A review on the edge caching mechanisms in the mobile edge computing: A social-aware perspective

• Published in: Internet of things (Amsterdam. Online) Vol. 22; p. 100690
• Main Authors: Reiss-Mirzaei, Mohammad, Ghobaei-Arani, Mostafa, Esmaeili, Leila
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023
• Subjects: Edge caching; Literature review; Mobile edge computing; Social relationship; Social-aware; User behavior; User mobility; User preference; Edge caching; User mobility; Social relationship; Mobile edge computing; Literature review; Social-aware; User preference; User behavior
• ISSN: 2542-6605; 2542-6605
• DOI: 10.1016/j.iot.2023.100690

•Providing a comprehensive review of the existing social-aware caching approaches in edge computing.•Classifying the social-aware edge caching approaches into four main categories: game theory-based, machine learning-based, model-based, and heuristic-based approaches.•Analyzing the key aspects of social-aware edge caching approaches for improving caching strategies in future research.•Providing a summary of the issues ahead and potential challenges in social-aware edge caching. In recent years, we have witnessed the rapid development of the 5G technology, the computing capabilities of smartphones, and the use of these technologies and types of smart devices by users in everyday life. Along with the rapid growth of these technologies, new architectures at mobile edge caching (MEC) are also evolving. One of the aspects of smartphone's popularity is using social networks and sharing content between users of these networks. Generating various types of content on social networks is impressive and has caused huge data traffic in the network. Therefore, providing edge caching mechanisms in terms of social relationships and behavioral characteristics of users to reduce network traffic and delays in accessing popular content should be considered. As far as we know, no review paper has been provided in this context. In this paper, we intend to express the literature on the subject and then review the social-aware edge caching mechanisms. Then, we consider users' social and behavioral characteristics that are effective in caching strategies. Furthermore, we provide a taxonomy of social-aware edge caching approaches consisting of game theory-based, machine learning-based, model-based, and heuristic-based approaches. Finally, this study concludes by outlining some of the challenges ahead and research future directions.