Effective traffic management in smart cities demands robust link selection and reliable communication under dynamic conditions. To address these needs, we present a deep learning (DL)-based framework for intelligent link selection using LTE V2X communication data. Our approach utilizes LSTM, GRU, and 1D-CNN models to predict signal-to-noise ratio (SNR) patterns across multiple network operators. Among these, the GRU model achieved the highest performance with a 17.3% R2score, effectively modeling temporal variations in signal quality. The framework incorporates operator-specific optimization by accounting for signal strength, network congestion, and vehicular mobility patterns. Experimental evaluations demonstrate substantial improvements in both prediction accuracy and communication reliability compared to traditional approaches. Designed for edge deployment, the system supports real-time decision-making, making it highly suitable for adaptive traffic management in urban smart city environments.

The rise of vehicular networks has ushered in the era of vehicle-to-everything (V2X) communication aimed at bolstering driving safety. A pivotal aspect of V2X communication is its role in facilitating emergency warning systems. This study focuses on the propagation of emergency messages and the provisioning of emergency services through V2X communication while considering the efficient allocation of resources necessary for effective vehicle communication. The primary objective of resource allocation within the context of Cellular-Vehicle-to-Everything (C-V2X) is to optimize the utilization of available resources, amplify system capacity, and address the diverse communication requisites within the confines of system constraints. A notable challenge in C-V2X resource allocation resides in the judicious allocation of spectrum resources and broadcast opportunities to V2X users. Therefore, we present NeuroEvolution of Augmenting Topologies (NEAT)-based efficient channel allocation within the C-V2X framework. Our approach aims to maximize the sum rate and throughput of emergency service vehicles (ESV) while ensuring the attainment of a minimum threshold throughput for other vehicles. Subsequently, we conduct a comparative analysis between the average sum rate achieved by the NEAT algorithm and a random resource allocation scheme. Furthermore, we undertake a comparative assessment of the time complexity of NEAT in contrast to other state-of-the-art techniques employed for channel allocation. These techniques encompass the graph matching algorithm, the Hungarian, and the brute force method. Our proposed C-V2X model demonstrates superior performance across various evaluation metrics compared to various alternative algorithms.

Abstract From the past few years, Unmanned Aerial Vehicles (UAVs) has proved an immense potential in providing the cost and time‐efficient solutions to the various societal applications such as healthcare, supply chain, and video & surveillance. It has many data security and privacy issues, and researchers across the globe have given many solutions to protect data from cyber‐attacks. Many of them have suggested cryptographic‐based solutions, which is very compute extensive. Very few researchers have suggested Blockchain (BC)‐based solutions, but their solutions may suffer from high data storage cost as well as network latency, reliability, and bandwidth issues. To overcome the above‐mentioned issues, this paper proposed an InterPlanetary File System and BC‐based secure UAV communication scheme over the 6G network. This proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Then, the research challenges and future directions for further improvement of the proposed system have been presented.