An Iterative Method of Sensor Calibration in Participatory Sensing Network

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, sensors on smart phones are prone to the unknown measurement errors, requiring automatical calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by EM (Expectation Maximization) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. Our algorithm can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized.