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A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: a deep reinforcement learning approach

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Abstract

Mobile crowdsensing (MCS) utilizes the mobility of participating users and relies on the sensing ability of user devices to complete high-quality sensing tasks with limited cost. Designing an incentive mechanism that maximizes revenue for both service provider and users while ensuring the quality of sensing data and preserving users’ privacy remains a challenge in many scenarios. In this paper, we try to design an privacy-preserving incentive scheme based on DRL and Stackelberg game model which is dedicated to MCS. The proposed incentive mechanism is based on a two-stage Stackelberg game, in which the service provider is the leader and the user devices are the followers. We construct the relationship between user devices as a non-cooperative game and prove the existence and uniqueness of Nash equilibrium (NE) in this game. Considering the cost and quality of sensing data, we use the reputation constraint mechanism as the evaluation standard of data quality, and include sensing cost as indicator. Different from the traditional NE derivation method, we adopt deep reinforcement learning (DRL) approach (called PPO-DSIM) to derive NE and the optimal sensing strategy while protecting the user’s private information. Numerical simulation results show the convergence and effectiveness of the PPO-DSIM.

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Acknowledgements

The work is partially supported by the National Natural Science Foundation of China (No. 61672176), the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Science and technology project (GuikeAA22387 and GuikeAD21220114), the Center for Applied Mathematics of Guangxi (Guangxi Normal University), the Guangxi Talent Highland Project of Big Data Intelligence and Application, the Guangxi Science and Technology Plan Projects No. AD20159039, the Guangxi Young and Middle-aged Ability Improvement Project No. 2020KY02032, the Innovation Project of Guangxi Graduate Education (No. JXXYYJSCXXM-2021-014), the Innovation Project of Guangxi Graduate Education (No. YCBZ2021038), and the Innovation Project of Guangxi Graduate Education(No. YCSW2022162).

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  1. School of Computer Science and Engineering and School of Software, Guangxi Normal University, Guilin, 530022, China

    Jialin Zhang, Xianxian Li, Zhenkui Shi & Cong Zhu

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  1. Jialin Zhang
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  2. Xianxian Li
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Correspondence to Zhenkui Shi or Cong Zhu.

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Zhang, J., Li, X., Shi, Z. et al. A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: a deep reinforcement learning approach. Wireless Netw 30, 4685–4698 (2024). https://doi.org/10.1007/s11276-022-03111-9

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