Data-aided Sensing for Distributed Detection

• URL: http://arxiv.org/abs/2011.08393v1
• Date: Tue, 17 Nov 2020 03:15:44 GMT
• Title: Data-aided Sensing for Distributed Detection
• Authors: Jinho Choi
• Abstract summary: 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) It is confirmed that the J-divergence based DAS can provide a reliable decision with a smaller number of sensors compared to other approaches.
• Score: 48.523643863141466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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