System-level calibration for data fusion in wireless sensor networks

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 in-tractable for moderate to large-scale networks due to limited access of individual sensors after deployment. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to im-prove the overall system performance. In the first tier, each sensor learns its local sensing model from noisy measure-ments using an online algorithm and only transmits a few model parameters. In the second tier, sensors ’ local sens-ing models are then calibrated to a common system sensing model. Our approach fairly distributes computation over-head among sensors and significantly reduces the communi-cation overhead of system-level calibration. Based on this approach, we develop an optimal model calibration scheme that 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%.