# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
\[CEN_{degree}(x)=\sum_{y=1}^{n}A_{xy} \tag{2}\]

Figure 6: SARAC4N framework illustrating the step-by-step formation of socially aware clusters, selection of primary and secondary cluster heads based on resource metrics, and neighbor cluster cooperation for popularity-driven content caching and efficient network coordination. where \(\left(A_{xy}\right)\) concludes with the evidence of a link among data nodes \(\left(x\right)\) and \(\left(y\right)\) in the contiguous matrix. \(\left(n\right)\) is the overall figure of data caching data points in the network. The degree of betweenness:

The total quantity of short paths comprised in an apex (x) is believed to be its betweenness centrality. It is explained as in Equation (3):

\[CEN_{betweenness}(x)=\sum_{s\
eq i\
eq t}\frac{\sigma_{st}(x)}{\sigma_{st}} \tag{3}\]

where \(\left(\sigma_{yz}\right)\) is the total quantity of the shortest data paths between node \(\left(y\right)\) and node \(\left(z\right)\). \(\left(\sigma_{yz}(x)\right)\) is the number that presents the data paths which pass through the node \(\left(x\right)\). The degree of closeness:

The degree of closeness for a node \(\left(x\right)\) is the inverse of the total of the shortest data path distances from data node \(\left(x\right)\) to all the others. The closeness is calculated as explained in Equation (4):

\[CEN_{closness}(x)=\frac{1}{\sum_{z\
eq y}distance(y,z)} \tag{4}\]

where \(\left(\text{distance}(y,z)\right)\) denotes the shortest data path from nodes \(\left(y\right)\) and \(\left(z\right)\). The selection of nodes for optimization:

Inter-cluster communication ensures continuity when content is not available locally. It prevents request looping and enhances content diversity across clusters while reducing the average content distance. The mechanism for selecting the optimum data node for the degree of betweenness and closeness centrality is implemented via a weighted combination and ordering of the centrality metrics, respectively. For instance, a ranked sum is explained in Equation (5):

\[\text{Score}(x)=w_{degree}\times CEN_{degree}(x)+w_{betweenness}\times CEN_{ betweenness}(x)+w_{closness}\times CEN_{closness}(x) \tag{5}\]

where \(\left(w_{\text{degree}}\right)\)_is_ the _centrality degree_, \(\left(w_{\text{betweenness}}\right)\)_is_ the _betweenness degree_, and \(\left(w_{\text{closness}}\right)\) is the closeness degree, where ranks are affiliated with each degree measure, explaining their corresponding value of importance. The weights for w-degree, w-betweenness, and w-closeness were empirically tuned using a simulation-based grid search. Initial values were set equally (0.33 each), and incremental adjustments were made based on the cache hit ratio and retrieval latency. The final weights used were as follows: w-degree = 0.4, w-betweenness = 0.3, and w-closeness = 0.3, prioritizing connectivity slightly more. While static K-means clustering is used in the current implementation for initial simplicity and reproducibility, we acknowledge the inherent dynamism of IoT networks. To partially address this, the clusters are refreshed periodically (every 1000 simulation ticks). Future work may incorporate real-time, mobility-aware re-clustering strategies using fuzzy or online K-means variants to enhance adaptability. ### Allocation of Resources

The proposed setup explains a state where the quantity of incoming data requests is more than the capacity of the respective data node. To address this problem, the secondary node performs as a backup to the primary node and takes care to disperse the information in the segmented cluster. Load is shared, resources are shared, and as a result the overhead is reduced at both nodes. Let us assume a simple model:Let Lr represent the total amount of incoming requests in term of load. Assume that Com denotes the computational capacity, device memory, device storage, and capacity of the network with respect to the performance at a node. Let Lc signify the grave threshold when the data node is choked.