AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10723-024-09743-9.pdf
Reference18 articles.
1. Zhao, Y., Hu, N., Zhao, Y., Zhu, Z.: A secure and flexible edge computing scheme for AI-driven industrial IoT. Cluster Comput. 26(1), 283–301 (2023)
2. Feng, X., Wu, J., Bashir, A.K., Li, J., Shen, A., Alshehri, M.D.: Vulnerability-aware task scheduling for edge intelligence empowered trajectory analysis in intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 24(4), 4661–4670 (2023)
3. Chaudhary, D., Kumar, B.: Cost optimized hybrid genetic-gravitational search algorithm for load scheduling in cloud computing. Appl. Soft Comput. 83, 105627 (2019)
4. Lv, Z.: Application of wireless network data driven using edge computing and deep learning in intelligent transportation. In: Data-Driven Intelligence in Wireless Networks, pp. 55–78 (2023)
5. Chaudhary, D., Kumar, B.: Cloudy GSA for load scheduling in cloud computing. Appl. Soft Comput. 71, 861–871 (2018)
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