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Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
2022
Drones
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This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks. ...
Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. ...
Most notably, Q-learning and deep Q-learning (DQL) are currently the most explored in the literature for FANETs to address their challenges. ...
doi:10.3390/drones6110334
fatcat:l4lmxn4bqrexzcbcb46a2uou3u
AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET
2020
Journal of Communications and Networks
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The simulation results revealed that the proposed adaptive federated reinforcement learning-based defense strategy outperformed the baseline methods by significantly reducing the number of en route jammer ...
First, a decision from a centralized knowledge base is unsuitable because of the communication and power constraints in FANET. ...
In [7] , a hot-booting deep Q-network based 2-D mobile communication scheme is proposed by applying deep convolutional neural network and macro-action techniques to accelerate learning in dynamic situations ...
doi:10.1109/jcn.2020.000015
fatcat:isdi6bqkcnht5nk3hgm2apqtgq
A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions
[article]
2023
arXiv
pre-print
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Consequently, our study involves the simulation and analysis of four distinct routing attacks on FANETs. ...
In recent years, UAVs, which are widely utilized in military missions, have begun to be deployed in civilian applications and mostly for commercial purposes. ...
The proposed IDS deployed both in each UAV and within GBS. In [182] , a Deep Reinforcement Learning (DRL) approach was proposed for training IDS models on the central system. ...
arXiv:2306.14281v3
fatcat:7md47g72hna7rkkguvs4z6jmgu
An Extensive Survey on the Internet of Drones
[article]
2021
arXiv
pre-print
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in the IoD field. ...
with the other surveys present in literature. ...
In [146] , the problems related to temporary overloading of the network are solved relying on Deep Reinforcement Learning (DRL) techniques. ...
arXiv:2007.12611v3
fatcat:qje6brwqcza5xo43qh7qpj4csa
Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing
2022
Electronics
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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia ...
The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing. ...
This sub-section provides descriptions of several learning models which have been proposed such as deep learning, reinforcement learning, deep reinforcement learning, federated learning and transfer learning ...
doi:10.3390/electronics11142239
fatcat:wf5wt7whbrbkdggygptum56244
Machine Learning Methods for Management UAV Flocks - a Survey
2021
IEEE Access
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Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. ...
For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. ...
Deep learning methods, or deep reinforcement methods, will not be considered in such cases. ...
doi:10.1109/access.2021.3117451
fatcat:f6xli6srencw3ezqg5fyzwmuie
A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook
[article]
2021
arXiv
pre-print
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We conclude by presenting future trends, and the remaining challenges in AI-based UAV networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency ...
Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions by integrating ...
AirSim is empowered with deep learning, computer vision, and reinforcement learning features to generate and utilize training datasets [253] . ...
arXiv:2104.01283v2
fatcat:p2vgtckponfm5flwz4dscf7dju
A Survey of VANET/V2X Routing from the Perspective of Non-Learning- and Learning-Based Approaches
2022
IEEE Access
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In this survey, we categorize the routing mechanisms as non-learning-and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance ...
Various routing protocols for V2X communication exist in the open technical literature. ...
In [35] , the authors propose a multilevel plot directed greedy opportunity routing protocol and investigate geographic routing protocols for multilevel VANET scenarios. ...
doi:10.1109/access.2022.3152767
fatcat:z5lfokp4ynarjhpaczjf2btrme
2021 Index IEEE Internet of Things Journal Vol. 8
2021
IEEE Internet of Things Journal
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
Context-Aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach. ...
doi:10.1109/jiot.2022.3141840
fatcat:42a2qzt4jnbwxihxp6rzosha3y
Self-Evolving Integrated Vertical Heterogeneous Networks
[article]
2022
arXiv
pre-print
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6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services ...
Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. ...
current learning methods, such as deep reinforcement learning, is the demand for massive amounts of sampled data. ...
arXiv:2106.13950v4
fatcat:oodlm4gdengnfcecuotvkahlui
Handover Management for Drones in Future Mobile Networks—A Survey
2022
Sensors
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The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems ...
Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. ...
Moreover, the authors wish to express their gratitude to Ministry of Higher Education (MOHE) Malaysia and Universiti Teknologi Malaysia for the financial support of this project under UTM RA ICONIC grant ...
doi:10.3390/s22176424
pmid:36080883
pmcid:PMC9460841
fatcat:ezwlzvueijh4tgfc5swu3vuizq
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
2019
IEEE Access
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Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. ...
In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. ...
Zhang et al. in [305] use deep reinforcement learning to determine the fastest path to a charging station. ...
doi:10.1109/access.2019.2909530
fatcat:xgknpyuqazhpvferjkkdohxmtu
URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence
2021
IEEE Access
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Non-QoS-aware scheduling models, however, implement the scheduling of blind equivalent throughput (BET), maximum throughput (MT), and proportional fair (PF) scheduling. ...
NEED FOR NEW MAC PROTOCOL FOR 5G NR Both the time over the air and the transmit opportunity (TXOP) waiting delay are reduced by decreasing the transmission slot and interval. ...
He taught numerous courses in the field of computer science and engineering, from reinforcement learning to computer networks. ...
doi:10.1109/access.2021.3073806
fatcat:7ngx3ah5vzdyvcz4nkd2vvdyjy
Surveying pervasive public safety communication technologies in the context of terrorist attacks
2020
Physical Communication
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base-station) architectures, multi-hop D2D routing for PSN, and jamming and anti-jamming in mobile networks. ...
Important aspects such as 70 localization, beamforming, suitable channel models, jamming and routing approaches are described in detail. • Open challenges, which highlight the limitations and way-forward ...
Similarly, the broad categories for D2D ad hoc routing are incentive-based, topology-based, QoS-based, security-based, device-aware, and 1075 multipath coding based. ...
doi:10.1016/j.phycom.2020.101109
fatcat:s74tzgwo6rfmbjyfqnytrdxece
A Survey on Machine-Learning Techniques for UAV-Based Communications
2019
Sensors
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In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. ...
In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such ...
In this type of learning, both labeled and unlabeled data are exploited for the training. • Reinforcement learning: In RL, the problems are solved by employing a sequence of actions that use the trial ...
doi:10.3390/s19235170
pmid:31779133
pmcid:PMC6929112
fatcat:pnur7lmpj5bj7poebmdfpd6bhi
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