Energy Harvesting-Based UAV-Assisted Vehicular Edge Computing: A Deep Reinforcement Learning Approach

Zhang, Zhanpeng; Xie, Xinghuan; Xu, Chen; Wu, Runze

doi:10.1109/icccworkshops55477.2022.9896720

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In [93], the authors proposed a mechanism for energy harvesting by UAV from BS and vehicles using wireless power transfer (WPT) and simultaneous wireless information and power transfer (SWIPT) techniques, respectively. Maximum data offloading to the UAV is the main goal of this research.…”

Section: Ai For Resource Allocation In Uav-iov Networkmentioning

confidence: 99%

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Ali Shah,

Fernando,

Kashef

2023

Preprint

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The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

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Section: Ai For Resource Allocation In Uav-iov Networkmentioning

confidence: 99%

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Ali Shah,

Fernando,

Kashef

2023

Preprint

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“…Addressing the issue of base station (BS) edge servers in power infrastructure networks occasionally failing to satisfy the users' computational needs, Hu et al (2019) and Peng et al (2020) advocated for deploying UAVs equipped with edge computing servers to overwhelmed areas; the proposed strategy can mitigate resource scarcity in roadside units during peak periods effectively. Zhang et al (2022) established a UAV-assisted edge vehicular network computing structure with energy harvesting. UAVs aid vehicles in executing onboard task computations.…”

Section: Introductionmentioning

confidence: 99%

Efficient multiple unmanned aerial vehicle-assisted data collection strategy in power infrastructure construction

Lai,

Xie,

Yang

et al. 2024

Front. Comms. Net.

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Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.

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Joint optimization of trajectory, beamforming, and power allocation in UAV-enabled WPT networks using DRL combined with water-filling algorithm

Muy

Lee

2023

Vehicular Communications

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Energy Harvesting-Based UAV-Assisted Vehicular Edge Computing: A Deep Reinforcement Learning Approach

Cited by 6 publications

References 11 publications

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

A Survey on Artificial Intelligence based Internet of Vehicles utilizing Unmanned Aerial Vehicles

Efficient multiple unmanned aerial vehicle-assisted data collection strategy in power infrastructure construction

Joint optimization of trajectory, beamforming, and power allocation in UAV-enabled WPT networks using DRL combined with water-filling algorithm

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