Work

Dynamic Content Popularity Prediction Based on Spatial-Temporal Feature in Vehicular Networks

Year: 2025

Type: article

Abstract: Content popularity is crucial for edge caching in vehicular networks, where prediction accuracy directly impacts communication performance. To enhance prediction accuracy by fully exploiting the characteristics of content popularity, we propose a novel approach that integrates spatial correlation among roadside units and the temporal correlation of vehicle content requests. We jointly construct a spatial-temporal feature matrix using Euclidean distance and Spearman correlation, called the ES matrix, as the adjacency matrix of the graph structure. This ES matrix is integrated with Spatial-Temporal Transformer Network (STTN) to build the ES-STTNS model. To evaluate its effectiveness, we design a dynamic caching strategy that continuously updates RSU cache in real-time based on predicted content popularity, aiming to maximize Cache Hit Ratio (CHR). Extensive experiments demonstrate that ES-STTNS achieves highly accurate predictions for real vehicular content requests, reducing Mean Absolute Error (MAE) by up to 24.4% and Root Mean Square Error (RMSE) by up to 20.4% compared to temporal-only models, especially over longer prediction horizons, while achieving 90% CHR at higher cache capacities.

Source: Intelligent and Converged Networks

DOI: 10.23919/icn.2025.0029

Authors: Ying Shi, Yuxia Zang, Tong Wang, Yiping Chen, Chi Feng

Institutions: Harbin Engineering University, Heilongjiang University of Science and Technology

Language: English

Topic: Caching and Content Delivery

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities