Blockchain and Trusted Hardware-Enabled Data Scheduling for Edge Learning in Wireless IIoT

• Published in: IEEE internet of things journal Vol. 11; no. 21; pp. 34229 - 34242
• Main Authors: Liu, Tianhao, Liu, Jiqiang, Zhang, Tao, Wang, Jian, Yuan, Zhenhui, Xu, Minrui, Zhai, Di, Wang, Tianxi, Du, Hongyang, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blockchain; Cybersecurity; Data compression; Data integrity; Data management; data scheduling; Data transmission; edge learning; Efficiency; Industrial applications; Industrial Internet of Things; Job shop scheduling; Machine learning; Processor scheduling; Resource management; Resource scheduling; Resource utilization; Scheduling; secure communication; Security; Task analysis; Task scheduling; Wireless communication; Wireless communications; Wireless Industrial Internet of Things (WIIoT)
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3443642

5G and Beyond 5G communication technologies have promoted the architectural innovation of the Industrial Internet of Things (IIoT) and the wide application of edge learning. As Beyond 5G technologies enhance wireless communication within IIoT, the demand for efficient, secure data management becomes paramount. Edge learning emerges as a solution for localized model training, reducing the necessity for extensive data transmission. However, this decentralization introduces vulnerabilities, particularly in data security during transmission and efficient resource utilization. To address the challenges of data scheduling for edge learning in the Wireless IIoT (WIIoT), we propose a novel architecture that leverages blockchain for secure, decentralized data scheduling and employs physically unclonable functions (PUFs)-based algorithm to ensure data integrity and confidentiality. The primary contributions consist of a task scheduling model based on blockchain, along with a data compression scheme in multiple stages combined with a data scheduling algorithm that is optimized for energy efficiency in edge learning environments. Experiments conducted on a simulated WIIoT platform comprising embedded devices validate our approach, demonstrating enhanced data security and learning efficiency which can reduce 40% in the training stage and 70% in the inference stage. Our findings contribute to the advancement of security and efficient edge learning frameworks in the context of WIIoT, addressing the intricate balance between security, efficiency, and decentralized trust.