Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning

Vehicular crowdsensing (VCS) takes the advantage of vehicles' mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers' carried smart mobile devices and on-board sensors to accomplish challenging sensing tasks. The daily roadway commutes of vehicle drivers may form "virtual" mobile communities, called Vehicular Social Networks (VSNs). It offers an opportunity to include social network effect into incentive mechanism design where a driver can benefit from others' sensing strategy in one VSN. In this paper, we consider a non-cooperative VCS campaign where multiple vehicles are incentivized by dynamically priced tasks and social network effect. In order to maximize the overall utility of vehicle drivers, we propose a social-aware incentive mechanism by deep reinforcement learning (called DRL-SIM), to derive the optimal long term sensing strategy for all vehicles. Finally, numerical results are supplemented to show both the convergence and the effectiveness of DRL-SIM when compared with other baselines.