Task Selection and Route Planning for Mobile Crowd Sensing Using Multi-Population Mean-Field Games

With the increasing deployment of mobile vehicles, such as mobile robots and unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in mobile crowd sensing (MCS). Specifically, mobile vehicles equipped with sensors and computing devices are able to collect massive data due to their fast and flexible mobility in MCS systems. In this paper, we consider a mobile vehicle-based MCS system where vehicles owned by different operators or individuals compete against others for limited sensing resources. We investigate the joint task selection and route planning problem for such an MCS system. However, since the structural complexity and computational complexity of the original problem is very high, we propose a multi-population Mean-Field Game (MFG) problem by simplifying the interaction between vehicles as a distribution over their strategy space, known as the mean-field term. To solve the multi-population MFG problem efficiently, we propose a G-prox primal-dual hybrid gradient method (PDHG) algorithm whose computational complexity is independent of the number of vehicles. Numerical results show that the proposed multi-population MFG scheme and algorithm are of effectiveness and efficiency.