Deliverables
| No. | Deliverable Title | Lead By | Due Date | Achieved |
|---|---|---|---|---|
| 1.1 | Data Management Plan (DMP) | UNEXE | M6 | Yes |
| 1.2 | Progress report 1 | UNEXE | M13 | Yes |
| 1.3 | Mid-term meeting report | UNEXE | M18 | Yes |
| 1.4 | Progress report 2 | UNEXE | M37 | On progress |
| 2.1 | Report on the system requirements analysis, architecture design and functional components | eNEB | M24 | On progress |
| 3.1 | Report on the model and mechanism for simultaneous power and information transfer | UiO | M42 | On progress |
| 4.1 | Report on the AI models and algorithms for Aerial-Terrestrial IoT networks | UVa | M30 | On progress |
| 5.1 | Report on AI-powered network management and anomaly detection | GS | M42 | On progress |
| 6.1 | Report on systems integration, experiments, testing, and evaluation results | CIP | M48 | On progress |
Main Publications (Up to M18)
[1] Y. Luo, C. Luo, G. Min, G. Parr and S. McClean, "On the Study of Sustainability and Outage of SWIPT-Enabled Wireless Communications", IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 5, pp. 1159-1168, Aug. 2021,DOI: 10.1109/JSTSP.2021.3092136.
[2] Tong Ding, Ning Liu, Zhong-Min Yan, Lei Liu, and Li-Zhen Cui. An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection. Journal of Computer Science and Technology, 2022, 37(6): 1356-1368.
[3] J. Mills, J. Hu, G. Min, R. Jin, S. Zheng, J. Wang, Accelerating Federated Learning with a Global Biased Optimiser, IEEE Transactions on Computers, doi: 10.1109/TC.2022.3212631,2022.
[4] J. Mills, J. Hu, G. Min, Client-Side Optimization Strategies for Communication-Efficient Federated Learning, IEEE Communications Magazine, vol. 60, no. 7, pp. 60 - 66, 2022.
[5] R. Jin, J. Hu, G. Min, H. Lin, Byzantine-Robust and Efficient Federated Learning for the Internet of Things, IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 114 - 118, 2022.
[6] J. Mills, J. Hu, G. Min, Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing, IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 3, pp. 630-641, 2022.
[7] Tong Ding, Lei Liu, Yi Zhu, Lizhen Cui, Zhongmin Yan, IoV environment exploring coordination: A federated learning approach, Digital Communications and Networks, 2022, In Press, DOI: 10.1016/j.dcan.2022.07.006.
[8] Zhang J, Luo C, Carpenter M, Min G. (2022) Federated Learning for Distributed IIoT Intrusion Detection using Transfer Approaches, IEEE Transactions on Industrial Informatics, volume PP, no. 99, pages 1-11, DOI: 10.1109/tii.2022.3216575.
[9] J. Wang, J. Hu, G. Min, Q. Ni, T. El-Ghazawi, Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach, IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2022.3197706, 2022.
[10] J. Wang, J. Hu, G. Min, W. Zhan, A. Y. Zomaya, N. Georgalas, Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning, IEEE Transactions on Computers, vol. 71, no. 10, pp. 2449 - 2461, 2022.
[11] Lei Liu, Tong Ding, Hui Feng, Zhongmin Yan, Xudong Lu, Tree sketch: An accurate and memory-efficient sketch for network-wide measurement, Computer Communications, 2022, 194: 148-155.