AI for UAV-Assisted IoT Applications: A Comprehensive Review

Cheng, Nan; Wu, Shen; Wang, Xiucheng; Yin, Zhisheng; Li, Changle; Chen, Fangjiong

doi:10.1109/jiot.2023.3268316

Cited by 36 publications

(6 citation statements)

References 167 publications

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“…Wilson et al [10] presented the latest advancements in embedded sensors, communication technologies, computing platforms, and machine learning methods which have been employed in UAVs for smart sensing tasks while simultaneously achieving high power efficiency. Cheng et al [11] presented a review of the integration of AI in various UAV-focused IoT(Internet of Things) functions.…”

Section: A Related Surveysmentioning

confidence: 99%

“…Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies like path planning [9], smart city surveillance [12], flood management [13], and Internet of Things(IoT) [11], swarm intelligence [14]. There is no comprehensive survey covering various applications, AI algorithms, AI paradigms, tools, and libraries used in AI-integrated UAV frameworks, etc.…”

Section: A Related Surveysmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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Section: A Related Surveysmentioning

confidence: 99%

Section: A Related Surveysmentioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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“…Especially in some extreme situations, such as imperfect infrastructure and communication interruption caused by severe natural disasters, the direct or indirect losses are very huge. Under these circumstances, UAVs have naturally become one of the methods of emergency communication due to their flexibility, convenience and efficiency [1].…”

Section: Introductionmentioning

confidence: 99%

QoE‐driven multi‐UAV deployment scheme for emergency communication networks

Dai,

Ju,

Liang

et al. 2024

Electronics Letters

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When ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)‐mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line‐of‐sight and non‐line‐of‐sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs.

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“…These technologies find practical applications in digital twins (DTs), which serve as the conduit between the real and virtual worlds [ 2 ]. On the other hand, in recent years, with the improvement of hovering position accuracy and the update of control functions considering safety [ 3 ], unmanned aerial vehicles (UAVs) are planned to be deployed in various fields such as agriculture, defense, delivery, and surveying, which will facilitate the realization of Industry 5.0 [ 4 , 5 , 6 , 7 ]. Furthermore, current research has shed light on the capabilities of nonterrestrial networks (NTNs) to equip UAVs and low Earth orbit satellites with robust communication systems.…”

Section: Introductionmentioning

confidence: 99%

Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension

Yamamoto,

Nakazato,

Tran

2024

Sensors

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In the era of Industry 5.0, advanced technologies like artificial intelligence (AI), robotics, big data, and the Internet of Things (IoT) offer promising avenues for economic growth and solutions to societal challenges. Digital twin technology is important for real-time three-dimensional space reproduction in this transition, and unmanned aerial vehicles (UAVs) can support it. While recent studies have explored the potential applications of UAVs in nonterrestrial networks (NTNs), bandwidth limitations have restricted their utility. This paper addresses these constraints by integrating millimeter wave (mmWave) technology into UAV networks for high-definition video transmission. Specifically, we focus on coordinating intelligent reflective surfaces (IRSs) and UAV networks to extend coverage while maintaining virtual line-of-sight (LoS) conditions essential for mmWave communication. We present a novel approach for integrating IRS into Beyond 5G/6G networks to enhance high-speed communication coverage. Our proposed IRS selection method ensures optimal communication paths between UAVs and user equipment (UE). We perform numerical analysis in a realistically modeled 3D urban environment to validate our approach. Our results demonstrate significant improvements in the received SNR for multiple UEs upon the introduction of IRSs, and they confirm the feasibility of coverage extension in mmWave UAV networks.

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AI for UAV-Assisted IoT Applications: A Comprehensive Review

Cited by 36 publications

References 167 publications

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

QoE‐driven multi‐UAV deployment scheme for emergency communication networks

Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension

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