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Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part I

Research Article

DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-54521-4_9,
        author={Weiwei Xing and Xinwei Yao and Chufeng Qi},
        title={DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part I},
        proceedings_a={COLLABORATECOM},
        year={2024},
        month={2},
        keywords={Mobile Crowd Sensing (MCS) Queueing theory Utility theory Incentive mechanism Utility equilibrium mode},
        doi={10.1007/978-3-031-54521-4_9}
    }
    
  • Weiwei Xing
    Xinwei Yao
    Chufeng Qi
    Year: 2024
    DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-031-54521-4_9
Weiwei Xing1, Xinwei Yao1,*, Chufeng Qi1
  • 1: College of Computer Science and Technology, Zhejiang University of Technology
*Contact email: xwyao@zjut.edu.cn

Abstract

As an emerging paradigm for collecting sensory data, Mobile Crowd Sensing (MCS) technology has found widespread application. The successful application of MCS technology relies not only on the active participation of participants but also on the continuous demand for sensing task from data requestors. However, existing researchers predominantly focus on designing participant incentive mechanisms to attract participant to engage in the sensing activities, while the incentive mechanisms for data requestors are rarely addressed. To address the gap, we conceptualize the interactions between data requestors and participants as a queueing process. Building upon utility theory, we propose Dynamic Pricing Incentive Mechanism (DPIM) that dynamically offers optimal incentive guidance to the sensing platform. Moreover, we devise two distinct utility optimization modes for data requestors: one for maximizing their utility and the other for achieving utility equilibrium. These modes are tailored to meet the distinct utility requirement of the sensing platform and data requestors. Through simulations and theoretical analysis, we demonstrate that DPIM effectively provides incentives for the sensing platform across different utility modes.

Keywords
Mobile Crowd Sensing (MCS) Queueing theory Utility theory Incentive mechanism Utility equilibrium mode
Published
2024-02-23
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-54521-4_9
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