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

Deng Li, Rongtao Hao, Zhenyan Wei and Jiaqi Liu ()
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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

Mathematics, 2024, vol. 12, issue 3, 1-22

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.

Keywords: edge computing; computation offloading; probability distortion; risk preference (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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