| Autor: |
Longzhe Han, Sheng Li, Chenchen Ao, Yan Liu, Guangming Liu, Yiying Zhang, Jia Zhao |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Wireless Communications and Mobile Computing. |
| ISSN: |
1530-8669 |
| DOI: |
10.1155/2022/8777890 |
| Popis: |
Multimedia applications are expected to widely deploy over vehicular networks. In order to meet the low-latency and high-speed transmission requirements of multimedia applications, edge caching is introduced to reduce the network traffic and the transmission delay. Due to the limited storage of the edge cache server, an efficient approach for the content management plays a decisive role for the edge cache performance. This paper proposes a vehicle-to-infrastructure-based cooperative caching mechanism for Internet of Vehicles to improve the edge cache utilization. The system model is established with the goal of maximizing the cooperative caching hit rate. To jointly consider the collaborations between macrobase stations (MBS) and multiple roadside units (RSU), we propose a reinforcement learning algorithm to adaptively control the cache management. According to the content popularity and the network status, the proposed algorithm can dynamically adjust cached content across relevant MBSs and RSUs. The simulation results show that the proposed cooperative caching mechanism significantly improve the cache utilization and the quality of services. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
Plný text