Incentive-Driven Vehicle Selection and Scheduling Method in Vehicular Crowdsensing

Vehicular crowdsensing aims to utilize a large number of on-board sensors and computing resources in intelligent vehicles to collect sensing data in a series of areas. Currently, sensing vehicles are usually divided into opportunistic vehicles and participatory vehicles, according to whether the vehicle trajectory can be changed. Here, opportunistic vehicles are with fixed trajectories and cannot be changed. The trajectories of participatory vehicles can be changed based on actual needs. Therefore, selecting the appropriate opportunistic vehicle to complete the sensing task and planning the trajectory of the participating vehicle is a challenging research problem. This paper takes the different mobility characteristics of the two types of sensing vehicles as the starting point, and manages the two types of vehicles through the crowd sensing platform (CSP). Then, different problems are solved for opportunistic and participatory vehicles, respectively. Specifically, for opportunistic vehicles, it is necessary to select an appropriate vehicle set to complete the sensing task and minimize the cost of CSP. To solve this problem, this paper proposes a reverse auction-based incentive mechanism to select the minimum cost vehicle set to complete the sensing task. It mainly includes two stages: winning vehicle selection and reward payment. Meanwhile, this paper verifies that the proposed method can ensure individual rationality and authenticity. For participating vehicles, it is necessary to plan the trajectory of each vehicle through CSP scheduling, perform sensing tasks and minimize the cost of CSP. To solve this problem, this paper proposes a deep reinforcement learning-based method to schedule vehicle trajectories and assign different sensing tasks to vehicles. In addition, while minimizing CSP overhead, this paper also considers the fairness issue of sensing task. The sensing fairness index is introduced to ensure the balance of completion of sensing tasks in different sub-regions. Finally, extensive evaluations based on real-world datasets show that the proposed method performs well and outperforms other benchmark schemes.