# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
In contrast, SARAC4N enhances performance by integrating social awareness, resource optimization, and dynamic cluster-based management, which collectively reduce latency, improve memory utilization, and lower content redundancy. The results show that SARAC4N outperforms all benchmarks by delivering a more adaptive and context-aware caching framework under varying network conditions. ### Baseline Methods

To benchmark the effectiveness of the proposed SARAC4N framework, we compared its performance with several existing caching strategies widely referenced in the literature. The CCndnS scheme operates as a coordinated caching mechanism that leverages content popularity and node hierarchy, placing frequently requested data closer to the requesting nodes to minimize retrieval latency. The CSQR (Cluster-Based Socially Aware Query Routing) approach utilizes social interaction graphs and content affinity scores to guide Interest packet forwarding and determine cache placement across clusters, aiming to improve content relevance and delivery efficiency. The CB-PC-DMM (Centrality-Based Popularity-Centric Dynamic Memory Management) model combines node centrality metrics (e.g., degree and betweenness) with content popularity trends to dynamically adjust memory allocation and optimize cache storage across the network. These baseline schemes represent a diverse set of strategies focused on popularity awareness, social relevance, and adaptive caching, and were selected to provide a well-rounded comparative evaluation against the proposed SARAC4N mechanism. While the current study is based on simulations in NS3 due to the scale and flexibility of parameter control, we acknowledge the necessity of validating SARAC4N in real-world environments. A real deployment scenario involving a campus-wide IoT deployment and multimedia content dissemination is under consideration. Future work will include benchmarking SARAC4N against existing caching strategies using real traffic data, latency logs, and bandwidth utilization metrics under identical environmental conditions to ensure generalizability and system resilience. ## 5 Conclusions

This paper introduced SARAC4N, a socially and resource-aware caching strategy tailored for clustered Content-Centric Networks (CCNs). Through extensive simulation-based evaluations across diverse network topologies and performance metrics, SARAC4N consistently outperformed existing schemes in terms of data retrieval time, memory efficiency, content duplication, and resource-aware content placement. The framework demonstrated effective clustering, efficient utilization of computational and storage resources, and incorporation of social-contextual factors for adaptive caching. Moreover, SARAC4N proved its robustness and adaptability in dynamic and heterogeneous environments, confirming its suitability for large-scale, data-intensive applications such as IoT networks and smart cities. Despite these promising results, there remains room for future enhancements. The current framework assumes static cluster configurations; hence, integrating support for cluster mobility and real-time re-clustering will be essential for applications with frequently changing content and user mobility. Additionally, embedding AI/ML-driven decision-making can enhance the intelligence and responsiveness of caching operations, particularly in latency-sensitive domains like the Tactile Internet. Despite its promising results, SARAC4N has several limitations. First, it currently employs static clustering, which may not adapt optimally to highly dynamic environments. Second, the framework has only been evaluated via simulation, and real-world deployment or testbed validation is pending. Finally, the current design lacks explicit support for mobility-aware cache updates or handovers. Methodology, A.R.K. ; Formal analysis, U.S.; Resources, U.S.; Data curation, H.B.L. All authors equally contributed. All authors have read and agreed to the published version of the manuscript. This research received no external funding.