|
[1]
|
Urrea, C. and Benítez, D. (2021) Software-Defined Networking Solutions, Architecture and Controllers for the Industrial Internet of Things: A Review. Sensors, 21, Article 6585. [Google Scholar] [CrossRef] [PubMed]
|
|
[2]
|
Bourechak, A., Zedadra, O., Kouahla, M.N., Guerrieri, A., Seridi, H. and Fortino, G. (2023) At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives. Sensors, 23, Article 1639. [Google Scholar] [CrossRef] [PubMed]
|
|
[3]
|
Hassan, N., Gillani, S., Ahmed, E., Yaqoob, I. and Imran, M. (2018) The Role of Edge Computing in Internet of Things. IEEE Communications Magazine, 56, 110-115. [Google Scholar] [CrossRef]
|
|
[4]
|
Chen, B., Wan, J., Celesti, A., Li, D., Abbas, H. and Zhang, Q. (2018) Edge Computing in IoT-Based Manufacturing. IEEE Communications Magazine, 56, 103-109. [Google Scholar] [CrossRef]
|
|
[5]
|
Qi, Q., Zhang, L., Wang, J., Sun, H., Zhuang, Z., Liao, J., et al. (2020) Scalable Parallel Task Scheduling for Autonomous Driving Using Multi-Task Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 69, 13861-13874. [Google Scholar] [CrossRef]
|
|
[6]
|
Walia, G.K., Kumar, M. and Gill, S.S. (2024) AI-Empowered Fog/edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives. IEEE Communications Surveys & Tutorials, 26, 619-669. [Google Scholar] [CrossRef]
|
|
[7]
|
Tan, H., Han, Z., Li, X. and Lau, F.C.M. (2017). Online Job Dispatching and Scheduling in Edge-Clouds. IEEE INFOCOM 2017-IEEE Conference on Computer Communications, Atlanta, 1-4 May 2017, 1-9.[CrossRef]
|
|
[8]
|
Zhang, Y., Du, P., Wang, J., Ba, T., Ding, R. and Xin, N. (2019) Resource Scheduling for Delay Minimization in Multi-Server Cellular Edge Computing Systems. IEEE Access, 7, 86265-86273. [Google Scholar] [CrossRef]
|
|
[9]
|
Chiang, Y., Zhang, T. and Ji, Y. (2019) Joint Cotask-Aware Offloading and Scheduling in Mobile Edge Computing Systems. IEEE Access, 7, 105008-105018. [Google Scholar] [CrossRef]
|
|
[10]
|
Orhean, A.I., Pop, F. and Raicu, I. (2018) New Scheduling Approach Using Reinforcement Learning for Heterogeneous Distributed Systems. Journal of Parallel and Distributed Computing, 117, 292-302. [Google Scholar] [CrossRef]
|
|
[11]
|
Liu, H., Ma, Y., Chen, P., Xia, Y., Ma, Y., Zheng, W., et al. (2020) Scheduling Multi-Workflows over Edge Computing Resources with Time-Varying Performance, a Novel Probability-Mass Function and DQN-Based Approach. In: Lecture Notes in Computer Science, Springer, 197-209. [Google Scholar] [CrossRef]
|
|
[12]
|
Xiong, X., Zheng, K., Lei, L. and Hou, L. (2020) Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing. IEEE Journal on Selected Areas in Communications, 38, 1133-1146. [Google Scholar] [CrossRef]
|
|
[13]
|
Haarnoja, T., Zhou, A., Hartikainen, K., et al. (2018) Soft Actor-Critic Algorithms and Applications.
|
|
[14]
|
Fan, Z., Su, R., Zhang, W. and Yu, Y. (2019) Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space.
|
|
[15]
|
Haarnoja, T., Zhou, A., Abbeel, P., et al. (2018) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. 2018 International Conference on Machine Learning, Stockholm, 10-15 July 2018, 1861-1870.
|
|
[16]
|
Figurnov, M., Mohamed, S. and Mnih, A. (2018) Implicit Reparameterization Gradients. Advances in Neural Information Processing Systems, 31.
|