A Cooperative Caching Scheme for VCCN With Mobility Prediction and Consistent Hashing

Yao, Lin; Xu, Xiao‐Ying; Deng, Jing; Wu, Guowei; Li, Zhaoyang

doi:10.1109/tits.2022.3171071

Cited by 13 publications

(4 citation statements)

References 41 publications

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“…The cooperation between DDQN and Fog access Point (F-AP) is applied to formulate the global optimal caching strategy. Yao et al [ 27 ] proposed the problem of cooperative caching in vehicle content centers, which comprehensively considered the future location of edge nodes and the cache redundancy between cooperative nodes to formulate a replacement strategy. Yang et al [ 28 ] studied the multi-hop cooperative caching of wireless sensor networks on ICN and made a trade-off between saving energy and reducing delay.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

Jin

Xing

2023

Sensors

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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%.

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Section: Related Workmentioning

confidence: 99%

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

Jin

Xing

2023

Sensors

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“…In [19], Chen et al proposed a cooperative edge caching scheme in VEC which considered the location-based contents and the popular contents, while designing an optimal scheme for cooperative content placement based on an ant colony algorithm to minimize the total transmission delay and cost. In [20], Yao et al designed a cooperative edge caching scheme with consistent hash and mobility prediction in VEC to predict the path of each vehicle, and also proposed a cache replacement policy based on the content popularity to decide the priorities of collaborative contents. In [21], Wang et al proposed a cooperative edge caching scheme in VEC based on the long short-term memory (LSTM) networks, which caches the predicted contents in RSUs or other vehicles and thus reduces the content transmission delay.…”

Section: Related Workmentioning

confidence: 99%

“…The local RSU calculates the reward r(t) according to Eqs. ( 16) - (20) and state s(t) transits to the next state s(t+1), then the local RSU observes s(t+1). Next, the neighboring RSU randomly samples c popular contents that are not cached in s(t+1) as its cached contents, which is denoted as s n (t + 1).…”

Section: System Modelmentioning

confidence: 99%

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Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Wu¹,

Yu²,

Fan³

et al. 2022

Preprint

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The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy.

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Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles

Ou,

Chen,

Huang

2024

Lecture Notes in Computer Science

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A Cooperative Caching Scheme for VCCN With Mobility Prediction and Consistent Hashing

Cited by 13 publications

References 41 publications

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles

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