Authors: Associate Professor Dr. Channakeshava RN
Abstract: The rapid integration of 5G networks, Cellular Internet of Things (C-IoT), and Vehicular Ad Hoc Networks (VANETs) has enabled intelligent and highly connected vehicular environments. However, the high mobility of vehicles, rapidly changing channel conditions, varying traffic density, and heterogeneous service requirements create significant challenges for mobility management and radio resource allocation. Conventional mobility and resource management approaches generally react to network changes after they occur, resulting in increased handover latency, resource utilization inefficiency, communication failures, and degraded Quality of Service (QoS). This paper proposes an AI-driven framework for predictive mobility management and context-aware resource allocation in 5G-enabled Cellular-IoT VANETs. The proposed approach utilizes contextual information such as vehicle location, velocity, direction, traffic density, channel conditions, network load, and service requirements to predict future mobility and network states. Artificial Intelligence (AI) and Machine Learning (ML) techniques are employed to anticipate handover requirements and dynamically allocate communication resources according to predicted network conditions. The framework aims to jointly optimize mobility decisions and resource allocation while considering QoS, latency, throughput, spectrum utilization, and energy efficiency. The proposed approach is intended to reduce unnecessary handovers, improve resource utilization, minimize communication interruptions, and enhance the reliability of vehicular-IoT services. Simulation-based evaluation can be performed against conventional mobility management and resource allocation techniques to demonstrate the effectiveness of the proposed framework under varying vehicle mobility and network-load conditions.
