Narsimhulu P, Chithaluru P, Al-Turjman F, Guda V, Inturi S, Stephan T, Kumar M. An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV. Internet of Things. 2024 Oct 1;27:101240.
The research conducted by Fadi Al-Turjman and colleagues addresses the optimization of vehicle traffic in the context of the Internet of Vehicles (IoV) by utilizing an intelligent Federated Learning (FL)-based protocol. This study presents a novel approach for enhancing traffic management in real-time by predicting vehicle demand, rerouting recommendations, and providing onboard health assistance to drivers, thereby improving travel safety and reducing traffic congestion. The focus is particularly on IoT-connected vehicles, utilizing cluster-based vehicle communication and location estimation models to ensure efficient data transmission and minimize delays.
The importance of this research lies in its ability to offer sustainable solutions for modern transportation systems. By optimizing vehicle routes and increasing the usage of public transportation, the proposed model contributes to the reduction of carbon emissions and fuel consumption. The FL-based scheme further addresses issues related to vehicle count, location tracking, and vacant seat information in real-time, leading to improved decision-making in traffic-heavy areas.
This project benefits from international collaboration, including contributions from researchers in Northern Cyprus, Turkey, India, and the United Arab Emirates, strengthening its global relevance and applicability. Prof. Dr. Fadi Al-Turjman is the head of Artificial Intelligence (AI), Information Systems, and Software Engineering Departments, and the director for the AI and Robotics Institute and the International Research Center for AI and IoT at Near East University. Researchers who are interested in collaborating or exploring further applications of this model are encouraged to contact Fadi Al-Turjman (fadi.alturjman@neu.edu.tr) for more details and potential partnerships.
Abstract
The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.
