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VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks based on Path Planning and Deep Reinforcement Learning

Nyothiri Aung, Sahraoui Dhelim, Liming Chen, Wenyin Zhang, Abderrahmane Lakas, Huansheng Ning

TL;DR

A social-aware vehicular edge computing architecture that solves the content delivery problem by using some of the vehicles in the network as edge servers that can store and stream popular content to close-by end-users is proposed.

Abstract

Vehicular social networking is an emerging application of the promising Internet of Vehicles (IoV) which aims to achieve the seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks such as high mobility and frequent communication interruptions make content delivery to end-users under strict delay constrains an extremely challenging task. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some of the vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three components. First, we propose a social-aware graph pruning search algorithm that computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. Secondly, we use a traffic-aware content recommendation scheme to recommend relevant content according to their social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a Deep Reinforcement Learning (DRL) method to optimize the content provider vehicles distribution across the network. The results obtained from a realistic traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks based on Path Planning and Deep Reinforcement Learning

TL;DR

A social-aware vehicular edge computing architecture that solves the content delivery problem by using some of the vehicles in the network as edge servers that can store and stream popular content to close-by end-users is proposed.

Abstract

Vehicular social networking is an emerging application of the promising Internet of Vehicles (IoV) which aims to achieve the seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks such as high mobility and frequent communication interruptions make content delivery to end-users under strict delay constrains an extremely challenging task. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some of the vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three components. First, we propose a social-aware graph pruning search algorithm that computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. Secondly, we use a traffic-aware content recommendation scheme to recommend relevant content according to their social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a Deep Reinforcement Learning (DRL) method to optimize the content provider vehicles distribution across the network. The results obtained from a realistic traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

Paper Structure

This paper contains 12 sections, 9 equations, 6 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Vehicular edge architecture
  4. System modeling
  5. Content consumers path planning
  6. Traffic-aware content recommendation scheme
  7. Content provider distribution
  8. Experimental evaluation
  9. Experiment and dataset
  10. Baselines and metrics
  11. Results analysis
  12. Conclusion

Figures (6)

  • Figure 1: Content dissemination architecture
  • Figure 2: Social path selection
  • Figure 3: Comparing average delivery delay under (a) increasing velocity (b) increasing traffic density (c) different RSUs count
  • Figure 4: Comparing average delivery rate under (a) increasing velocity (b) increasing traffic density (c) different RSUs count
  • Figure 5: Comparing average vehicle travel time under (a) increasing velocity (b) increasing traffic density (c) increasing traffic accidents count
  • ...and 1 more figures