Maximizing Spatial-Temporal Coverage In Mobile Crowd-Sensing Based On Public Transports With Predictable Trajectory

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial-temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.