# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 1 Introduction
Multiple naming conventions and effective caching mechanisms are proposed to address the efficiency of content retrieval in conjunction with social awareness and clustering [11]. Despite the promise of the CCN, challenges such as ineffective dynamic cache placement, high resource consumption under load, and a lack of context-aware decisions limit its scalability. SARAC4N targets these issues by leveraging cluster-based caching and social centrality-based cache selection. While traditional mechanisms in ICN address resource optimization to some extent, they often overlook the integration of social factors and the strategic placement of caching nodes within dynamically formed clusters. This study introduces SARAC4N, a novel framework that combines social awareness (via node centrality and interaction metrics) with available computational resources (CPU, memory, storage, and link capacity) to enhance caching decisions. The proposed model reduces data retrieval time, minimizes error rates,lowers content duplication, and improves overall network efficiency. By integrating social context and clustering techniques, SARAC4N significantly enhances ICN performance compared to conventional approaches [12]. While SARAC4N integrates both social awareness and resource utilization, we acknowledge that some recent studies [13; 14; 15] and also other studies such as [16; 17; 18; 19; 20], have begun addressing resource-aware strategies in ICN. However, these approaches often overlook the dynamic and social contextual dimensions embedded within node interactions in clustered environments. Figure 1: Comparison of protocol suit (TCP/IP and Named Data Network protocol architecture). Figure 2: NDN packet architecture. This research introduces SARAC4N, a novel framework that advances Content-Centric Networking through five key innovations. First, we develop a social-aware clustering approach that groups nodes based on social relationships and communication proximity, utilizing centrality measures (degree, betweenness, and closeness) rather than simple topological characteristics. This ensures that nodes with similar content interests are strategically grouped, maximizing cache efficiency and minimizing redundant distribution. Second, we introduce an intelligent dual-leadership architecture featuring resource-aware cluster head selection based on computational power, memory capacity, storage availability, and bandwidth. The framework establishes both primary cluster heads and backup nodes, creating fault-tolerant hierarchies that ensure continuous service availability and eliminate single points of failure. Third, SARAC4N implements dynamic content allocation that adapts to real-time network conditions and user behavior patterns. Unlike static caching policies, our context-aware placement mechanism considers node relevance, historical request patterns, and geographical proximity to strategically position content at socially central nodes. Fourth, we integrate advanced congestion detection using reinforcement learning for optimal traffic distribution. The system proactively monitors resource utilization, predicts potential bottlenecks, and intelligently redistributes requests to secondary heads or neighboring nodes while continuously improving through pattern learning. Finally, comprehensive evaluation across diverse topologies demonstrates SARAC4N's superior performance with a 30-50% improvement in memory efficiency and up to a 40% reduction in retrieval times compared to baseline approaches. The framework consistently outperforms existing methods across cache hit ratios, content redundancy, error rates, and network throughput, establishing SARAC4N as a comprehensive solution addressing multiple networking challenges simultaneously. In the context of networking, social awareness refers to the ability of nodes to make caching and routing decisions based on their interaction frequency, structural importance (centrality), and communication patterns within the network.