A Proactive Caching Approach to Boost Network Offloading in Urban Areas

The rapid growth of 6G networks and the Internet of Things (IoT) has increased the need for advanced and adaptive data management. In response, our research brings together multiple devices, including smartphones, bicycles, and in-vehicle systems, under a single user preference model to propose a novel proactive caching framework that surpasses traditional single-device approaches that give each device a unique set of preferences. Exploiting the advantages of both Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, our proposed approach minimizes core network congestion and increases spectrum efficiency. At its core is the Cognitive File Caching Algorithm (CFCA), a proactive, scalable, and adaptive approach designed to address complex challenges involving varying file popularity in cities and user movement patterns. Our proposed CFCA improves caching efficiency by clustering users based on shared zones of interest and behavioral data, achieving a 27% improvement in network offloading and a 24% increase in cache hit ratio over existing benchmarks. Furthermore, the study explores the economic implications of proactive caching, establishing a strategic balance that ensures long-term profitability while minimizing high-cost caching inefficiencies. This study marks a paradigm advancement in proactive caching, setting the groundwork for future-ready 6G networks by combining user-centric data models, scalable algorithms, and adaptive file exchange mechanisms to address ever-increasing traffic needs.