| Title |
The 1st Workshop on 5G and machine learning for IoT and unmanned aerial vehicles (UAV) |
| ID_Doc |
44699 |
| Authors |
Leung, H; Xie, N |
| Title |
The 1st Workshop on 5G and machine learning for IoT and unmanned aerial vehicles (UAV) |
| Year |
2023 |
| Published |
|
| DOI |
10.1109/WF-IOT58464.2023.10539526 |
| Abstract |
The emergence of 5G and machine learning technologies leads to new opportunities and challenges to exploit new features for performance and security of Internet of Things (IoT) and robotic systems. The development of a new 5G-enabled and machine learning-aided communications & computing framework towards enhanced decision-making in IoT will be important in scenarios such as real-time surveillance for smart city, environmental sustainability, and defense applications. With advantages in mobility, higher line-of-sight and ease of use, unmanned aerial vehicles (UAV) has high potential for developments to enhance communication and processing. This workshop discussed the research and progress made in this direction, including some of the synergies and opportunities in 5G-UAV processing to enhance terrestrial IoT networks, machine learning for UAV optimization and prediction in 5G networks, as well as UAV's for situation awareness. The development of a new 5G-enabled and machine learning (ML)-aided communications & computing framework towards enhanced decision-making in IoT will be important in scenarios such as real-time surveillance for smart city, sustainability, and defence. |
| Author Keywords |
Unmanned Aerial Vehicles; 5G; machine learning; IoT; situation awareness; UAV networks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001241286500140 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://ieeexplore.ieee.org/ielx7/10539362/10539375/10539526.pdf
|