预测区块链网络内未来资源需求的博弈论与深度学习

IF 4.3 2区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Consumer Electronics Pub Date : 2024-08-22 DOI:10.1109/TCE.2024.3445458
Siyun Xu;Miao Zhang;Tong Wang
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引用次数: 0

摘要

全球bbb网络不断发展,对资源的需求也在不断增长。由于区块链环境的分散性,这些系统正在从传统系统转向先进系统,从而可以与6G通信通道无缝连接,并确保数据的安全性。在区块链网络中,计算资源、数据存储资源、带宽、传感器、发电动力资源等资源是不可或缺的组成部分。对未来资源需求的预测对区块链网络的顺利运行具有重要意义。6G网络和机器学习技术、物联网(IoT)、数字孪生、网络物理系统和人工智能工具等先进技术在重塑区块链网络方面发挥着重要作用。本研究工作是利用深度学习和博弈论来绘制资源需求并评估区块链系统,以找到区块链启用系统顺利运行的潜在资源需求。从区块链节点收集采样数据,并设计了基于参数的迁移方法来改进深度学习模型的预测。可以预测基于软件的区块链网络的资源需求,在支持区块链的网络上可以预测未来的负载。基于深度学习神经网络的训练模型通过非线性模块实现多层转换组合,在基于区块链的系统中对资源需求进行准确预测。本文利用迁移理论,结合深度神经网络的优势进行准确的预测。所需未来资源对原始变量的预测预测精度达到85.87%。由于区块链网络的分散性和安全性,目前许多应用程序都依赖于区块链环境,因此所提出的模型有助于确定区块链系统顺利运行的未来资源需求。
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Game Theory and Deep Learning for Predicting Demand for Future Resources Within Blockchain-Networks
The global Blockchain networks are growing and demand for resources is also growing respectively. The systems are switching from traditional systems to advanced systems where there is a seamless connectivity with 6G communication channels and security of data due to decentralized nature of Blockchain environment. The resources play integral part in Blockchain networks such as computational resources, data storage resources, bandwidth, sensors and energy generation power resources. The forecasting of futuristic demand of resources is important for the smooth functioning of Blockchain networks. The advanced technologies like 6G networks and machine learning techniques, Internet of Things (IoT), Digital Twins, Cyber Physical systems and AI enabled tools are playing an important role in reshaping the Blockchain networks. This research work is utilizing deep learning and game theory to map the resource requirement and to evaluate the Blockchain systems to find the potential demand for resources for smooth functioning of Blockchain enabled systems. The sampling data has been collected from Blockchain nodes and parameter based migration methods are devised to improve the predictions of deep learning models. The resource needs of the software based Blockchain networks can be predicted where the future load can be predicted on Blockchain enabled networks. The trained model based on deep learning neural networks achieves multi-layer conversion combinations through nonlinear modules to make accurate predictions in Blockchain based systems for resource requirement. This article uses the migration theory, combined with the advantages of deep neural networks to produce accurate predictions. The forecasting prediction accuracy of the required futuristic resources on raw variables is attained at 85.87%. The proposed model helps to determine the futuristic need of the resources for smooth functioning of Blockchain systems as many applications nowadays are dependent upon the Blockchain environment due to decentralized and secured nature of Blockchain networks.
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来源期刊
CiteScore
7.70
自引率
9.30%
发文量
59
审稿时长
3.3 months
期刊介绍: The main focus for the IEEE Transactions on Consumer Electronics is the engineering and research aspects of the theory, design, construction, manufacture or end use of mass market electronics, systems, software and services for consumers.
期刊最新文献
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