# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review
### Edge-/IoT-Based Caching  
The Internet of Things (IoT) paradigm has transformed network architecture by interconnecting a wide array of heterogeneous devices such as smartphones, vehicles, industrial sensors, and household appliances into a unified data ecosystem. To handle the massive volume and real-time requirements of IoT-generated data, several edge-centric caching strategies have been proposed. Approaches like ClientCache, TCCN (Time-Constrained Caching in NDN), FMC (Fog-Based Multi-Layer Caching), and SleepCaching aim to store frequently accessed data closer to end devices, thereby reducing latency and backhaul congestion. These mechanisms improve data accessibility and support reachability across diverse node types, including low-power or intermittently connected devices.  
Despite their benefits, most existing IoT caching schemes focus heavily on proximity and availability, without integrating social relevance, node importance, or resource state into their decision logic. As a result, they struggle with content duplication, inefficient cache usage, and poor adaptability in highly dynamic or dense environments. SARAC4N addresses these limitations by embedding socially aware clustering and resource-optimized cache placement into the IoT-edge context, enabling more intelligent, responsive, and scalable caching in distributed IoT networks.  
Edge- and IoT-based caching strategies like ClientCache, FMC, and SleepCaching enhance latency reduction and local data availability across heterogeneous devices. Despite their effectiveness in decentralized environments, they often struggle with limited resource capacity, inconsistent connectivity, and lack of coordination between nodes. These approaches typically lack a global caching policy informed by social or cluster-level interactions. SARAC4N extends edge caching by introducing coordinated clustering, social-metric-guided cache node selection, and workload balancing, thereby improving data consistency, fault tolerance, and cache efficiency even in resource-constrained IoT scenarios.  
ICN's pervasive caching strategies characterize a major shift in how content is stored and retrieved, focusing on reducing data duplication, optimizing resource utilization, and improving content delivery mechanisms. By caching content closer to requesters and adopting intelligent caching strategies that can increase data retrieval time, the ICN proposes a more efficient, scalable, and adaptable network architecture. [19, 20]  
Recent work by [PERSON] et al. [21] introduced a socially aware edge collaborative caching framework for the Internet of Vehicles, leveraging deep reinforcement learning and digital twin integration to enhance caching decisions. While this approach effectively integrates social assistance and predictive learning, it primarily focuses on vehicular mobility and latency in real-time transport systems. In contrast, **SARAC4N** generalizes the social-awareness model using centrality-based node scoring (degree, betweenness, and closeness) and resource profiling to support diverse clustered ICN environments. It operates independently of specific mobility models and is optimized for both intra- and inter-cluster content disbursement, enabling higher scalability and adaptability in heterogeneous networks.