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Transfer Learning-powered Resource Optimization for Green Computing in 5G-Aided Industrial Internet of Things

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Published:22 October 2021Publication History
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Abstract

Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

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  1. Transfer Learning-powered Resource Optimization for Green Computing in 5G-Aided Industrial Internet of Things

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            • Published in

              cover image ACM Transactions on Internet Technology
              ACM Transactions on Internet Technology  Volume 22, Issue 2
              May 2022
              582 pages
              ISSN:1533-5399
              EISSN:1557-6051
              DOI:10.1145/3490674
              • Editor:
              • Ling Liu
              Issue’s Table of Contents

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 22 October 2021
              • Accepted: 1 November 2020
              • Revised: 1 August 2020
              • Received: 1 May 2020
              Published in toit Volume 22, Issue 2

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