Stackelberg game based dynamic admission and scheduling in mobile crowdsensing

Abstract

Mobile crowdsensing is a crowdsourcing-based paradigm, where the platform executes the sensing requests with the help of many common peoples’ handheld devices (typically smartphones). In this paper, we mainly address the dynamic sensing request admission and smartphone scheduling problem to maximize the long-term profit, taking into account the competitive interaction procedure between the platform and smartphones, the queue backlog, and the location of sensing requests and smartphones. First, formulate this problem as a discrete time model and the interaction procedure between the platform and smartphones as a Stackelberg game. Then, we introduce the Lyapunov optimization technique and design a Stackelberg game based dynamic Admission and Scheduling algorithm(SAS). In SAS, all control decisions are made only based on the currently available information and none of the stakeholders, including the platform and smartphones, can improve his utility by unilaterally changing its current strategy. Next, we design an online Cooperative dynamic Admission and Scheduling algorithm(CAS) for the situation where the platform and smartphones work in a cooperative way. Theoretical analysis shows that under any control parameter V > 0, both SAS and CAS algorithm can achieve O(1/V)-optimal average profit while the sensing request backlog is bounded by O(V). The extensive numerical results based on both synthetic and real trace demonstrate the Stackelberg equilibrium of the SAS. The CAS always outperforms SAS, and in some certain situations, the profit of SAS is very close to that of the CAS.