An Incentive Mechanism for Privacy-Preserving Crowdsensing via Deep Reinforcement Learning

With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way toward an emerging paradigm, i.e., crowdsensing that facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants and to incentivize them to improve sensing quality. In this article, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data, and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multileader-multifollower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., Q-learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with Q-learning in a dueling network architecture, i.e., double deep Q network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.