Mobile crowdsensing incentives under participation uncertainty

Mobile crowdsensing applications rely on crowds of contributors who are willing to carry out certain tasks of interest. This willingness varies widely across users and tasks and (monetary) incentives are often engaged to strengthen it or make up for its total absence. These incentives need to carefully account for the highly non-homogeneous response of users to external motivation. We propose a framework that explicitly accounts for the implicit uncertainty about the eventual participation and contributions of users. In the framework, the impact of incentives on the user choice to contribute or not is modeled probabilistically. First, we formulate a convex optimization problem for incentive allocation with the goal of achieving maximum expected quality while taking into account task budget limitations and constraints related to the physical locations of users and tasks. We then propose an iterative algorithm to alleviate the complexity of the original problem with two basic steps: an allocation step that applies incentive allocation to a set of available contributors; and a refinement step that revokes portions of the allocated incentives. We study the performance characteristics of our algorithm by comparing it to a default solver for convex optimization problems.