Contract-Based Incentive Design for Resource Allocation in Edge Computing-Based Blockchain

被引:0
作者
Yu, Ziqing [1 ,2 ]
Chang, Zheng [1 ,2 ]
Wang, Li [3 ]
Min, Geyong [4 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
[2] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710126, Peoples R China
[3] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
[4] Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, England
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2024年 / 11卷 / 06期
基金
中国国家自然科学基金;
关键词
Blockchains; Resource management; Data mining; Security; Cloud computing; Contracts; Servers; Blockchain; contract theory; mobile edge computing (MEC); mining pool; incentive mechanism; EVOLUTIONARY GAME; ENABLED INTERNET; ENERGY; MANAGEMENT; SELECTION; MECHANISM;
D O I
10.1109/TNSE.2024.3457888
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
To boost the wide applications of the blockchain, Mobile edge computing (MEC) emerges as potential solution that can provide computing resources in terms of computation offloading. In blockchain, pool mining allows to combine a small amount of computing resources to operate together, which helps the miners with small number of resources mine blocks more efficiently. Therefore, a MEC-enabled blockchain has recently received significant research interests. However, how to encourage the involvements of different parties and operate resource allocation in the MEC-enabled blockchain in an efficient manner are still under-investigation. In this paper, we study the problem of resource allocation in a MEC-enabled blockchain network, and design a novel contract-based incentive mechanism to motivate the MEC service providers (SPs) to provide computing services to blockchain miners. Numerical results demonstrate that the proposed mechanism can improve the payoffs of miners and SPs. Besides, we also analyzed the impact of changes in the number of miners and SPs on network performance based on experimental results, aiming to provide some suggestions to construct efficient resources trading networks.
引用
收藏
页码:6143 / 6156
页数:14
相关论文
共 45 条
[1]  
[Anonymous], 2015, Working Paper
[2]   Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains With Mobile Edge Computing [J].
Asheralieva, Alia ;
Niyato, Dusit .
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2021, 7 (01) :319-335
[3]   Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks [J].
Asheralieva, Alia ;
Niyato, Dusit .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2021, 20 (03) :1092-1109
[4]   Information Propagation in the Bitcoin Network [J].
Decker, Christian ;
Wattenhofert, Roger .
13TH IEEE INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING (P2P), 2013,
[5]  
Dib Omar., 2018, International Journal On Advances in Telecommunications, V11
[6]  
Garay J, 2015, The Bitcoin Backbone Protocol: Analysis and Applications
[7]  
Gemeliarana I. Gusti Ayu Kusdiah, 2018, 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), P126, DOI 10.1109/ISRITI.2018.8864381
[8]   Blockchain Meets Edge Computing: Stackelberg Game and Double Auction Based Task Offloading for Mobile Blockchain [J].
Guo, Shaoyong ;
Dai, Yao ;
Guo, Song ;
Qiu, Xuesong ;
Qi, Feng .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (05) :5549-5561
[9]   Importance-Aware Data Selection and Resource Allocation in Federated Edge Learning System [J].
He, Yinghui ;
Ren, Jinke ;
Yu, Guanding ;
Yuan, Jiantao .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (11) :13593-13605
[10]   Incentive Mechanism Design for Wireless Energy Harvesting-Based Internet of Things [J].
Hou, Zhanwei ;
Chen, He ;
Li, Yonghui ;
Vucetic, Branka .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (04) :2620-2632