Mutual Information Maximization-Based Collaborative Data Collection With Calibration Constraint

People pay greater attention to air quality which is closely related to their health, especially in developing countries. Air quality is a part of the Chinese weather forecast, and the government has developed air quality monitoring systems and built high-quality monitoring stations (HQMS). With the data from HQMS, many companies and research institutes demonstrate an accurate air pollution map on the Internet, which is valuable for many issues related to air quality, including exposure modeling and urban planning. Due to the high equipment and operating costs, the distribution of HQMS is too uneven and sparse to achieve a high-resolution air pollution map. Thus, people try to deploy a large number of high precision sensors in and around the city for detecting air pollution. However, these sensors require frequent calibrations with the HQMS to maintain data accuracy. On the other side, to reduce the cost of sensor deployment, people begin to use mobile sensors instead of fixed sensors, which make sensor route planning a very important issue. Nevertheless, existing works on the route planning of mobile sensors largely focus on data reconstruction, which either ignores calibration or views it as an independent problem. In this paper, we propose a novel scheme to improve the accuracy of data reconstruction, which jointly considers sensor calibration and data reconstruction in route planning for mobile sensors. We formulate a novel sensor route planning problem (SRPP) that aims to maximize the mutual information and guarantee the accuracy of measurements through the sensor calibration. We also propose a heuristic algorithm to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. The extensive simulation results well justify the effectiveness of our approach that can reduce 83% root mean square error on average compared with the traditional approach.