Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/6061
Title: | Sustainability and Latency Reduction Through Federated Learning-Powered Digital Twins in IoT Devices | Authors: | Abdullah, Q. Salh, A. Ahmed, M.S. Mohd, Shah, N.S. Aydogdu, O. Hussain, G.A. |
Keywords: | B5G; Digital twins; DRL; energy consumption Bandwidth; Blockchain; Data privacy; Deep learning; Digital devices; Edge computing; Green computing; Internet of things; Learning algorithms; Reinforcement learning; Sustainable development; B5G; Block-chain; Deep reinforcement learning; Digital system; EDGE Networks; Emerging technologies; Energy-consumption; Latency reduction; Reinforcement learnings; Wireless access; Energy utilization |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | The rapid advancement of emerging technologies and the Internet of Things (IoT), including the evolution of Digital Twins (DT), necessitates an accelerated pace in the Beyond Fifth Generation (B5G). This is crucial to establish widespread wireless access by ensuring resilient and immediate wireless connectivity within the real network environment. This article uses edge networks and DTs with blockchain technology. Ensuring robust real-time data processing while providing a scalable and secure solution is the aim. Bridging the gap between digital systems and physical edge networks is the goal. In this research, we bridge the gap between physical edge networks and digital systems by introducing Networks with Digital Twin Edges (NDITE), which combine digital twins and edge networks. Next, we propose a blockchain-driven federated learning method in NDITE to improve data privacy and communication security. We schedule relaying users and manage bandwidth resources using DT- powered Deep Reinforcement Learning (DRL) to increase efficiency. According to the simulation results, the suggested DRL agent-based DT can minimize the weighted cost of transmission policy of edge computing strategies and choose 47.5% of computing tasks to be completed locally with 1 MHz of bandwidth. It can also exploit the optimal policy. © 2024 IEEE. | Description: | 16th International Conference on Computer and Automation Engineering, ICCAE 2024 -- 14 March 2024 through 16 March 2024 -- 200755 | URI: | https://doi.org/10.1109/ICCAE59995.2024.10569209 https://hdl.handle.net/20.500.13091/6061 |
ISBN: | 9798350370058 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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