论文标题
数据辅助检测用于分布式检测
Data-aided Sensing for Distributed Detection
论文作者
论文摘要
在本文中，当传感器的测量值相关时，我们研究了无线传感器网络（WSN）中分布式检测的数据辅助传感（DAS）。特别是，我们根据DAS中的J-Divergence得出一个节点选择标准，以获得可靠的决策，但要受到决策延迟约束。基于提出的基于J-Diverence的DA，可以选择节点以迅速增加对数可能性比率（LLR），这会导致可靠的决策，并具有较小数量的传感器，该传感器上传测量值以较短的决策延迟。从仿真结果中可以证实，与其他方法相比，基于J-Diverence的DA可以用较少数量的传感器提供可靠的决策。
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.
