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Time-Sensitive Networks for Unmanned Aircraft Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 43859

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Special Issue Editors

Prof. Dr. Hwangnam Kim

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Guest Editor

School of Electrical Engineering, Korea University, Seoul 02841, Korea
Interests: unmanned aircraft system; UTM; Internet of Things; wireless network; cyberphysical system; ubiquitous system
Special Issues, Collections and Topics in MDPI journals

Dr. Yong Wun Jung

E-Mail Website
Guest Editor

Principal Researcher, Unmanned Vehicle Advanced Research Center, Korea Aerospace Research Institute, Daejeon, Korea
Interests: unmanned vehicle; hybrid-electric propulsion; sensing and perception; autonomous system; human-machine interface; UTM; PAV

Dr. Honghai Zhang

E-Mail Website
Guest Editor

Senior Software Engineer, Google Inc., USA
Interests: Realtime communication, Wireless networking, Machine learning, Unmanned aircraft systems

Special Issue Information

Dear Colleagues,

Unmanned aircraft systems (UASs), commonly known as drones, are increasingly investigated in various contexts to support humans. Services employing drones can be extended to a wide range of applications, e.g., surveillance, platooning, traffic control, search-and-rescue missions, wild fire monitoring, product delivery, or video taking. In the course of such missions, UASs can make a flying ad hoc network with their neighbors, send their flight status and sensed information through a variety of sensors and cameras to a controller, such as ground control station, and receive commands from it over the network. Therefore, participating UASs require a highly time-sensitive network that should guarantee deterministic properties in packet delivery, positioning, navigation, and timing.

The focus of this Special Issue will be on dealing with requirements, challenges, constraints, theoretical issues, innovative applications, and experimental results with a time-sensitive network of UASs. Topics of interest include but are not limited to:

Low latency, highly reliable communication systems for unmanned aircraft systems (UASs);
Positioning, navigation and timing for time-sensitive UAS networks;
Time-sensitive guidance and control for UAS;
Time synchronization for time-sensitive UAS networks;
Reliable transmission with bounded delay over time-sensitive UAS networks;
Resource management for time-sensitive UAS networks;
Time-sensitive UAV network in 5G and beyond cellular communication systems;
Theoretical analysis and models on time-sensitive UAS networks;
Security and privacy issues over time-sensitive UAS networks;
Empirical results of time-sensitive UAS network testbeds.

Prof. Hwangnam Kim
Dr. Yong Wun Jung
Dr. Honghai Zhang
Guest Editors

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Keywords

low latency
time-sensitive network
deterministic network
cyberphysical systems

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Published Papers (11 papers)

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Editorial

Jump to: Research

5 pages, 158 KiB

Open AccessEditorial

Guest Editorial Special Issue on Time-Sensitive Networks for Unmanned Aircraft Systems

by Hwangnam Kim, Yong Wun Jung and Honghai Zhang

Sensors 2021, 21(18), 6132; https://doi.org/10.3390/s21186132 - 13 Sep 2021

Cited by 2 | Viewed by 2042

Abstract

In this special issue, we explored swarming, network management, routing for multipath, communications, service applications, detection and identification, computation offloading, and cellular network-based control in time-sensitive networks of unmanned aircraft systems. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

Research

Jump to: Editorial

27 pages, 7240 KiB

Open AccessArticle

Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks

by Laura Michaella Batista Ribeiro, Ivan Müller and Leandro Buss Becker

Sensors 2021, 21(13), 4255; https://doi.org/10.3390/s21134255 - 22 Jun 2021

Cited by 8 | Viewed by 2579

Abstract

Exchanging messages with stable connections in missions composed of multiple unmanned aerial vehicles (UAV) remains a challenge. The variations in UAV distances from each other, considering their individual trajectories, and the medium dynamic factors are important points to be addressed.In this context, to increase the stability of UAV-to-UAV (U2U) communication with link quality, this paper presents an interface manager (IM) that is capable of improving communication in multi-UAV networks.Given a predefined set of available individual wireless interfaces, the proposed IM dynamically defines the best interface for sending messages based on on-flight conditions sensed and calculated dynamically from the wireless medium. Different simulation scenarios are generated using a complex and realistic experimental setup composed of traditional simulators such as NS-3, Gazebo, and GzUAV. IEEE 802.11n 2.4 GHz and 802.11p 5 GHz interfaces are used for the IM selection. The IM performance is evaluated in terms of metrics from the medium-access-control (MAC) and physical layers, which aim to improve and maintain the connectivity between the UAVs during the mission, and from the application layer, which targets the reliability in the delivery of messages. The obtained results show that compared with the cases where a single interface is used, the proposed IM is able to increase the network throughput and presents the best proportion of transmitted and received packets, reception power (−60 dBm to −75 dBm), and loss (−80 dB to −85 dB), resulting in a more efficient and stable network connections. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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19 pages, 34400 KiB

