Deep Reinforcement Learning Based Latency Minimization for Mobile Edge Computing With Virtualization in Maritime UAV Communication Network

Liu, Ying; Yan, Jeff; Zhao, Xiaohui

doi:10.1109/tvt.2022.3141799

Cited by 92 publications

(19 citation statements)

References 40 publications

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“…In [105], a two-layer UAV-enabled MEC maritime communication network has been investigated, in which parallel computation of tasks offloading with different amount of data in different virtual machines has been studied. In this framework, a latency minimization problem has been formulated, which takes into account both communication and computation aspects.…”

Section: A Mobile Edge Computingmentioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

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Section: A Mobile Edge Computingmentioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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“…Recently, [14] proposed an energy-efficient fair communication through trajectory design and band allocation (EEFC-TDBA) which allows an unmanned aerial vehicle (UAV) to adjust the flight speed and direction to enhance energy efficiency and allocate a frequency band to achieve fair communication service. The DRL algorithm for solving the latency minimization problem for both communication and computation in a maritime UAV swarm mobile edge computing network was suggested and analyzed in [15].…”

Section: A Related Workmentioning

confidence: 99%

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

Chung

Lim

2023

IEEE Access

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In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NPhard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

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“…The DNNs have one input layer, one output layer, and several hidden layers, which are fully connected. Accordingly, the computation complexity analysis is mainly based on the DNN model [39].…”

Section: Complexity Analysismentioning

confidence: 99%

“…Let N a L and N c L denote the numbers of hidden layers of the actor and critic networks, respectively, and the numbers of neurons in the mth hidden layer of the actor and critic networks are denoted by n a m and n c m , respectively. In addition, we denote the dimensions of the state and action spaces by |S 1 | and |A 1 |, respectively, which can be obtained based on (38) and (39), and assume that the parameters of the DNNs will converge after F conv 1 episodes and N conv 1 time slots. Accordingly, the complexity of the DDPG-based algorithm is expressed as…”

Section: Complexity Analysismentioning

confidence: 99%

Deep Reinforcement Learning-Based Optimization for End-to-End Network Slicing With Control- and User-Plane Separation

Wang

Zhao

Chu

et al. 2022

IEEE Trans. Veh. Technol.

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Deep Reinforcement Learning Based Latency Minimization for Mobile Edge Computing With Virtualization in Maritime UAV Communication Network

Cited by 92 publications

References 40 publications

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

Deep Reinforcement Learning-Based Optimization for End-to-End Network Slicing With Control- and User-Plane Separation

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