{"title":"预测区块链网络内未来资源需求的博弈论与深度学习","authors":"Siyun Xu;Miao Zhang;Tong Wang","doi":"10.1109/TCE.2024.3445458","DOIUrl":null,"url":null,"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.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6997-7006"},"PeriodicalIF":4.3000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Game Theory and Deep Learning for Predicting Demand for Future Resources Within Blockchain-Networks\",\"authors\":\"Siyun Xu;Miao Zhang;Tong Wang\",\"doi\":\"10.1109/TCE.2024.3445458\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":13208,\"journal\":{\"name\":\"IEEE Transactions on Consumer Electronics\",\"volume\":\"70 4\",\"pages\":\"6997-7006\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Consumer Electronics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10643878/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Consumer Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10643878/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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.