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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Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2024-08-22
DOI
https://doi.org/10.1109/tce.2024.3445458
Citations
2
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
1.56
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article

Game Theory and Deep Learning for Predicting Demand for Future Resources Within Blockchain-Networks

Miao Zhang, Tong Wang, Siyun Xu
2 citations
IEEE Transactions on Consumer Electronics
Blockchain Technology Applications and Security
1.56
article

Game Theory and Deep Learning for Predicting Demand for Future Resources Within Blockchain-Networks

Miao Zhang, Tong Wang, Siyun Xu
article en
2 citations

Abstract

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.

IEEE Transactions on Consumer ElectronicsVol. 70(4)
Hebei Finance University (CN)
Openalex Percentile: Top 13%
Blockchain Technology Applications and Security
1.56
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