面向多子元宇宙矿工分配的多背包问题优化方案

康嘉文; 吴天昊; 文锦柏; 陈俊龙; 熊泽辉; 黄旭民; 刘雷

doi:10.11999/JEIT231214

面向多子元宇宙矿工分配的多背包问题优化方案

doi: 10.11999/JEIT231214

1.
广东工业大学自动化学院广州 510006
2.
南京航空航天大学计算机科学与技术学院南京 210016
3.
新加坡科技与设计大学信息系统技术与设计学院新加坡 487372
4.
西安电子科技大学通信工程学院西安 710071

基金项目: 国家自然科学基金 (62102099, 62003099)，广州市基础与应用基础研究项目(2023A04J1699, 2023A04J1704)

详细信息

作者简介:
康嘉文：男，教授，研究方向为无线通信网络

吴天昊：男，硕士生，研究方向为元宇宙、区块链

文锦柏：男，硕士生，研究方向为生成式人工智能、元宇宙、区块链

陈俊龙：男，研究方向为元宇宙、虚拟人、强化学习、车联网

熊泽辉：男，助理教授，研究方向为无线通信网络、区块链、边缘计算

黄旭民：男，副教授，研究方向为移动边缘计算、车联网

刘雷：男，副教授，研究方向为车载智能交通系统、车载边缘计算、算力网络、区块链

通讯作者:
康嘉文　kavinkang@gdut.edu.cn

中图分类号: TN915
计量
- 文章访问数: 249
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- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-01
- 修回日期: 2024-05-01
- 网络出版日期: 2024-05-12
- 刊出日期: 2024-05-30

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

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)

摘要

摘要: 元宇宙是一种新型互联网社会生态，旨在促进用户交流、提供虚拟服务和数字资产交易。区块链作为元宇宙的底层技术，支持非同质化通证(NFT)等数字资产在元宇宙内流通。然而，随着共识节点的增加，数字资产的交易共识效率会降低。因此，该文设计了基于边缘计算和跨链技术的多子元宇宙数字资产交易管理框架，首先，利用跨链技术将多个子元宇宙连接成多子元宇宙系统；其次，将边缘设备以矿工的身份分配到各个子元宇宙中，并利用其空闲的计算资源来提高数字资产交易的效率；此外，将边缘设备分配问题建模为一个多背包问题，并设计了一套矿工选择方案。针对环境动态变化的分配问题，采用深度强化学习中的近端策略优化(DRL-PPO)算法，有效解决多子元宇宙中子元宇宙的矿工分配问题。仿真结果验证了所提方法的有效性，能够以安全、高效和灵活的方式实现跨链NFT交易和子元宇宙管理。
- 元宇宙 /
- 区块链 /
- 多背包问题 /
- 深度强化学习
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)

HTML全文

图 1 多子元宇宙系统交易管理框架

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图 2 FISCO BCOS区块链平台节点信息

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图 3 单个元宇宙矿工数量对TPS和安全收益的影响

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图 4 单元宇宙系统与多子元宇宙系统的TPS对比

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图 5 不同优化算法结果对比

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图 6 不同子元宇宙数量的总收益比较

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图 7 不同矿工数量的总收益比较

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图 8 不同clip值对于DRL-PPO算法的影响

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1 基于DRL-PPO的子元宇宙矿工分配求解算法

输入：子元宇宙与矿工相关参数，初始策略参数${\theta _0}$，价值函数参数$ {\phi _0} $, DRL-PPO-Clip 超参数 $ \varepsilon $, 迭代次数$K$，每轮迭代步数$T = n$。
for $e = 0$ to $K$ do:
1 根据策略${\pi _{{e}}} = \pi ({\theta _{{e}}})$收集轨迹集合 ${\mathcal{D}_{{e}}} = {\lambda _i}$。
2 基于式(13)计算估计收益值$\widehat {{R_t}}$。
3 基于价值函数$ {V_{{\phi _e}}} $计算优势估计${\hat A_t}$。
4 通过最大化DRL-PPO-Clip目标来更新策略网络参数。
5 $ {\theta }_{e+1}=\text{arg}\underset{\theta }{\text{max}}{\widehat{E}}_{t}\left[\mathrm{min}\left({r}_{t}(\theta )\widehat{{A}_{t}},g\left(\varepsilon ,{r}_{t}(\theta )\right)\widehat{{A}_{t}}\right)\right] $
6 通过均方误差回归来更新价值函数参数。
7 $ {\phi _{e + 1}} = {\text{arg}}\mathop {{\text{min}}}\limits_\phi \frac{1}{{\|{\mathcal{D}_e}\|T}}\sum\limits_{\tau \in {\mathcal{D}_e}} {\sum\limits_{t = 0}^T {{{({V_\phi }({S_t}) - \widehat {{R_t}})}^2}} } $。
结束

下载: 导出CSV

表 1 实验基础系数设置

参数	设置值
区块大小 $b$ 平均信道容量 $\delta $ ${k_1},{k_2},{w_1},{w_2},p,g$ DRL-PPO超参数 $ \varepsilon $	100 kB 100 bps 0.5, 0.5, 0.5, 0.5, 2, 50 0.2

下载: 导出CSV

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