Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
With the continuous development of intelligent vehicles, people’s demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/10/4619 |
| _version_ | 1797598421330165760 |
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| author | Honghai Wu Jichong Jin Huahong Ma Ling Xing |
| author_facet | Honghai Wu Jichong Jin Huahong Ma Ling Xing |
| author_sort | Honghai Wu |
| collection | DOAJ |
| description | With the continuous development of intelligent vehicles, people’s demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%. |
| first_indexed | 2024-03-11T03:21:57Z |
| format | Article |
| id | doaj.art-fd531d7df78941b299e46849c7d7cccd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:21:57Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd531d7df78941b299e46849c7d7cccd2023-11-18T03:09:59ZengMDPI AGSensors1424-82202023-05-012310461910.3390/s23104619Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge NetworkHonghai Wu0Jichong Jin1Huahong Ma2Ling Xing3School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaWith the continuous development of intelligent vehicles, people’s demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%.https://www.mdpi.com/1424-8220/23/10/4619vehicle edge networkcooperative cachetemporal convolutional networks |
| spellingShingle | Honghai Wu Jichong Jin Huahong Ma Ling Xing Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network Sensors vehicle edge network cooperative cache temporal convolutional networks |
| title | Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network |
| title_full | Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network |
| title_fullStr | Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network |
| title_full_unstemmed | Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network |
| title_short | Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network |
| title_sort | hybrid cooperative cache based on temporal convolutional networks in vehicular edge network |
| topic | vehicle edge network cooperative cache temporal convolutional networks |
| url | https://www.mdpi.com/1424-8220/23/10/4619 |
| work_keys_str_mv | AT honghaiwu hybridcooperativecachebasedontemporalconvolutionalnetworksinvehicularedgenetwork AT jichongjin hybridcooperativecachebasedontemporalconvolutionalnetworksinvehicularedgenetwork AT huahongma hybridcooperativecachebasedontemporalconvolutionalnetworksinvehicularedgenetwork AT lingxing hybridcooperativecachebasedontemporalconvolutionalnetworksinvehicularedgenetwork |