Dynamic task assignment for edge computing-enabled vehicular mobile crowdsensing

Vehicular mobile crowdsensing (VMCS) has emerged as powerful and promising paradigm for mobile crowdsensing (MCS) by deploying sensing devices on connected vehicles to collect perception data. Task assignment plays a crucial role in determining the efficiency and performance of in VMCS systems. However, the large number of connected vehicles and sensing tasks involved, coupled with the implication of task information and vehicle status over time, which poses new challenges to system stability. Therefore, this paper puts forth an edge computing-enabled task assignment for VMCS, seeking to enhance the efficiency of task assignment by scheduling a reasonable match between sensing tasks and connected vehicles, and to reduce decision-making delays. Specifically, the task assignment problem is acknowledged as an optimization process with the objective of reducing the tasks waiting duration by considering the task completion sequence rationality under the task time constraint and the energy limitation of the vehicles, and is demonstrated to be an NP-hard problem. Firstly, a valid task sequence is mainly generated for vehicles prior to task assignment to reduce the collision rate between different lists of vehicular tasks. Secondly, this issue is resolved by employing a cooperative game theory-based coalition formation game. On the basis of the introduction of the coalition formation game, the switching rule and historical selection set are used to prevent cyclical coalition switching, and a convergence-guaranteed algorithm with low complexity is proposed to achieve a Nash stable solution. Finally, a thorough set of experiments are performed under various dynamic conditions to show that our suggested scheme is superior to other comparison schemes in terms of task completion percentage, average waiting time, and running time.