| Title |
Multimedia Analytics Based On Air-assisted Vehicle Network In Smart City |
| ID_Doc |
36023 |
| Authors |
Song, YQ; Shen, Y; Ding, P |
| Title |
Multimedia Analytics Based On Air-assisted Vehicle Network In Smart City |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/BMSB55706.2022.9828585 |
| Abstract |
In autonomous vehicles, multimedia analytics is a potential technology to improve the safety and intelligence of the Internet of Vehicles (IoV), which use UAVs as air communication carriers. However, MEC servers are usually deployed in base stations in a fixed way, while the air-assisted Internet of Vehicles (IoV) in smart city need access to distribute massive multimedia analytics services. Moreover, the demand for computing resources is different, and in this paper, hencely, integrating edge computing and blockchain technology into the Internet of Vehicles to lower transaction throughput and reduce the latency of multimedia analytics in the Internet of Vehicles, so that the time-varying computing resource demands can be satisfied. It is designed that a Markov decision process for resource allocation in air-assisted IoV of smart city, which can be addressed by a deep reinforcement learning algorithm. Simulation results show that the proposed method can improve the performance of multimedia analytics in the air-assisted IoV. |
| Author Keywords |
Internet of Vehicles; Multimedia Analytics; MEC; Deep Reinforcement Learning; Smart City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000948925900119 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications |
| Research Area |
Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications |
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