Mobile Calibration for Bus-Based Urban Sensing

Abstract

In bus-based sensing, public transport serves as a mobile urban sensing platform. While offering much higher geographical coverage, the low-cost sensors mounted on vehicles can be less accurate and demand more frequent calibration, which may be challenging for large vehicles fleets. As calibration is performed by relating mobile sensor readings to those of fixed reference stations, the placement of reference stations is very important. In this work, we propose an algorithm for computing the optimal reference stations locations to maximize the sensing coverage. Contrary to prior work, the coverage is defined in terms of geographical area , extending a certain distance away from the route trajectory representing the actual sensing capacity of the vehicles. The proposed algorithm computes it using geographical set operations, such as spatial join and subtraction to compute the unique contribution of each bus route. We evaluate the approach using real bus trajectories from Manhattan, US and compare it with a random baseline and prior work. The results indicate that the given the bus routes, a complete sensing coverage can be achieved using a single reference station with a maximum of 2-hop calibration path