Time-dependent Stable Task Assignment in Participatory Mobile Crowdsensing

Finding efficient task assignments is key to the success of mobile crowdsensing campaigns. Many studies in the literature focus on this problem and propose solutions that optimize the goals of mobile crowdsensing platform, but disregard user preferences. On the other hand, in a few recent studies that consider user preferences, workers are assigned a single task at a time, and the effect of these assignments to their prospective utilities is ignored. In this paper, we address these issues and study the task assignment problem considering both the user preferences and impact of each task assignment on the long-term utility of workers given the spatio-temporal characteristics of tasks. We propose a dynamic programming based task assignment algorithm that guarantees the satisfaction of users with their assignments. Through simulations, we compare it with a state-of-the-art algorithm and show the superiority of our algorithm in various aspects.