networks (WSNs) when sensors' measurements are correlated. In
particular, we derive a node selection criterion based on the J-divergence in
DAS for reliable decision subject to a decision delay constraint. Based on the
proposed J-divergence based DAS, the nodes can be selected to rapidly increase
the log-likelihood ratio (LLR), which leads to a reliable decision with a
smaller number of the sensors that upload measurements for a shorter decision
delay. From simulation results, it is confirmed that the J-divergence based DAS
can provide a reliable decision with a smaller number of sensors compared to
other approaches.
