Wireless Networks and UAV

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

Deadline for manuscript submissions: 14 May 2024 | Viewed by 12160

Special Issue Editor

Institut National Polytechnique de Toulouse, Toulouse, France
Interests: performance evaluation of mo-bile and vehicular networks; satellite networks; wireless sensor networks; cross-layer systems

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Drones on the subject of “Wireless Networks and UAV”.

The use of aerial drones, also known as unmanned aerial vehicles (UAVs), and unmanned marine vehicles (UMVs), has proven to be very useful in rescue operations or in environmental monitoring; more particularly, they have been used for population surveillance during the management of the COVID-19 pandemic. Drones can be used for communication, monitoring, and delivery. Autonomous drone swarms help to monitor and connect mobile unconnected objects, as well as aid in Earth or extra-Earth exploration. The integration of unmanned vehicles with terrestrial and space networks has been standardized and is the focus of ongoing experiments.

Research in this domain has revealed several critical issues, such as autonomous deployment, navigation, and control, energy management, and seamless services. Advanced methods should be designed to ensure the reliable and efficient functioning of the unmanned vehicles network.

This Special Issue focuses on new developments in the field of placement, smart network control, and the navigation of unmanned vehicles from theoretical to experimental studies.

Potential topics include, but are not limited to, the following:

  • Efficient and resilient deployment of aerial drones;
  • Control of unmanned vehicles-aided networks;
  • UAV-aided mobile sensor networks;
  • Autonomous vehicles networks monitoring;
  • Energy-efficient UAV tracking of mobile targets;
  • Energy harvesting for networks composed of unmanned vehicles;
  • Traffic monitoring aided by unmanned vehicles;
  • Beyond 5G space–air–ground network control;
  • Coverage optimization and control in UAV surveillance;
  • Smart autonomous networks of vehicles;
  • Collision avoidance for unmanned vehicles;
  • Autonomous networks for deep space exploration;
  • Experimental platforms for UAV-aided mobile networks.

Dr. Riadh Dhaou
Guest Editor

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 (9 papers)