Open AccessArticle

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

by Zain Anwar Ali, Zhangang Han and Rana Javed Masood

Sensors 2021, 21(11), 3820; https://doi.org/10.3390/s21113820 - 31 May 2021

Cited by 34 | Viewed by 3804

Abstract

This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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29 pages, 1469 KiB

Open AccessArticle

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

by Woonghee Lee

Sensors 2021, 21(9), 3295; https://doi.org/10.3390/s21093295 - 10 May 2021

Cited by 5 | Viewed by 3035

Abstract

In the last ten years, supported by the advances in technologies for unmanned aerial vehicles (UAVs), UAVs have developed rapidly and are utilized for a wide range of applications. To operate UAVs safely, by exchanging control packets continuously, operators should be able to monitor UAVs in real-time and deal with any problems immediately. However, due to any networking problems or unstable wireless communications, control packets can be lost or transmissions can be delayed, which causes the unstable drone control. To overcome this limitation, in this paper, we propose MuTran for enabling reliable UAV control. MuTran considers the packet type and duplicates only control packets, not data packets. After that, MuTran transmits the original and duplicate packets through multiple protocols and paths to improve the reliability of control packet transmissions. We designed MuTran and conducted a lot of theoretical analyses to demonstrate the validity of MuTran and analyze it from various aspects. We implemented MuTran on real devices and evaluated MuTran using the devices. We conducted experiments to verify the limitations of the existing systems and demonstrate that control packets can be transmitted more stably by using MuTran. Through the analysis and experimental results, we confirmed that MuTran reduces the control packet transfer delay, which improves the reliability and stability of controlling UAVs. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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19 pages, 694 KiB

Open AccessArticle

An MPTCP-Based Transmission Scheme for Improving the Control Stability of Unmanned Aerial Vehicles

by Woonghee Lee, Joon Yeop Lee, Hyeontae Joo and Hwangnam Kim

Sensors 2021, 21(8), 2791; https://doi.org/10.3390/s21082791 - 15 Apr 2021

Cited by 9 | Viewed by 3052

Abstract

Recently, unmanned aerial vehicles (UAVs) have been applied to various applications. In order to perform repetitive and accurate tasks with a UAV, it is more efficient for the operator to perform the tasks through an integrated management program rather than controlling the UAVs one by one through a controller. In this environment, control packets must be reliably delivered to the UAV to perform missions stably. However, wireless communication is at risk of packet loss or packet delay. Typical network communications can respond to situations in which packets are lost by retransmitting lost packets. However, in the case of UAV control, delay due to retransmission is fatal, so control packet loss and delay should not occur. As UAVs move quickly, there is a high risk of accidents if control packets are lost or delayed. In order to stably control a UAV by transmitting control messages, we propose a control packet transmission scheme, ConClone. ConClone replicates control packets and then transmits them over multiple network connections to increase the probability of successful control packet transmission. We implemented ConClone using real equipment, and we verified its performance through experiments and theoretical analysis. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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19 pages, 6159 KiB

Open AccessArticle

A Robot Operating System Framework for Secure UAV Communications

by Hyojun Lee, Jiyoung Yoon, Min-Seong Jang and Kyung-Joon Park

Sensors 2021, 21(4), 1369; https://doi.org/10.3390/s21041369 - 15 Feb 2021

Cited by 16 | Viewed by 6346

Abstract

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on several perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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13 pages, 692 KiB

