UAV‐enabled computation migration for complex missions: A reinforcement learning approach

Zhu, Shichao; Gui, Lin; Cheng, Nan; Zhang, Qi; Sun, Fei; Lang, Xiupu

doi:10.1049/iet-com.2019.1188

Cited by 30 publications

(5 citation statements)

References 36 publications

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“…In [85], the authors proposed the MDP and then an Actor-Critic based RL technique (A2C) solution in UAVs to offload the computational tasks of the ground users and achieve the minimum mission time. This scheme achieves the terrestrial users offloading through UAVs and in turn the offloading of UAVs through MEC server.…”

Section: Ai For Resource Allocation In Uav 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 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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“…Under the uncertain and stochastic environments of UAVenabled aerial MEC systems, the decision-making problems can be typically modeled as a Markov Decision Process (MDP), where the next state of the system is only related to the current state, regardless of the historical states [142]. As an important branch of ML, RL is an effective tool to address the MDPs [143]. RL is a control-theoretic trial-and error learning method with rewards and punishments of a sequence of actions [141].…”

Section: Reinforcement Learningmentioning

confidence: 99%

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Song

Qin

Hao

et al. 2022

Computer Communications

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“…In [157], UAVs are considered as remote edge computing systems that could offload ground users when they are excessively overwhelmed with computation. Hence, the authors studied the best way to assign a UAV to execute a given task in order to minimize the overall mission time.…”

Section: ) Update Rulementioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

110

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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

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UAV‐enabled computation migration for complex missions: A reinforcement learning approach

Cited by 30 publications

References 36 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

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

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