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Review

A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles

Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
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Authors to whom correspondence should be addressed.
Drones 2024, 8(8), 353; https://doi.org/10.3390/drones8080353
Submission received: 14 June 2024 / Revised: 17 July 2024 / Accepted: 23 July 2024 / Published: 29 July 2024
(This article belongs to the Special Issue Wireless Networks and UAV)

Abstract

As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV.
Keywords: Internet of Vehicles; unmanned aerial vehicles; Machine Learning; Artificial Intelligence; resource management; routing; task offloading; trajectory; survey Internet of Vehicles; unmanned aerial vehicles; Machine Learning; Artificial Intelligence; resource management; routing; task offloading; trajectory; survey

Share and Cite

MDPI and ACS Style

Ali Shah, S.A.; Fernando, X.; Kashef, R. A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles. Drones 2024, 8, 353. https://doi.org/10.3390/drones8080353

AMA Style

Ali Shah SA, Fernando X, Kashef R. A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles. Drones. 2024; 8(8):353. https://doi.org/10.3390/drones8080353

Chicago/Turabian Style

Ali Shah, Syed Ammad, Xavier Fernando, and Rasha Kashef. 2024. "A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles" Drones 8, no. 8: 353. https://doi.org/10.3390/drones8080353

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