Optimal Miner Allocation Scheme for Sub-metaverses: From Multi-knapsack Problem Perspective

KANG Jiawen; WU Tianhao; WEN Jinbo; CHEN Junlong; XIONG Zehui; HUANG Xumin; LIU Lei

doi:10.11999/JEIT231214

Volume 46 Issue 5

May 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2177-2186

KANG Jiawen, WU Tianhao, WEN Jinbo, CHEN Junlong, XIONG Zehui, HUANG Xumin, LIU Lei. Optimal Miner Allocation Scheme for Sub-metaverses: From Multi-knapsack Problem Perspective[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2177-2186. doi: 10.11999/JEIT231214

Citation:

KANG Jiawen, WU Tianhao, WEN Jinbo, CHEN Junlong, XIONG Zehui, HUANG Xumin, LIU Lei. Optimal Miner Allocation Scheme for Sub-metaverses: From Multi-knapsack Problem Perspective[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2177-2186. doi: 10.11999/JEIT231214

Citation:

KANG Jiawen, WU Tianhao, WEN Jinbo, CHEN Junlong, XIONG Zehui, HUANG Xumin, LIU Lei. Optimal Miner Allocation Scheme for Sub-metaverses: From Multi-knapsack Problem Perspective[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2177-2186. doi: 10.11999/JEIT231214

PDF( 3815 KB)

Optimal Miner Allocation Scheme for Sub-metaverses: From Multi-knapsack Problem Perspective

doi: 10.11999/JEIT231214

1.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3.
Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore 487372
4.
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62102099, 62003099), Guangzhou Basic and Applied Basic Research Project (2023A04J1699, 2023A04J1704)

Received Date: 2023-11-01
Rev Recd Date: 2024-05-01

Available Online: 2024-05-12

Publish Date: 2024-05-30

Abstract

Abstract

Metaverses is a new type of internet social ecosystem that promotes user interaction, provides virtual services, and enables digital asset transactions. Blockchain, as the underlying technology of metaverses, supports the circulation of digital assets such as Non-Fungible Token (NFT) within the metaverse. However, the increase in consensus nodes can decrease the consensus efficiency of digital asset transactions. Therefore, a multi-metaverse digital assets transaction management framework based on edge computing and cross-chain technology is proposed. Firstly, cross-chain technology is utilized to connect multiple sub-metaverses into a multi sub-metaverse system. Secondly, edge devices are allocated as miners in various sub-metaverses, contributing idle computational resources to enhance the efficiency of digital asset transactions. Additionally, the paper models the edge device allocation problem as a multi-knapsack problem and designs a miner selection approach. To address the dynamic allocation problem caused by environmental changes, the Deep Reinforcement Learning Proximal Policy Optimization (DRL-PPO) algorithm from deep reinforcement learning is employed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving secure, efficient, and flexible cross-chain NFT transactions and sub-metaverse management.
- Metaverse,
- Blockchain,
- Multi-knapsacks problem,
- Deep Reinforcement Learning (DRL)

FullText(HTML)

References(25)

