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A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge Computing

Author

Listed:
  • Deng Li

    (School of Electronic Information, Central South University, Changsha 410075, China)

  • Rongtao Hao

    (School of Electronic Information, Central South University, Changsha 410075, China)

  • Zhenyan Wei

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

  • Jiaqi Liu

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

Abstract

Mobile Edge Computing (MEC) is a new distributed computing method based on the mobile communication network. It can provide cloud services and an IT service environment for application developers and service providers at the edge of the network. Computation offloading is a crucial technology of edge computing. However, computation offloading will consume the resources of the edge devices, and therefore the edge devices will not offload computation unconditionally. In addition, the service quality of edge computing applications is related to the cooperation rate of edge devices. Therefore, it is essential to design an appropriate incentive mechanism to motivate edge devices to execute computation offloading. However, the current existing incentive mechanisms have two problems: Firstly, existing mechanisms do not account for probability distortions under uncertainty in collaborator utility valuation models. Secondly, the platform ignores the risk preferences of collaborators in multiple rounds of decision-making. To address these issues, we propose an incentive mechanism based on risk preference, IMRP. The IMRP considers the collaborator’s probability distortion, introduces an uncertain utility bonus scheme, and builds a probability distortion model to influence the collaborator’s willingness to offload tasks. The IMRP also considers the collaborator’s risk preference and builds the collaborator’s risk preference model to influence the collaborator’s bidding decision. Simulation results show that our mechanism effectively improves the cooperation rate of edge devices and the utility of the requester.

Suggested Citation

  • Deng Li & Rongtao Hao & Zhenyan Wei & Jiaqi Liu, 2024. "A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge Computing," Mathematics, MDPI, vol. 12(3), pages 1-22, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:496-:d:1333491
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    References listed on IDEAS

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    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
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