EIHDP : Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Tian WANG, Yucheng LU, Jianhuang WANG, Hong Ning DAI, Xi ZHENG, Weijia JIA

Research output: Journal PublicationsJournal Article (refereed)peer-review

13 Citations (Scopus)

Abstract

Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignores the competition among multiple edge service providers. For this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We describe an improved double-layer Stackelberg game model. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, which reduces the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

Original languageEnglish
Article number9359517
Pages (from-to)1285-1298
Number of pages14
JournalIEEE Transactions on Computers
Volume70
Issue number8
DOIs
Publication statusPublished - 1 Aug 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by Grants from Guangdong Key Lab of AI and Multi-modal Data Processing; in part by the Natural Science Foundation of Fujian Province of China under Grant 2020J06023; in part by BNU-UIC Institute of Artificial Intelligence and Future Networks funded by Beijing Normal University (Zhuhai) and AI-DS Research Hub, BNU-HKBU United International College (UIC), Zhuhai, Guangdong, China; in part by the National Natural Science Foundation of China (NSFC) under Grant 61872154, Grant 61872239, and Grant 61772148; and in part by Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University, China under Grant 17014083012.

Publisher Copyright:
© 1968-2012 IEEE.

Keywords

  • Cloud computing
  • Cloud-edge-client Collaboration
  • Collaboration
  • Dynamic pricing
  • Games
  • Internet of Things
  • Pricing
  • Quality of service
  • Stackelberg game
  • Task analysis
  • cloud-edge-client collaboration
  • stackelberg game
  • dynamic pricing

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