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Research

30 pages, 17407 KiB  
Article
Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
by Atsushi Suetsugu, Hirokazu Madokoro, Takeshi Nagayoshi, Takero Kikuchi, Shunsuke Watanabe, Makoto Inoue, Makoto Yoshida, Hitoshi Osawa, Nobumitsu Kurisawa and Osamu Kiguchi
Drones 2024, 8(2), 38; https://doi.org/10.3390/drones8020038 - 25 Jan 2024
Viewed by 1272
Abstract
Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the [...] Read more.
Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the water surface (e.g., 45 m for vertical half-wave dipole antennas with the XBee S2CTM, estimated using the two-ray ground reflection model) persists as a formidable challenge for amphibious systems. To overcome this difficulty, we developed a wireless data relay system for amphibious drones using the mesh-type networking functions of the XBeeTM. We then conducted field tests of the developed system in a large marsh pond to provide experimental evidence of the efficiency of the multiple-drone network in amphibious settings. In these tests, hovering relaying over water was attempted for extension and bypassing obstacles using the XBee S2CTM (6.3 mW, 2.4 GHz). During testing, the hovering drone (<10 m height from the drone controller) successfully relayed water quality data from the transmitter to the receiver located approximately 757 m away, but shoreline vegetation decreased the reachable distance. A bypassing relay test for vegetation indicated the need to confirm a connected path formed by pair(s) of mutually observable drones. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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27 pages, 1199 KiB  
Article
Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies
by Javier Garau Guzman and Victor Monzon Baeza
Drones 2024, 8(1), 7; https://doi.org/10.3390/drones8010007 - 29 Dec 2023
Cited by 1 | Viewed by 1543
Abstract
This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities [...] Read more.
This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOLO algorithm. Through image capture and processing, UAVs can rapidly identify congested areas and transmit this information to ground-based traffic lights, facilitating dynamic traffic control adjustments. Moreover, VLC establishes a communication link between UAVs and traffic lights that complements existing RF-based solutions, underscoring visible light’s potential as a reliable and energy-efficient communication medium. In addition to integrating UAVs and VLC, we propose a new communication protocol and messaging system for this framework, enhancing its adaptability to varying traffic flows. This research represents a significant stride toward developing more efficient, sustainable, and resilient urban transportation systems. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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25 pages, 7811 KiB  
Article
A Multichannel MAC Protocol without Coordination or Prior Information for Directional Flying Ad hoc Networks
by Shijie Liang, Haitao Zhao, Jiao Zhang, Haijun Wang, Jibo Wei and Junfang Wang
Drones 2023, 7(12), 691; https://doi.org/10.3390/drones7120691 - 29 Nov 2023
Viewed by 1182
Abstract
Achieving neighbor discovery for a directional flying ad hoc network (FANET) with multiple channels poses challenges for media access control (MAC) protocol design, as it requires simultaneous main lobe and channel rendezvous while dealing with the high UAV mobility. In order to achieve [...] Read more.
Achieving neighbor discovery for a directional flying ad hoc network (FANET) with multiple channels poses challenges for media access control (MAC) protocol design, as it requires simultaneous main lobe and channel rendezvous while dealing with the high UAV mobility. In order to achieve fast neighbor discovery for initial access without coordination or prior information, we first establish the theoretical supremum for the directional main lobe. Then, to achieve the supremum, we introduce the BR-DA and BR-DA-FANET algorithms to respectively establish the supremum on rendezvous between a pair of UAVs’ main lobes and rendezvous of main lobes for all UAVs in the FANET. To further accelerate the neighbor discovery process, we propose the neighbor discovery with location prediction protocol (ND-LP) and the avoiding communication interruption with location prediction (ACI-LP) protocol. ND-LP enables quick main lobe rendezvous and channel rendezvous, while ACI-LP enables beam tracking and channel rendezvous together with the avoidance of communication interruptions. The simulation results demonstrate that the proposed protocols outperform the state-of-the-art works in terms of neighbor discovery delay. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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17 pages, 1265 KiB  
Article
A Decision for Throughput Optimization in UAV-Enabled Emergency Outdoor–Indoor Fairness Communication
by Zinan Guo, Bo Hu and Shanzhi Chen
Drones 2023, 7(7), 460; https://doi.org/10.3390/drones7070460 - 11 Jul 2023
Viewed by 672
Abstract
This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal [...] Read more.
This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal fading caused by outdoor transmission loss through wall loss as well as indoor transmission loss when the UAV forwards the information to the indoor users reduces the channel gain and degrades the system downlink throughput. To improve the downlink throughput of the system and ensure communication fairness for indoor users, we designed a joint UAV location deployment and resource allocation (JLRO) algorithm that optimized UAV three-dimensional (3D) deployment location, power and bandwidth resource allocation. The simulation results demonstrate the convergence and validity of the proposed JLRO algorithm, as well as its superiority compared to benchmark algorithms. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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17 pages, 2984 KiB  
Article
Deployment Method with Connectivity for Drone Communication Networks
by Hirofumi Osumi, Tomotaka Kimura, Kouji Hirata, Chinthaka Premachandra and Jun Cheng
Drones 2023, 7(6), 384; https://doi.org/10.3390/drones7060384 - 07 Jun 2023
Viewed by 1341
Abstract
In this paper, we consider a drone deployment problem in situations where the number of drones to be deployed is small compared to the number of users on the ground. In this problem, drones are deployed in the air to collect information, but [...] Read more.
