Efficient Caching in Cyber-Physical-Social Systems Based on Social-Aware and Popularity Prediction

Cyber-Physical-Social Systems (CPSS) aim to enhance societal efficiency by integrating intelligent, interactive systems. To support CPSS, data is often gathered from the cloud, but non-real-time cloud-user connections affect Quality-of-Service (QoS). To solve this, we propose offloading storage to the edge, caching popular content near users. We select cluster heads using local clustering, social intimacy, and betweenness centrality. Then, we group users using the Pairwise Constrained K-Means-Monotonic (PCKMM) algorithm based on distance, relationships, and content preferences. Using unsupervised recurrent federated learning (URFL), we predict content popularity and adjust clusters dynamically. Content is cached with a popularity-driven greedy strategy, reducing access latency.