Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics

In a delay-sensitive mobile crowdsensing (MCS) platform, a service provider offers monetary incentives to mobile users for participating in the data collection and reporting their obtained data by a deadline. One aspect missing from most prior literature in the incentive mechanism design is the consideration of the detailed data reporting process through cellular or Wi-Fi networks. In this paper, we consider the interactions between the service provider and the users in two stages. First, the service provider chooses a reward to maximize its expected profit under the incomplete information of the users' responses. Next, given the reward, each user makes his participation and reporting decisions, which are complicated due to his mobility and network heterogeneity. We propose an algorithm to compute the optimal user's decisions under the general setting using dynamic programming, and derive closed-form decision criteria for the special yet practical case of a non-discounted reward. We compute the optimal reward by characterizing the solution set and the discontinuity in the profit function. Simulation results show that our proposed algorithm achieves a significant gain in the user payoff over three benchmark heuristic schemes. In addition, a service provider's profit is sensitive to the estimation of the users' Wi-Fi availabilities.