Unmanned Aerial Vehicle-Assisted Cooperative Air and Ground Communications

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 37118

Special Issue Editors


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Guest Editor
College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates
Interests: computer networks; quality of service; vehicular ad hoc networks; wireless networks
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, The University of Oklahoma, Norman, OK 73019, USA
Interests: computer networking, wireless networks, mobile networks, space communications, vehicular communications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Laboratoire d'Informatique Gaspard Monge, Université Paris-Est Marne-la-Vallée, 77454 Marne-la-Vallée, France
Interests: UAV; mobile edge computing; reinforcement learning; IoT; computer network
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5BN, UK
Interests: UAV communications; 5G and beyond; blockchain; mobile internet systems and CPS-IoT
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
L2S, CentraleSupélec, Paris Saclay University, 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France
Interests: cloud-RAN architecture; resource allocation; 5G networks

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Guest Editor
College of Information Technology, United Arab Emirates University, PO Box 17551, Al Ain, United Arab Emirates
Interests: networking; security; distributed systems; simulation

Special Issue Information

Dear Colleagues,

Drones, or what is commonly known as Unmanned Aerial Vehicles (UAVs), could be involved in a wide range of applications, spanning from the agricultural domain, disaster management, and on-demand emergency communications, to environmental monitoring, 3D mapping, and remote sensing. With the recent advent of beyond 5G (B5G) and upcoming 6G networks, UAVs have seen impressive growth of demand from researchers, practitioners, and investors in multidisciplinary fields, particularly in relation to the design of alternative UAV-assisted solutions to handle the different constraints encountered in traditional protocols, applications, and systems. For instance, UAVs could be deployed as relays to bridge communication gaps of terrestrial networks witnessing instability on data transmission, such as basestations, to extend the coverage and performance of 5G/B5G terrestrial cellular networks, and as data collectors to gather information from Wireless Sensor Networks (WSNs). However, the assistance of UAVs to such systems and applications always raises a lot of exciting new challenges in terms of energy consumption, positioning and trajectory optimization, communications, and security, just to name a few. From the B5G point of view, the current UAV assistance paradigms should also be revised to explicitly consider different features, such as computing, network management, intelligent decisions, and energy efficiency. The potential of the UAV assistance paradigm paves a path towards many advantages, including flexibility of deployment, reduced risk of dangerous environments, agility in missions, and efficiency in communications. However, various open challenges are still to be tackled to ensure its correct deployment. This Special Issue (SI) targets novel scientific ideas, schemes, results, possible applications, and new challenges and perspectives devoted to the UAV assistance paradigm and everything around UAV networks. The particular topics of interest for this SI include but are not limited to:

  1. Networking solutions for UAV-assisted communications.
  2. Cloud, fog, and edge computing architecture for UAV-assisted systems.
  3. SDN and NFV for UAV communications.
  4. Positioning and trajectory optimization for UAV-assisted systems.
  5. Machine learning algorithms for UAV-assisted systems.
  6. UAV-to-everything (U2X).
  7. Test bed and validation for UAV-assisted systems.
  8. Energy efficiency in UAV-assisted applications.
  9. Energy harvesting in UAV-assisted systems.
  10. Mobile edge computing in UAV-assisted systems.
  11. NDN in UAV-assisted systems.
  12. Data gathering in UAV-assisted networks.
  13. Security, trust, and privacy in UAV-assisted networks.
  14. UAV–UGV coordination.
  15. UAV–IoT networks.
  16. UAV-enabled smart city.
  17. UAV-assisted VANETs, MANETs, WSNs, and cellular networks.

