Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing

Mobile crowd sensing has become an effective approach to meet the demand in large scale sensing applications. In mobile crowd sensing applications, incentive mechanisms are necessary to compensate the resource consumptions and manual efforts of smartphone users. In this paper, we focus on exploring budget feasible frameworks for a novel and practical mobile crowd sensing scenario, where the platform expects to maximize the continuous time interval coverage under budget constraint. We present the system model and formulate the budget feasible maximum continuous time duration problem for this scenario. We design two budget feasible frameworks: BFF-STI and BFF-BTI, and integrate MST as the truthful mechanism to maximize the social efficiency. Then we extend the budget feasible frameworks to the general case, in which each user can bid multiple time intervals simultaneously. We show the proposed budget feasible frameworks are computationally efficient, individually rational, truthful and budget feasible. Through extensive simulations, we demonstrate that our budget feasible frameworks are efficient with different parameter settings. The simulation results also show that BFF-STI has superiority in large scale mobile crowd sensing applications, while BFF-STI is more suitable for long-term sensing applications.