Accurate Quantification of Sensor Noise in Participatory Sensing Network
Chaocan Xiang, Panlong Yang, Chang Tian, Changzheng Li, Qingyu Li and Xiangyang Li
In the participatory sensing network, the sensor noise dominates the quality of sensing data as well as the processing efficiency. Previous works focus on evaluating sensing accuracy with expectations, and fail to accurately quantify the sensor noise with uncertainty. In this paper, we propose FSP (Feeling Sensors’ Pulse) method, which quantifies the sensor noise using the confidence interval. Specifically, we first use EM (Expectation Maximization) based iterative estimation algorithm to compute the maximum likelihood estimation (MLE) of sensor noise. Second, on the basis of these estimations, we leverage the asymptotic normality of MLE and the Fisher information to compute the confidence interval. The extensive simulations show that, FSP can achieve 90% success rate where the true values of sensor noise fall into the 95% confidence interval, at the cost of the polynomial time complexity only.
Keywords: Participatory sensing, sensor noise quantification, confidence interval.