Mutual Information Maximization for Collaborative Mobile Sensing with Calibration Constraint

Highly resolved and accurate air pollution maps are valuable resources for many issues related to air quality including exposure modeling and urban planning. Due to the high equipment costs, there are limited high quality monitoring stations (HQMS) in cities. In order to achieve high resolution air pollution maps, a large number of mobile sensors are required. Besides, mobile sensors require frequent calibrations with the HQMS to maintain data accuracy. Existing work on route design for mobile sensors largely focuses on data reconstruction, which either ignores calibration or views it as an independent problem. To improve the accuracy of data reconstruction, this paper proposes a novel scheme that jointly considers sensor calibration and data reconstruction in route design for mobile sensors. We formulate a novel sensor route planning problem (SRPP) which aims to maximize the mutual information and guarantee the accuracy of measurements through sensor calibration. A heuristic algorithm is proposed to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. Simulation results show that, compared with traditional approach, our approach can reduce 83% root mean square error (RMSE) on average.