SDDV: scalable data dissemination in vehicular ad hoc networks

An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations

Smart Roads, Smarter Cities: Machine Learning Integration for Dynamic Traffic Management

As the world's cities become more urbanised, traffic congestion becomes a major problem. Conventional approaches are unable to deliver timely insights, which impedes the application of efficient congestion control strategies. This study presents a novel machine learning-based traffic congestion control system that combines a Euclidean distance tracker with the YOLO (You Only Look Once) object recognition framework. As cities struggle with the intricacies of increasing traffic, the need for intelligent technologies capable of real-time vehicle surveillance and congestion analytics is highlighted. To address this, the suggested solution goes beyond traditional constraints by using machine learning to accurately detect and track automobiles in urban environments. Utilizing the YOLO object detection framework, which is renowned for its speed and accuracy, the study builds on prior research in computer vision and transportation engineering. By connecting object detections between frames, the Euclidean Distance Tracker improves performance and allows a continuous comprehension of vehicle motions. The system's effectiveness in real-world circumstances is demonstrated by the results, which offer high accuracy across a range of vehicle classes. A major advancement in the development of urban mobility has been made with the integration of YOLO and the Euclidean Distance Tracker, which offers a viable solution for intelligent traffic management