Rui Tan, Zhaohui Yuan, Guoliang Xing, Xue Liu, Jianguo Yao
February, 2009
Systematic biases in sensor measurements undermine the performance of wireless sensor networks in mission-critical applications such as target detection and tracking. Traditional device-level calibration approaches become intractable for moderate to large-scale networks due to limited access of individual sensors after deployment. In this paper, we propose a two-tier {\em system-level} calibration approach for a class of sensor networks that employ data fusion to improve the overall system performance. In the first tier, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of system-level calibration. Based on this approach, we develop an {\em optimal} model calibration scheme that {\em maximizes} the target detection probability of a sensor network under bounded false alarm rate. The simulations based on synthetic data as well as real data traces collected by $18$ sensors show that our system-level calibration scheme can improve the detection performance of a sensor network by up to $50\%$.
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