面向多子元宇宙矿工分配的多背包问题优化方案  

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

作　　者：康嘉文 吴天昊 文锦柏 陈俊龙 熊泽辉 黄旭民 刘雷[4] KANG Jiawen;WU Tianhao;WEN Jinbo;CHEN Junlong;XIONG Zehui;HUANG Xumin;LIU Lei(School of Automation,Guangdong University of Technology,Guangzhou 510006,China;College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Information Systems Technology and Design Pillar,Singapore University of Technology and Design,Singapore 487372;School of Telecommunications Engineering,Xidian University,Xi’an 710071,China)

机构地区：[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

出　　处：《电子与信息学报》2024年第5期2177-2186,共10页Journal of Electronics & Information Technology

基　　金：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)。

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(DRLPPO)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。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.

关 键 词：元宇宙 区块链 多背包问题 深度强化学习 

分 类 号：TN915[电子电信—通信与信息系统]