System-level Calibration for Data Fusion in Wireless Sensor Networks

Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of cali-bration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate- to large-scale networks. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing perfor-mance. In the first tier of our calibration approach, 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 calibration compared with the device-level approach. 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. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive sim-ulations based on synthetic data sets as well as data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in the scenarios with low signal-to-noise ratios