# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
Figure 11: Cache hit probability versus cache size for SARAC4N and baseline caching schemes (MCD, LCD, and CCndnS). SARAC4N consistently achieves superior performance, especially in mid-sized caches. Figure 10: Error rate comparison of caching schemes (SARAC4N, CCndns, LCD, and MCD) across four trials. LCD: Least Recently Cached Data; MCD: Most Centralized Data. ### Average Content Distance

The \"Average Content Distance\" graph in Figure 12 shows the numbers of hops required to fetch the desired content from the data origin. SARAC4N has shortest content distance across multiple cache sizes, starting from eight hop counts to four hop counts, as it proposes to store data on the basis of social awareness and the location of the requester. CCndnS, LCD, and MCD have higher hop counts. The graph explains how the proposed SARAC4N methodology precisely pinpoints the destination node and manages user requests by considering its cluster-based and social-factor-based management mechanism. This approach has predominantly benefited high-traffic networks, as it ensures faster and more efficient access to the required information in a shorter time. ### Content Store Changes

The graph of \"CS Search\" in Figure 13 exposes the number of explorations in the data store, which can be changed if the size of the store or the content itself is different. As per received number of requests, both SARAC4N and CCndnS present the high search activity. On the other hand, LCD and MCD show minimal searches, indicating a more static approach to caching with limited updates and content checks. Figure 12: Comparison of average content distances across cache sizes for SARAC4N, CCndnS, LCD, and MCD strategies. Figure 13: Comparison of CS search frequency versus cache size across SARAC4N, CCndnS, LCD, and MCD strategies. It is obvious from these graphs that an effective caching and data storage strategy can enhance efficiency in data retrieval along with other important metrics. SARAC4N explicitly outperformed its peers through its lower data redundancy and maintained a high number of searches, as well keeping the most required data in its cache and removing unneeded content from store and saving storage. The steady search numbers for SARAC4N and CCndnS suggest that their cache management is sophisticated, precise, proactive, and responsive. ### Topology-Specific Analysis

To assess the generalizability and adaptability of SARAC4N, simulations were conducted using three distinct topologies: hierarchical, grid, and random topologies. Each topology emulates a different real-world deployment scenario. The hierarchical topology resembles enterprise or campus networks, where traffic is tiered and structured. The grid topology mimics structured environments like smart grids or urban sensor networks, while the random topology reflects dynamic and less predictable IoT and peer-to-peer (P2P) systems. Across these topologies, SARAC4N consistently outperformed traditional caching methods in terms of cache hit ratio, CPU utilization, and data retrieval time. In the hierarchical setup, SARAC4N leveraged socially aware clustering to minimize inter-tier communication delays. In the grid topology, the structured layout allowed SARAC4N's resource optimization to enhance spatial locality, reducing redundant caching. The random topology posed more unpredictable challenges, yet SARAC4N still maintained performance stability due to its dynamic node selection and backup strategies via cluster secondary heads. The results confirm SARAC4N's robustness and adaptability in heterogeneous network conditions, demonstrating its suitability for both static and dynamic real-world environments. The benchmarking schemes used for comparison included LCD, MCD, CCdnS, CSQR, and CB-PC-DMM. These represent traditional caching mechanisms that optimize content placement based on basic principles like node hierarchy, proximity, and probabilistic popularity.