Blockchain Empowered Resource Trading in Mobile Edge Computing and Networks

被引:0
|
作者
Qiao, Guanhua [1 ]
Leng, Supeng [1 ]
Chai, Haoye [1 ]
Asadi, Arash [2 ]
Zhang, Yan [3 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu 611731, Sichuan, Peoples R China
[2] Tech Univ Darmstadt, Secure Mobile Networking Lab, D-64293 Darmstadt, Germany
[3] Univ Oslo, Oslo, Norway
来源
ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) | 2019年
基金
中国国家自然科学基金; 国家重点研发计划; 欧盟地平线“2020”;
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a new device-to-device edge computing and networks (D2D-ECN) framework which facilitates low-latency execution of real-time Internet-of Things applications through computation offloading with minimal overhead. Our framework accounts for key challenges of D2D-ECN in terms of the efficiency of the resource management and the resulting security concerns caused by lacking trustworthy between task owners and resource providers. In particular, we propose to use a blockchain-empowered framework for implementing resource trading and task assigment as the smart contracts. However, the existing Proof-of-Work (PoW) is impractical for the resource-constrained toT devices due to high computational complexity of the mining process. Thus, we present a reputation-based consensus mechanism called proof-of-reputation (PoR), where the device with the highest reputation score is responsible for packaging the resource transactions and reputation records in the blockchain. Furthermore, we evaluate the reputation score of each device according to the current computation performance and history reputation. Security, feasibility analysis and numerical results show that our proposed computation offloading scheme can be deployed in the decentralized D2D-ECN system safely and effectively.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Utility Optimization for Blockchain Empowered Edge Computing with Deep Reinforcement Learning
    Nguyen, Dinh C.
    Ding, Ming
    Pathirana, Pubudu N.
    Seneviratne, Aruna
    Li, Jun
    Poor, H. Vincent
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [42] Blockchain-Empowered Distributed Multicamera Multitarget Tracking in Edge Computing
    Wang, Shuai
    Sheng, Hao
    Zhang, Yang
    Yang, Da
    Shen, Jiahao
    Chen, Rongshan
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 20 (01) : 369 - 379
  • [43] Auction-Based Resource Allocation for Mobile Edge Computing Networks
    Liu, Ben
    Xu, Ding
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2020, E103A (04) : 718 - 722
  • [44] Resource Calendaring for Mobile Edge Computing in 5G Networks
    Xiang, Bin
    Elias, Jocelyne
    Martignon, Fabio
    Di Nitto, Elisabetta
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [45] Resource Allocation in Adaptive Virtualized Wireless Networks with Mobile Edge Computing
    Parwez, Md Salik
    Rawat, Danda B.
    2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2018,
  • [46] AI-EMPOWERED MOBILE EDGE COMPUTING IN THE INTERNET OF VEHICLES
    Huang, Jun
    Othman, Jalel Ben
    Wang, Shiqiang
    Kwok, Ricky Y. K.
    Leung, Victor C. M.
    Sun, Wei
    IEEE NETWORK, 2021, 35 (03): : 72 - 73
  • [47] Dynamic Mobile Edge Computing empowered by Reconfigurable Intelligent Surfaces
    Di Lorenzo, Paolo
    Merluzzi, Mattia
    Strinati, Emilio Calvanese
    SPAWC 2021: 2021 IEEE 22ND INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (IEEE SPAWC 2021), 2020, : 526 - 530
  • [48] Editorial: Collaborative Computing in AI Empowered Mobile Networks
    Xiaoxian Yang
    Li Kuang
    Mobile Networks and Applications, 2022, 27 : 2098 - 2099
  • [49] Editorial: Collaborative Computing in AI Empowered Mobile Networks
    Yang, Xiaoxian
    Kuang, Li
    MOBILE NETWORKS & APPLICATIONS, 2022, 27 (05): : 2098 - 2099
  • [50] Three-stage Stackelberg game based edge computing resource management for mobile blockchain
    Fan, Yuqi
    Jin, Zhifeng
    Shen, Guangming
    Hu, Donghui
    Shi, Lei
    Yuan, Xiaohui
    PEER-TO-PEER NETWORKING AND APPLICATIONS, 2021, 14 (03) : 1431 - 1445