Volume 37, Issue 3 (Spring 2022)                   ... 2022, 37(3): 695-720 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rabieinejad, E, Mohammadi S, Yadegari M. Provision of a recommender model for performance improvements for blockchain in the Internet of Things with a deep reinforcement learning approach. .... 2022; 37 (3) :695-720
URL: http://jipm.irandoc.ac.ir/article-1-4662-en.html
K.N.Toosi University of Technology, Faculty of Industrial Engineering, Department of Information Technology, Tehran, Iran;
Abstract:   (744 Views)
With the advancement of human society and information and communication technology, the Internet of Things has penetrated into various aspects of the daily lives of people and industries. Emerging blockchain technology has become a viable solution to IoT security due to its inherent characteristics such as distribution, security, immutability, and traceability. However, the integration of IoT and blockchain has challenges such as latency, throughput, scalability, and device power limitation. Recent research has focused on the role of artificial intelligence methods in improving IoT performance in blockchain. According to the studies, there are few effects on improving the performance of IoT devices with limited power, so in this study, a conceptual model for improving blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. According to studies, there is little research on improving the performance of IoT devices with limited power, so in this study, a conceptual model to improve blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. In this model, Internet devices with limited power can delegate their extraction task to the mobile edge computing layer. The presented model has six layers of perception, data, network, consensus, mobile edge computing and application, which are explained in detail. In this model, to improve the throughput and select the mining method, a recommender located in the mobile edge computation layer is used. recommender systems are adjusted by adjusting the size and time of building blocks to improve the throughput and also tries to minimize the delay and energy consumption of the mining operation by selecting suitable method. To achieve good performance in reinforcement learning, the use of Q learning and long- short term memory is suggested. the use of deep reinforcement learning is to set the block size by considering the transmission delay in order to increase throughput as well as mining with respect to the minimum delays and energy consumption in the proposed conceptual model can improve the performance of blockchain in the IoT.
Full-Text [PDF 1585 kb]   (686 Downloads)    
Type of Study: Research | Subject: Internet of Things (IoT)
Received: 2021/02/22 | Accepted: 2021/08/23 | Published: 2022/03/30

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Iranian Journal of Information processing and Management

Designed & Developed by : Yektaweb