Profit-Maximizing Stochastic Control for Mobile Crowd Sensing Platforms

In this paper, we consider the crucial problem of maximizing the profit of a crowd sensing platform which receives sensing requests from various subscribers and completes the requests by leveraging sensing time of participating smartphones. The profit of the platform is defined as the total charges of sensing requests minus the payments to smartphones. It is highly challenging to obtain the maximal profit for the platform, because of stochastic arrivals of sensing requests, dynamic participation of smartphones, and high complexity of optimally allocating requests to smartphones. In response to the challenges, we propose an optimal online control framework which can efficiently utilize the limited sensing time on each smartphone. Based on the stochastic Lyapunov optimization techniques combined with the idea of weight perturbation, our control framework makes crucial online control decisions, such as sensing requests admission and dispatching control, sensing time purchasing control and sensing time allocation control, without requiring any future knowledge about request arrivals. Rigorous mathematical analysis and comprehensive simulation results show that our control framework can achieve a time averaged profit that is arbitrarily close to the optimum, while still maintaining strong system stability.