Article: A review of traffic-aware content caching vehicular social networks in the internet of vehicles Journal: International Journal of Vehicle Autonomous Systems (IJVAS) 2025 Vol.18 No.1 pp.31 - 53 Abstract: The Internet of Vehicles (IoV) is predicted to significantly enhance the safety and effectiveness of conveyance systems by supplying effectual data distribution among vehicles and people, vehicles and roads, vehicles and networks, vehicles and vehicles, etc. In this survey, 50 research papers on traffic-aware content caching are considered and reviewed. The numerous methods of traffic-aware content caching in vehicular social networks are categorised based on their computational mechanism, like Deep Learning, Optimisation-based techniques, Federated Learning and other techniques. Initially, for a reliable evaluation, research papers based on traffic-aware content caching in vehicular social networks from 2018 to 2023 are gathered. Thereafter, an extensive outline of traffic-aware content caching in vehicular social networks is depicted with its fundamental features. Then, the issues involved in the classified algorithms are enlightened. At last, the assessment is performed based on performance metrics, category analysis, publication year, data set and tools. Based on the estimation, it is concluded that many of the papers gathered depend on deep learning methods and SUMO is a widely used tool for execution. Inderscience Publishers - linking academia, business and industry through research

Title: A review of traffic-aware content caching vehicular social networks in the internet of vehicles

Authors: Vedha Vinodha Doraiswamy; Malathy Subramanium

Addresses: Department of ECE, JCT College of Engineering and Technology, Pichanur, Coimbatore 641105, Tamil Nadu, India ' Department of ECE, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, Tamil Nadu, India

Abstract: The Internet of Vehicles (IoV) is predicted to significantly enhance the safety and effectiveness of conveyance systems by supplying effectual data distribution among vehicles and people, vehicles and roads, vehicles and networks, vehicles and vehicles, etc. In this survey, 50 research papers on traffic-aware content caching are considered and reviewed. The numerous methods of traffic-aware content caching in vehicular social networks are categorised based on their computational mechanism, like Deep Learning, Optimisation-based techniques, Federated Learning and other techniques. Initially, for a reliable evaluation, research papers based on traffic-aware content caching in vehicular social networks from 2018 to 2023 are gathered. Thereafter, an extensive outline of traffic-aware content caching in vehicular social networks is depicted with its fundamental features. Then, the issues involved in the classified algorithms are enlightened. At last, the assessment is performed based on performance metrics, category analysis, publication year, data set and tools. Based on the estimation, it is concluded that many of the papers gathered depend on deep learning methods and SUMO is a widely used tool for execution.

Keywords: deep learning; optimisation-based techniques; federated learning; internet of vehicles; content caching.

DOI: 10.1504/IJVAS.2025.143037

International Journal of Vehicle Autonomous Systems, 2025 Vol.18 No.1, pp.31 - 53

Received: 14 Feb 2024
Accepted: 29 Apr 2024
Published online: 02 Dec 2024 *

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Title: A review of traffic-aware content caching vehicular social networks in the internet of vehicles

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