LAUSR

The ubiquitous mobile portable devices have accelerated the rise of mobile crowdsensing (MCS), a distributed perception paradigm. In MCS, multiple requesters issue their sensing tasks on a server platform, and then the platform distributes the tasks to multiple workers. In this process, requesters typically specify the tasks and requirements; meanwhile, the needed task pricing to workers is also specified, which is used to offsetting worker’s efforts by completing the tasks. However, for different application scenarios, the task requirements, the time periods, and the resource consumptions for completing the tasks are varied, which results in a challenge to raise an appropriate task pricing to diverse workers. Therefore, we study the dynamic task pricing problem in the MCS network system with diverse factors (e.g., multiple requester queueing competitions, dynamic task requirements, and distinct waiting time costs). To solve the problem, we resort to the theory of Age of Information (AoI). Specifically, we leverage the AoI timeliness metric in modeling the requester’s waiting time costs, and then we use queueing game theory to build the dynamic task pricing model. The model analysis has shown the existence of the optimal pricing strategies under the first-come–first-served (FCFS) queueing rule and the last-come–first-served with preemptive in waiting (LCFSW) queueing rule. Finally, numerical simulations are conducted to validate the existence of the optimal task pricing.