Trajectory-based node selection scheme in vehicular crowdsensing

Vehicular crowdsensing has attracted lots of attentions due to its low cost and timeliness for urban sensing applications such as traffic estimation and environment monitoring. It is of great importance for a vehicular crowdsensing system to recruit a limited number of vehicles to achieve a maximum sensing coverage. It is challenging due to the unpredictable behaviors of vehicles. In this paper, an efficient vehicle recruiting scheme is proposed by minimizing the vehicle's trajectory. To simplify the recruiting scheme design, a heuristic algorithm is firstly proposed. Then considering the dynamics of vehicles in the real world, a dynamic threshold based online algorithm is presented. We evaluate the performance of the proposed algorithms through estimating the Urban Heat Island of Shanghai, China in the real word data. The results demonstrate that the proposed algorithms outperform existing algorithms by reducing more than 50% of vehicles needed and improving the estimation accuracy.