Prof. Abderrahmane Lakas
Prof. Mohammed Atiquzzaman
Prof. Omar Sami Oubbati
Prof. Vishal Sharma
Prof. Sahar Hoteit
Prof. Taieb Znati
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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15 pages, 1725 KiB  
Article
Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET
by Esmot Ara Tuli, Mohtasin Golam, Dong-Seong Kim and Jae-Min Lee
Drones 2022, 6(1), 22; https://doi.org/10.3390/drones6010022 - 13 Jan 2022
Cited by 16 | Viewed by 3521
Abstract
The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from [...] Read more.
The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyzes the performance of four topology-based routing protocols, dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), geographic routing protocol (GRP), and optimized link state routing (OLSR), by using practical simulation software OPNET 14.5. Performance evaluation carries out various metrics such as throughput, delay, and data drop rate. Moreover, the performance of the OLSR routing protocol is enhanced and named “E-OLSR” by tuning parameters and reducing holding time. The optimized E-OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper. Full article
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13 pages, 410 KiB  
Article
Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
by Sudheesh Puthenveettil Gopi, Maurizio Magarini, Saeed Hamood Alsamhi and Alexey V. Shvetsov
Drones 2021, 5(4), 128; https://doi.org/10.3390/drones5040128 - 29 Oct 2021
Cited by 15 | Viewed by 3249
Abstract
The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several [...] Read more.
The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance. Full article
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16 pages, 1730 KiB  
Article
Background Invariant Faster Motion Modeling for Drone Action Recognition
by Ketan Kotecha, Deepak Garg, Balmukund Mishra, Pratik Narang and Vipul Kumar Mishra
Drones 2021, 5(3), 87; https://doi.org/10.3390/drones5030087 - 31 Aug 2021
Cited by 10 | Viewed by 3000
Abstract
Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical [...] Read more.
Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition. Full article
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29 pages, 1742 KiB  
Article
A Network Slicing Framework for UAV-Aided Vehicular Networks
by Emmanouil Skondras, Emmanouel T. Michailidis, Angelos Michalas, Dimitrios J. Vergados, Nikolaos I. Miridakis and Dimitrios D. Vergados
Drones 2021, 5(3), 70; https://doi.org/10.3390/drones5030070 - 30 Jul 2021
Cited by 16 | Viewed by 3545
Abstract
In a fifth generation (5G) vehicular network architecture, several point of access (PoA) types, including both road side units (RSUs) and aerial relay nodes (ARNs), can be leveraged to undertake the service of an increasing number of vehicular users. In such an architecture, [...] Read more.
In a fifth generation (5G) vehicular network architecture, several point of access (PoA) types, including both road side units (RSUs) and aerial relay nodes (ARNs), can be leveraged to undertake the service of an increasing number of vehicular users. In such an architecture, the application of efficient resource allocation schemes is indispensable. In this direction, this paper describes a network slicing scheme for 5G vehicular networks that aims to optimize the performance of modern network services. The proposed architecture consists of ground RSUs and unmanned aerial vehicles (UAVs) acting as ARNs enabling the communication between ground vehicular nodes and providing additional communication resources. Both RSUs and ARNs implement the LTE vehicle-to-everything (LTE-V2X) technology, while the position of each ARN is optimized by applying a fuzzy multi-attribute decision-making (fuzzy MADM) technique. With regard to the proposed network architecture, each RSU maintains a local virtual resource pool (LVRP) which contains local RBs (LRBs) and shared RBs (SRBs), while an SDN controller maintains a virtual resource pool (VRP), where the SRBs of the RSUs are stored. In addition, each ARN maintains its own resource blocks (RBs). For users connected to the RSUs, if the remaining RBs of the current RSU can satisfy the predefined threshold value, the LRBs of the RSU are allocated to user services. On the contrary, if the remaining RBs of the current RSU cannot satisfy the threshold, extra RBs from the VRP are allocated to user services. Similarly, for users connected to ARNs, the satisfaction grade of each user service is monitored considering both the QoS and the signal-to-noise plus interference (SINR) factors. If the satisfaction grade is higher than the predefined threshold value, the service requirements can be satisfied by the remaining RBs of the ARN. On the contrary, if the estimated satisfaction grade is lower than the predefined threshold value, the ARN borrows extra RBs from the LVRP of the corresponding RSU to achieve the required satisfaction grade. Performance evaluation shows that the suggested method optimizes the resource allocation and improves the performance of the offered services in terms of throughput, packet transfer delay, jitter and packet loss ratio, since the use of ARNs that obtain optimal positions improves the channel conditions observed from each vehicular user. Full article
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18 pages, 4881 KiB  
Article
A Novel Link Failure Detection and Switching Algorithm for Dissimilar Redundant UAV Communication
by Yan Han Lau and Marcelo H. Ang, Jr.
Drones 2021, 5(2), 48; https://doi.org/10.3390/drones5020048 - 01 Jun 2021
Cited by 4 | Viewed by 3894
Abstract
Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. [...] Read more.
Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes. Full article
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12 pages, 2718 KiB  
Article
MIMO Relaying UAVs Operating in Public Safety Scenarios
by Joseanne Viana, Francisco Cercas, Américo Correia, Rui Dinis and Pedro Sebastião
Drones 2021, 5(2), 32; https://doi.org/10.3390/drones5020032 - 26 Apr 2021
Cited by 3 | Viewed by 4320
Abstract
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have [...] Read more.
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone. Full article
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Review

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30 pages, 1632 KiB  
Review
UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
by Yassine Yazid, Imad Ez-Zazi, Antonio Guerrero-González, Ahmed El Oualkadi and Mounir Arioua
Drones 2021, 5(4), 148; https://doi.org/10.3390/drones5040148 - 13 Dec 2021
Cited by 59 | Viewed by 13155
Abstract
Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI. Full article
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