Predictive Recruitment in Vehicular Crowdsensing Based on Spatial Sensing Strength Analysis Method

Vehicular crowdsensing is an efficient method of data collection in cities, benefiting from the powerful moving and sensing capabilities of intelligent vehicles. Modeling sensing capacities of vehicles and analyzing sensing effect based on sensor setup schemes are crucial in vehicular crowdsensing system. If we can infer the sensing strength of vehicles towards surrounding spatial points in advance, it would facilitate the determination of which vehicles to recruit for uploading sensing data, thereby achieving uniform and wide sensing coverage. However, the sensing strength of vehicles is not considered in the current research due to the diversity of sensors deployed on vehicles and the complexity of spatial relationships among vehicles in actual traffic scenarios. Therefore, this paper proposes a predictive vehicle recruitment method based on spatial sensing strength analysis. First, a fine-grained method is proposed to analyze the sensing strength of vehicles with different sensor setup schemes at various points in space. We fuse the sensing results of multiple vehicles and design novel metrics to evaluate the overall sensing effect, which comprehensively consider sensing strength, uniformity, and coverage ratio. The proposed method innovatively combines the perceptual properties of actual sensors and the complex spatiotemporal relationships among vehicles. Then, to guarantee the timeliness of recruiting vehicles, we distinguish moving modes to predict vehicle movements and further obtain the spatial sensing strength of vehicles at a specific moment in the future. Furthermore, combined with the sensing and communicating capabilities of the roadside infrastructure, the vehicle recruitment problem is described as an optimization problem under multiple practical constraints. To address this problem, an online heuristic algorithm is proposed based on the predicted vehicles' sensing strength. Finally, we conduct extensive simulations based on a real dataset to visualize vehicle sensing effect and verify the superiority of the proposed scheme.