Abstract

Advancements toward 6G mobile networks are expected to transform telecommunications wireless technology by facilitating ultra-low latency, extremely high data rates, massive device connectivity, and intelligence that recognizes user context. One of the most critical issues of a dynamic ecosystem with seamless user experience is efficient mobility management. The traditional mobility management methods employed in 4G and 5G networks are likely to face challenges in the increasingly sophisticated and heterogeneous world of 6G, particularly with its multi-access architectures. AI, network slicing, edge computing, and analytic processing, among other technologies, will shape the future of 6G. This paper focuses on designing and implementing Context-Aware Mobility Management (CAMM) frameworks for mobile Internet networks, with a scope that extends to 6 G. CAMM policies are formulated to respond instantaneously with real-time contextual information on mobility, including user behavior, device capabilities, application requirements, network conditions, and spatial-temporal patterns. CAMM utilizes AI/ML techniques, intensive learning, and reinforcement learning models to predict user trajectories, optimize handovers, and minimize latency and signaling overhead. Edge intelligence is integrated alongside distributed data analytics to enhance low-latency responsiveness and localization of the decision-making process. Additionally, it addresses issues such as privacy preservation, context data collection, and interoperability across disparate access networks (i.e., Wi-Fi 7, satellite, and terahertz). Simulation results validate the effectiveness of CAMM in reducing handover failures by up to 40% and improving Quality of Experience (QoE) metrics across various applications, including autonomous vehicles, smart cities, and remote healthcare. The proposed context-aware mobility management architecture provides a customizable and flexible scaffolding to address the rigorous demands of upcoming 6G networks. It highlights the role of context fusion, intelligent prediction, and distributed orchestration as primary elements for the following mobility strategies. This research lays the groundwork for implementing robust and adaptive mobility services in an evolving mobile Internet environment.