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Overview

This study explores the integration of deep neural networks (DNNs) and blockchain technology within Vehicular Ad Hoc Networks (VANETs) to develop an adaptive traffic management system tailored for urban settings. As urban traffic complexity grows, traditional traffic management methods often struggle to accommodate dynamic traffic flows, resulting in congestion and increased pollution. The proposed approach leverages DNNs to predict traffic patterns and optimize vehicle routing, while blockchain technology ensures secure, transparent data sharing among vehicles and infrastructure. The DNN model continuously learns from real-time data, enabling the system to adapt to varying traffic conditions, predict potential congestion points, and suggest alternative routes. Blockchain technology adds a layer of trust, allowing decentralized data sharing without the need for a central authority, which enhances the system's robustness against data tampering and cyber threats. .

02 May 2025·Leveraging VANETs and Blockchain Technology for Urban Mobility

Dynamic Resource Allocation in VANETs for Urban Mobility Applications Using Blockchain and Convolutional Neural Networks (CNNs)

Author: Swaminathan, K. ; Lakshmi, B. ; Karthikeyan, R. ; Prabakar, T. N.

research explores a dynamic resource allocation framework for Vehicular Ad Hoc Networks (VANETs) aimed at optimizing urban mobility applications through the integration of blockchain technology and Convolutional Neural Networks (CNNs). As urban environments face increasing transportation demands, efficient resource allocation in VANETs is essential to support reliable data transmission, reduce congestion, and enhance overall traffic management. The proposed approach leverages blockchain to establish secure, decentralized communication among vehicles, ensuring data integrity and preventing unauthorized access in the highly dynamic network. CNNs are employed to analyze traffic patterns and vehicular movement, extracting valuable insights from vast amounts of real-time data to make informed resource allocation decisions.

02 May 2025·Advances in Computational Intelligence and Robotics

Metaverse Security Monitoring Based on Virtual Environment Analysis Using Machine Learning Techniques

Author: Manikandan, N. ; Magesh, S. ; Manikandan, R. ; Chinnasamy, A. ; Varun, P. ; Oliver, Sheryl

A NextG Internet platform allows users to participate in many kinds of virtual events and communicate with avatars in a 3D virtual environment to perform many activities in this environment. Building an intrusion detection system is computationally challenging in the Metaverse because of its interactive nature as well as large number of user interactions that take place within virtual settings. This research proposes novel techniques in a Metaverse-based virtual environment in security monitoring using machine learning techniques. Here, security monitoring was carried out using reinforcement-federated regressive Gaussian neural networks. The metaverse virtual environment has been deployed, and its analysis is carried out using a cloud edge network with virtual software-defined infrastructure. Experimental analysis is carried out in terms of scalability, quality of service, latency, accuracy, and network integrity. The proposed model attained a scalability of 94%, a quality of service of 95%, an accuracy of 97%, a latency of 96%, and a network integrity of 93%.

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