Data-aided Sensing for Distributed Detection
This addresses the challenge of efficient and reliable detection in resource-constrained wireless sensor networks, representing an incremental improvement over existing methods.
The paper tackles the problem of distributed detection in wireless sensor networks with correlated measurements by proposing a data-aided sensing method based on J-divergence for node selection, resulting in reliable decisions with fewer sensors and shorter delays, as confirmed by simulations showing it outperforms other approaches.
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.