Mobility-Aware Participant Recruitment for Vehicle-Based Mobile Crowdsensing

Abstract

Nowadays, vehicles have been increasingly adopted in mobile crowdsensing applications. Due to their predictable mobility trajectories, vehicles as participants bring new insight in improving the crowdsensing quality. The predictable mobility of vehicles provides not only the current locations of the vehicles, but also their future mobility trajectory. In this context, the existing participant recruitment solutions, which are mainly based on the current locations of the participants, cannot be directly used in vehicle-based mobile crowdsensing. Utilizing the predicted mobility trajectory of vehicles, this paper aims to propose efficient vehicle recruitment algorithms for mobile crowdsensing, so as to minimize the overall recruitment cost. Specifically, we study two mobility trajectory models of the vehicles, named deterministic and probabilistic models. We first prove that the vehicle recruitment problem under both models is NP-hard. Then, for the deterministic trajectory model, an efficient LP-relaxation-based heuristic algorithm is proposed, and an approximation ratio is analyzed. For the probabilistic trajectory model, we propose a greedy algorithm and analyze its performance with a guaranteed approximation ratio. Finally, we evaluate the performance of the proposed schemes through simulations.