In this paper, we consider a drone deployment problem in situations where the number of drones to be deployed is small compared to the number of users on the ground. In this problem, drones are deployed in the air to collect information, but they cannot collect information from all ground users at once due to the limitations of their communication range. Therefore, the drones need to continue to move until they collect the information for the all ground users. To efficiently realize such drone deployment, we propose two deployment methods. One is an integer linear programming (ILP)-based deployment method and the other is an adjacent deployment method. In the ILP-based deployment method, the positions of the drones at each point in time are determined by solving an ILP problem in which the objective function is the total number of users from whom data can be collected. In contrast, in the adjacent deployment method, drones are sequentially deployed in areas with probabilities determined according to the number of user nodes in adjacent areas at which other drones are already deployed. Through numerical experiments, we show that these deployment methods can be used to efficiently collect data from user nodes on the ground. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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19 pages, 3440 KiB  
Article
A Deep Learning Approach for Wireless Network Performance Classification Based on UAV Mobility Features
by Yijie Bai, Daojie Yu, Xia Zhang, Mengjuan Chai, Guangyi Liu, Jianping Du and Linyu Wang
Drones 2023, 7(6), 377; https://doi.org/10.3390/drones7060377 - 05 Jun 2023
Viewed by 1126
Abstract
The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been [...] Read more.
The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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17 pages, 1001 KiB  
Article
Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP
by Shenghong Qin, Laixian Peng, Renhui Xu, Xianglin Wei, Xingchen Wei and Dan Jiang
Drones 2023, 7(5), 335; https://doi.org/10.3390/drones7050335 - 22 May 2023
Viewed by 1047
Abstract
Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial [...] Read more.
Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the β-Ginibre point process (β-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system’s performance based on the expressions derived in this paper. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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16 pages, 4405 KiB  
Article
The Influence of UAV Altitudes and Flight Techniques in 3D Reconstruction Mapping
by Muhammad Hafizuddin Zulkifli and Khairul Nizam Tahar
Drones 2023, 7(4), 227; https://doi.org/10.3390/drones7040227 - 24 Mar 2023
Cited by 1 | Viewed by 1401
Abstract
Occasionally, investigating an accident is time-consuming, further compounding traffic congestion. This study aims to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). This study tested several flight parameters to check the accuracy and differences compared to site [...] Read more.
Occasionally, investigating an accident is time-consuming, further compounding traffic congestion. This study aims to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). This study tested several flight parameters to check the accuracy and differences compared to site measurement data. The flight parameters selected were POIs and waypoint techniques. These designs can produce a good 3D model to achieve our objectives. This study tested all parameters for accuracy based on the root mean square error (RMSE) value by comparing the UAV data and site measurement data. This study tested this objective using five types of processing and different types of flight parameters (including RMSE) to determine the accuracy of the outcomes. The POI technique achieved an optimal result with centimeter-level accuracy. Furthermore, using UAVs can speed up decision-making, especially in data acquisition, and offer reliable accuracy for specific applications. This study is useful for accident investigation teams to expedite their data collection process. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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15 pages, 3749 KiB  
Article
On Countermeasures against Cooperative Fly of UAV Swarms
by Xia Zhang, Yijie Bai and Kai He
Drones 2023, 7(3), 172; https://doi.org/10.3390/drones7030172 - 02 Mar 2023
Cited by 1 | Viewed by 1607
Abstract
Aiming at anti Unmanned Aerial Vehicle (UAV) swarm, this paper studies the detection and suppression mechanisms of emergence in cooperative flight. Cooperative fly is one of the critical operations for UAV swarm in both military and civilian utilities, which allows individual UAVs to [...] Read more.
Aiming at anti Unmanned Aerial Vehicle (UAV) swarm, this paper studies the detection and suppression mechanisms of emergence in cooperative flight. Cooperative fly is one of the critical operations for UAV swarm in both military and civilian utilities, which allows individual UAVs to distributed adjust their velocity to head for a common destination as well as avoid a collision. This process is viewed as the emergence of complex systems. An emergence detection algorithm based on double thresholds is proposed. It simultaneously monitors the cooperative flight process and system connectivity to accurately identify the occurrence, achievement, or failure of cooperative fly, which provides a solid prerequisite for the suppression mechanism. For suppression, in-band radio interference is designed under the constraint of average power, and the effect is modeled from the perspective of degrading the communication performance of the target system. It is found that low-intensity continuous interference can effectively delay the cooperative fly process and has better concealment, while medium-intensity continuous interference can rapidly stop that process. Based on the above analysis, for the first time, two countermeasures for the UAV swarm’s cooperative fly are designed for the operation intent of delay and disruption of the target UAC swarms, respectively. Simulation results show the effectiveness of the countermeasures. Full article
(This article belongs to the Special Issue Wireless Networks and UAV)
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