Task Selection and Collision-Free Route Planning for Mobile Crowdsensing 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 communication modules are able to execute large scale tasks due to their fast and flexible mobility in MCS systems. However, the battery capacity of mobile vehicles imposes a significant limitation on their performance, and so energy efficiency is an important metric especially when a large number of mobile vehicles collect sensing data. In this paper, we consider a mobile-vehicle-assisted MCS system where vehicles are owned by different operators or individuals who compete against others for limited sensing resources. We investigate the joint task selection and route planning problem for such an MCS system from an energy-efficiency perspective. However, since the computational complexity of the original problem is very high due to the large number of vehicles, we propose a multi-population Mean-Field Game (MPMFG) problem by simplifying the interaction between vehicles as a distribution over their strategy space, known as the mean-field term. To solve the MPMFG 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 verify the effectiveness and efficiency of the proposed MPMFG scheme and G-prox PDHG algorithm.