Mobile Computing, Applications, and Services. 10th EAI International Conference, MobiCASE 2019, Hangzhou, China, June 14–15, 2019, Proceedings

Research Article

A Pricing Incentive Mechanism for Mobile Crowd Sensing in Edge Computing

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  • @INPROCEEDINGS{10.1007/978-3-030-28468-8_14,
        author={Xin Chen and Zhuo Li and Lianyong Qi and Ying Chen and Yuzhe Zhao and Shuang Chen},
        title={A Pricing Incentive Mechanism for Mobile Crowd Sensing in Edge Computing},
        proceedings={Mobile Computing, Applications, and Services. 10th EAI International Conference, MobiCASE 2019, Hangzhou, China, June 14--15, 2019, Proceedings},
        proceedings_a={MOBICASE},
        year={2019},
        month={9},
        keywords={Mobile crowd sensing Pricing Social welfare Incentive mechanism Convex optimization},
        doi={10.1007/978-3-030-28468-8_14}
    }
    
  • Xin Chen
    Zhuo Li
    Lianyong Qi
    Ying Chen
    Yuzhe Zhao
    Shuang Chen
    Year: 2019
    A Pricing Incentive Mechanism for Mobile Crowd Sensing in Edge Computing
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-28468-8_14
Xin Chen1,*, Zhuo Li,*, Lianyong Qi2,*, Ying Chen1,*, Yuzhe Zhao1,*, Shuang Chen1,*
  • 1: Beijing Information Science and Technology University
  • 2: Qufu Normal University
*Contact email: chenxin@bistu.edu.cn, lizhuo@bistu.edu.cn, lianyongqi@gmail.com, chenying@bistu.edu.cn, duyadude@163.com, 1980995580@qq.com

Abstract

Mobile crowd sensing (MCS) has been recognized as a promising method to acquire massive volume of data. Stimulating the enthusiasm of participants could be challenging at the same time. In this paper, we first propose a three-layer mobile crowd sensing architecture and introduce edge servers into it. The edge servers are used to process raw data and improve response time. Our goal is to maximize social welfare. Specifically, we model the social welfare maximization problem by Markov decision process and study a convex optimization pricing problem in the proposed three-layer architecture. The size of the tasks the edge servers assign is adjustable in this system. Then Lagrange multiplier method is leveraged to solve the problem. We derive the experimental data from real-world dataset and extensive simulations demonstrate the performance of our proposed method.