System-Level Calibration for Fusion-Based 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 calibration 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, the authors propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance.