Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems

Jung, Soyi; Yun, Won Joon; Shin, MyungJae; Kim, Joongheon; Kim, Jae Hyun

doi:10.1109/tvt.2021.3062418

Cited by 68 publications

(24 citation statements)

References 31 publications

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“…In [87], the authors suggested a cloud-assisted joint charging scheduling and energy management framework for UAVs, and then used multi-agent DRL to design and implement cooperative energy sharing across towers, resulting in intelligent energy sharing. It can be seen that the two approaches are linked and that they should be controlled, coordinated, and harmonized by a centralized orchestration manager, with fairness, energy efficiency, and cost effectiveness in mind.…”

Section: Ml-based Solutions 1) Sl Solutionsmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks' high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.INDEX TERMS Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), and reinforcement learning (RL).

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Section: Ml-based Solutions 1) Sl Solutionsmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

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“…• Unmanned aerial vehicle (UAV)-codesign related characteristics: Nowadays, there are many research results in satellite-air-ground integrated networks (SAGIN). Thus, the handover decision algorithms should consider UAVrelated characteristics [19], [20]. • Millimeter-wave channel characteristics: For establishing fast communications within SAGIN networks, high-speed networking should be realized by utilizing millimeterwave links [21], [22].…”

Section: Potential Future Trendsmentioning

confidence: 99%

Trends in LEO Satellite Handover Algorithms

Park¹,

Kim²

2021

Preprint

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“…With the development of communication technologies in 5G networks and IoT, the automotive industry continues to accelerate towards connected cars [12], [13]. The Internet of Vehicles (IoV) is a distributed network that supports the use of data created by connected cars and vehicular networks.…”

Section: B Internet Of Vehicles (Iov)mentioning

confidence: 99%

Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms

Lee¹,

Kim²

2021

Preprint

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With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns are major obstacles in distributed and wireless networks. In addition, IoT has a limitation on system resources depending on the purpose of services. In addition, a blockchain technology enables secure transactions among participants through consensus algorithms and encryption without a centralized coordinator. In this paper, we first review the federated leaning (FL) and blockchain mechanisms, and then, present a survey on the integration of blockchain and FL for data sharing in industrial, vehicle, and healthcare applications.

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Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems

Cited by 68 publications

References 31 publications

AI-Enabled UAV Communications: Challenges and Future Directions

AI-Enabled UAV Communications: Challenges and Future Directions

Trends in LEO Satellite Handover Algorithms

Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms

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