Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications. Группа авторов

A. M. Ghosh and K. Grolinger, “Deep Learning: Edge-Cloud Data Analytics for IoT,” 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, 2019, pp. 1-7, doi: 10.1109/CCECE.2019.8861806.

      33. M. Yang et al., “Deep Reinforcement Learning based Green Resource Allocation Mechanism in Edge Computing driven Power Internet of Things,” 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 2020, pp. 388-393, doi: 10.1109/IWCMC48107.2020.9148169.

      34. Chen, Jiasi & Ran, Xukan. (2019). Deep Learning with Edge Computing: A Review. Proceedings of the IEEE. pp. 1-20. 10.1109/JPROC.2019.2921977.

      35. Zhang, Yi, Yu Liu, Junlong Zhou, Jin Sun, and Keqin Li. “Slow-movement particle swarm optimization algorithms for scheduling security-critical tasks in resource-limited mobile edge computing.” Future Generation Computer Systems (2020).

      36. Zakaryia, Samah A., Safaa A. Ahmed, and Mohamed K. Hussein. “Evolutionary offloading in an edge environment.” Egyptian Informatics Journal (2020).

      37. Feng, Jingyun, Zhi Liu, Celimuge Wu, and YushengJi. “AVE: Autonomous vehicular edge computing framework with ACO-based scheduling.” IEEE Transactions on Vehicular Technology 66, no. 12 (2017): 10660-10675.

      1 ^*Corresponding author: [email protected]