# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
Node failure scenarios were implemented to examine network vulnerability and recovery mechanisms, providing insights into the fault tolerance capabilities of the proposed clustering approach. Comprehensive caching strategy comparisons were conducted through detailed analysis of cache hit and miss ratios during content retrieval operations, enabling quantitative assessment of storage efficiency and content availability. Real-time monitoring of data delivery times from consumer to producer nodes provided critical insights into overall network efficiency and responsiveness. The evaluation framework specifically examined load balancing and resource sharing mechanisms to assess their effectiveness in preventing network congestion and optimizing resource utilization. Additionally, comprehensive analysis of data transmission errors and content redundancy patterns was conducted to evaluate the reliability and efficiency of the proposed socially aware caching approach. The extensive simulation results demonstrated that Named Data Networking, particularly when enhanced with the SARAC4N framework, offers significant advantages over conventional IP-based networks. The findings revealed measurable improvements in network performance and efficiency, characterized by reduced data error rates and minimized data retrieval times across various operational scenarios and network topologies. ## 4 Results and Discussion

The comparison of the caching schemes with respect to the identified metrics is presented in graphs. A very clear and strong analysis with deep insights into network efficiency and performance is demonstrated in this research. SARAC4N beats the all-caching strategies, and empirical results show that SARAC4N's socially aware caching approach's capability is far better for optimizing network paths, reducing bottlenecks, and enhancing user experience. To quantitatively evaluate SARAC4N, several key performance metrics were considered. These included data retrieval time, memory utilization, error rate, average content distance, and cache store changes. Table 3 summarizes each metric and its purpose within the evaluation framework. ### Data Retrieval Time

The \"Data Retrieval Time\" graph in Figure 7 compares the four caching schemes, MCD, LCD, CCndns, and SARAC4N. SARAC4N constantly outclassed its competitors in \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Description** \\ \hline Data Retrieval Time & The time taken to fetch the content from the network after sending a request. \\ Performance & Overall effectiveness combining latency, hit rate, and resource efficiency. \\ Memory Utilization & The percentage of cache memory being effectively used across the clusters. \\ Error Rate & The proportion of content requests that result in failed or incorrect fetches. \\ Probability of Data Finding & Likelihood of requested data being available in the nearby cluster cache. \\ Average Content Distance & The average number of hops needed to retrieve the requested content. \\ Content Store Changes & The frequency of updates (additions/replacements) in the content cache store. \\ Topology-Specific Analysis & Evaluates SARAC4N’s adaptability and robustness across hierarchical, grid, and \\  & random topologies in terms of cache hit ratio, resource usage, and retrieval efficiency. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of performance metrics used in SARAC4N evaluation. data retrieval time; MCD showed comparatively better values. LCD consistently took more time to retrieve the data, while CCndnS gave abrupt variations. SARAC4N stayed at the top with a shorter data fetching time. Clustering with the SAC mechanism aligned itself perfectly with the partitioning, where CH and CSH selection streamlined the data retrieval processes more quickly. Additionally, the social engagement metric with respect to the node's position in the cluster significantly lowered the latency and increased efficiency in data fetching time.