References

[1]	WANG Hang, NING Huansheng, LIN Yujia, et al. A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges[J]. IEEE Internet of Things Journal, 2023, 10(16): 14671–14688. doi: 10.1109/JIOT.2023.3278329.
[2]	LIN Yijing, DU Hongyang, NIYATO D, et al. Blockchain-aided secure semantic communication for AI-generated content in metaverse[J]. IEEE Open Journal of the Computer Society, 2023, 4: 72–83. doi: 10.1109/OJCS.2023.3260732.
[3]	CAO Bin, WANG Zixin, ZHANG Long, et al. Blockchain systems, technologies, and applications: A methodology perspective[J]. IEEE Communications Surveys & Tutorials, 2023, 25(1): 353–385. doi: 10.1109/COMST.2022.3204702.
[4]	REHMAN W, ZAINAB H E, IMRAN J, et al. NFTs: Applications and challenges[C]. The 22nd International Arab Conference on Information Technology (ACIT), Muscat, Oman, 2021: 1–7. doi: 10.1109/ACIT53391.2021.9677260.
[5]	WEN Jinbo, LIU Xiaojun, XIONG Zehui, et al. Optimal block propagation and incentive mechanism for blockchain networks in 6g[C]. 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Wuhan, China, 2022: 369–374. doi: 10.1109/TrustCom56396.2022.00058.
[6]	ZHENG Zibin, XIE Shaoan, DAI Hongning, et al. Blockchain challenges and opportunities: A survey[J]. International Journal of Web and Grid Services, 2018, 14(4): 352–375. doi: 10.1504/IJWGS.2018.095647.
[7]	YANG Qinglin, ZHAO Yetong, HUANG Huawei, et al. Fusing blockchain and AI with metaverse: A survey[J]. IEEE Open Journal of the Computer Society, 2022, 3: 122–136. doi: 10.1109/OJCS.2022.3188249.
[8]	KANG Jiawen, HE Jiayi, DU Hongyang, et al. Adversarial attacks and defenses for semantic communication in vehicular metaverses[J]. IEEE Wireless Communications, 2023, 30(4): 48–55. doi: 10.1109/MWC.004.2200617.
[9]	KANG Jiawen, WEN Jinbo, YE Dongdong, et al. Blockchain-empowered federated learning for healthcare metaverses: User-centric incentive mechanism with optimal data freshness[J]. IEEE Transactions on Cognitive Communications and Networking, 2024, 10(1): 348–362. doi: 10.1109/TCCN.2023.3316643.
[10]	NGUYEN C T, HOANG D T, NGUYEN D N, et al. MetaChain: A novel blockchain-based framework for metaverse applications[C]. 2022 IEEE 95th Vehicular Technology Conference, Helsinki, Finland, 2022: 1–5. doi: 10.1109/VTC2022-Spring54318.2022.9860983.
[11]	JIANG Zexun, ZHA Cong, LI Xinyi, et al. A cross-chain framework for industry collaboration and transaction[C]. 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta), Haikou, China, 2022: 2436–2443. doi: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00341.
[12]	REN Yongjun, LV Zhiying, XIONG N N, et al. HCNCT: A cross-chain interaction scheme for the blockchain-based metaverse[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2024, 20(7): 188. doi: 10.1145/3594542.
[13]	WOOD G. POLKDOT: Vision for a heterogeneous multi-chain framework[EB/OL]. https://assets.polkadot.network/Polkadot-whitepaper.pdf, 2022.
[14]	LUONG N C, HOANG D T, GONG Shimin, et al. Applications of deep reinforcement learning in communications and networking: A survey[J]. IEEE Communications Surveys & Tutorials, 2019, 21(4): 3133–3174. doi: 10.1109/COMST.2019.2916583.
[15]	GU Jinlei, WANG Jiacun, GUO Xiwang, et al. A metaverse-based teaching building evacuation training system with deep reinforcement learning[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(4): 2209–2219. doi: 10.1109/TSMC.2022.3231299.
[16]	WANG Zhilin, HUT Q, XU Minghui, et al. Blockchain-based edge resource sharing for metaverse[C]. 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, USA, 2022: 620–626. doi: 10.1109/MASS56207.2022.00092.
[17]	LIU Weikang, CAO Bin, and PENG Mugen. Blockchain based offloading strategy: Incentive, effectiveness and security[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(12): 3533–3546. doi: 10.1109/JSAC.2022.3213324.
[18]	CAO Mingrui, ZHANG Long, and CAO Bin. Toward on-device federated learning: A direct acyclic graph-based blockchain approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 2028–2042. doi: 10.1109/TNNLS.2021.3105810.
[19]	OU Wei, HUANG Shiying, ZHENG Jingjing, et al. An overview on cross-chain: Mechanism, platforms, challenges and advances[J]. Computer Networks, 2022, 218: 109378. doi: 10.1016/j.comnet.2022.109378.
[20]	LIN Shaofeng, KONG Yihan, and NIE Shaotao. Overview of block chain cross chain technology[C]. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Beihai, China, 2021: 357–360. doi: 10.1109/ICMTMA52658.2021.00083.
[21]	PRADANA I G M T, DJATNA T, and HERMADI I. Blockchain modeling for traceability information system in supply chain of coffee agroindustry[C]. 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 2020: 217–224. doi: 10.1109/ICACSIS51025.2020.9263214.
[22]	LIU Xiaojun, WANG Wenbo, NIYATO D, et al. Evolutionary game for mining pool selection in blockchain networks[J]. IEEE Wireless Communications Letters, 2018, 7(5): 760–763. doi: 10.1109/LWC.2018.2820009.
[23]	KANG Jiawen, XIONG Zehui, NIYATO D, et al. Incentivizing consensus propagation in proof-of-stake based consortium blockchain networks[J]. IEEE Wireless Communications Letters, 2019, 8(1): 157–160. doi: 10.1109/LWC.2018.2864758.
[24]	ZHANG Junwei, ZHANG Zhenghao, HAN Shuai, et al. Proximal policy optimization via enhanced exploration efficiency[J]. Information Sciences, 2022, 609: 750–765. doi: 10.1016/j.ins.2022.07.111.
[25]	ZHAN Yufeng, LI Peng, QU Zhihao, et al. A learning-based incentive mechanism for federated learning[J]. IEEE Internet of Things Journal, 2020, 7(7): 6360–6368. doi: 10.1109/JIOT.2020.2967772.