Collaborative calibration and sensor placement for mobile sensor networks

Mobile sensing systems carried by individuals or machines make it possible to measure position- and time-dependent environmental conditions, such as air quality and radiation. The low-cost, miniature sensors commonly used in these systems are prone to measurement drift, requiring occasional re-calibration to provide accurate data. Requiring end users to periodically do manual calibration work would make many mobile sensing systems impractical. We therefore argue for the use of collaborative, automatic calibration among nearby mobile sensors, and provide solutions to the drift estimation and placement problems posed by such a system. Collaborative calibration opportunistically uses interactions among sensors to adjust their calibration functions and error estimates. We use measured sensor drift data to determine properties of time-varying drift error. We then develop (1) both optimal and heuristic algorithms that use information from multiple collaborative calibration events for error compensation and (2) algorithms for stationary sensor placement, which can further decrease system-wide drift error in a mobile, personal sensing system. We evaluated the proposed techniques using real-world and synthesized human motion traces. The most advanced existing work has 23.2% average sensing error, while our collaborative calibration technique reduces the error to 2.2%. The appropriate placement of accurate stationary sensors can further reduce this error.