Three-Stage Stackelberg Long-Term Incentive Mechanism and Monetization for Mobile Crowdsensing: An Online Learning Approach

Recently, crowdsensing revolutionizes sensing paradigm in Internet of Things (IoTs). However, a practical incentive mechanism which works for time-varying scenario and fairly incentivizes users to participate in crowdsensing is less studied. In this paper, we propose an incentive mechanism for crowdsensing under continuous and time-varying scenario using three-stage Stackelberg game. In such a scenario, different requesters generate sensing tasks with payments to the platform at each time slot. The platform makes pricing decision to determine rewards for tasks without complete information, and then notifies task-price pairs to online users in Stage I. In Stage II, users select optimal tasks as their interests under certain constraints and report back to the platform. The platform fairly selects users as workers in order to ensure users' long-term participation in Stage III. We use Lyapunov optimization to address online decision problems for the platform in Stage I and III where there are no prior knowledge and future information available. We propose an FPTAS for users to derive their interests of tasks based on their mobile devices' computing capabilities in Stage II. Numerical results in simulations validate the significance and superiority of our proposed incentive mechanism.