Open AccessArticle

Dynamic Bandwidth Part Allocation in 5G Ultra Reliable Low Latency Communication for Unmanned Aerial Vehicles with High Data Rate Traffic

by Minsig Han, Jaewon Lee, Minjoong Rim and Chung G. Kang

Sensors 2021, 21(4), 1308; https://doi.org/10.3390/s21041308 - 12 Feb 2021

Cited by 11 | Viewed by 3356

Abstract

The 3GPP standardized the physical layer specification in 5G New Radio to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) coexistence in usage scenarios including aerial vehicles (AVs). Dynamic multiplexing of URLLC traffic was standardized to increase the outage capacity. DM allocates a fully overlapped bandwidth part (BWP) of eMBB and URLLC AVs to perform the immediate scheduling of URLLC traffic by puncturing ongoing eMBB traffic. However, DM often suffers from a significant frame error incurred by puncturing. Meanwhile, BWP can be sliced orthogonally for eMBB and URLLC AVs, possibly preventing overdimensioning the resources depending on the eMBB and URLLC traffic loads. In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS), so as to minimize an impact of uRLLC traffic on eMBB traffic. To implement efficient BWP allocation, the capacity region is analyzed by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) levels and code block group size on DM and OS. OS is effective for improving the eMBB throughput under a URLLC latency constraint for deterministic and predictable URLLC traffic, whereas DM has limited error-correcting capability against the URLLC’s puncturing effect. The relative MCS level of eMBB and URLLC is critical in determining the eMBB traffic tolerance against puncturing. Identifying the performance tradeoff between DM and OS, the tolerance level is quantified by a URLLC load threshold. It is given in an approximate closed form, which is an essential reference for selecting DM over OS, enabling dynamic BWP allocation for the URLLC AV. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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15 pages, 5766 KiB

Open AccessCommunication

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

by Sooyeon Kim, Jae Hyun Kwak, Byoungryul Oh, Da-Han Lee and Duehee Lee

Sensors 2021, 21(4), 1219; https://doi.org/10.3390/s21041219 - 9 Feb 2021

Cited by 11 | Viewed by 3068

Abstract

A delivery service using unmanned aerial vehicles (UAVs) has potential as a future business opportunity, due to its speed, safety and low-environmental impact. To operate a UAV delivery network, a management system is required to optimize UAV delivery routes. Therefore, we create a routing algorithm to find optimal round-trip routes for UAVs, which deliver goods from depots to customers. Optimal routes per UAV are determined by minimizing delivery distances considering the maximum range and loading capacity of the UAV. In order to accomplish this, we propose an algorithm with four steps. First, we build a virtual network to describe the realistic environment that UAVs would encounter during operation. Second, we determine the optimal number of in-service UAVs per depot. Third, we eliminate subtours, which are infeasible routes, using flow variables part of the constraints. Fourth, we allocate UAVs to customers minimizing delivery distances from depots to customers. In this process, we allow multiple UAVs to deliver goods to one customer at the same time. Finally, we verify that our algorithm can determine the number of UAVs in service per depot, round-trip routes for UAVs, and allocate UAVs to customers to deliver at the minimum cost. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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18 pages, 25118 KiB

Open AccessArticle

Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks

by Dongsuk Park, Seungeui Lee, SeongUk Park and Nojun Kwak

Sensors 2021, 21(1), 210; https://doi.org/10.3390/s21010210 - 31 Dec 2020

Cited by 29 | Viewed by 6133

Abstract

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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19 pages, 1674 KiB

Open AccessArticle

Devising a Distributed Co-Simulator for a Multi-UAV Network

by Seongjoon Park, Woong Gyu La, Woonghee Lee and Hwangnam Kim

Sensors 2020, 20(21), 6196; https://doi.org/10.3390/s20216196 - 30 Oct 2020

Cited by 9 | Viewed by 3400

Abstract

Practical evaluation of the Unmanned Aerial Vehicle (UAV) network requires a lot of money to build experiment environments, which includes UAVs, network devices, flight controllers, and so on. To investigate the time-sensitivity of the multi-UAV network, the influence of the UAVs’ mobility should be precisely evaluated in the long term. Although there are some simulators for UAVs’ physical flight, there is no explicit scheme for simulating both the network environment and the flight environments simultaneously. In this paper, we propose a novel co-simulation scheme for the multiple UAVs network, which performs the flight simulation and the network simulation simultaneously. By considering the dependency between the flight status and networking situations of UAV, our work focuses on the consistency of simulation state through synchronization among simulation components. Furthermore, we extend our simulator to perform multiple scenarios by exploiting distributed manner. We verify our system with respect to the robustness of time management and propose some use cases which can be solely simulated by this. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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25 pages, 3606 KiB

Open AccessArticle

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

by Anis Koubaa, Adel Ammar, Mahmoud Alahdab, Anas Kanhouch and Ahmad Taher Azar

Sensors 2020, 20(18), 5240; https://doi.org/10.3390/s20185240 - 14 Sep 2020

Cited by 49 | Viewed by 5489

Abstract

Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays. Full article

(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)

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