Elsevier

Computer Networks

Volume 136, 8 May 2018, Pages 105-118
Computer Networks

Experimental characterization of UAV-to-car communications

https://doi.org/10.1016/j.comnet.2018.03.002Get rights and content

Abstract

Unmanned Aerial Vehicles (UAVs), popularly known as drones, can be deployed in conjunction with a network of ground vehicles. In situations where no infrastructure is available, drones can be deployed as mobile infrastructure elements to offer all types of services. Examples of such services include safety in rural areas where, upon an emergency event, drones can be quickly deployed as information relays for distributing critical warning to vehicles. In this work, we analyze the communications performance on the link between cars and drones taking into account the altitude, the antenna orientation, and the relative distance. The presented results show that the communication between a drone and a car can reach up to three kilometers in a rural area, and achieves at least a fifty percent success ratio for the delivery rate at a 2.7 km range. Finally, to allow integrating the communications link behaviour in different network simulators, the experimental results were also modeled with a modified Gaussian function that offers a suitable representation for this kind of communication.

Introduction

Intelligent Transportation Systems (ITS) are able to provide efficient solutions for traffic-related issues, such as safety and efficiency [1]. When attempting to make roads safer, ITS can provide systems that reduce the number of accidents taking place [2], along with safety-related applications [3]. Vehicle-to-Everything (V2X) paradigm that breaks down into the exchange of data between cars (V2V, or Vehicle-to-Vehicle), and infrastructure elements (V2I, or Vehicle-to-Infrastructure) which acts as relays towards a wider network or the Internet, is essential to provide ITS services and applications [4]. However, since vehicular networks are fast-moving and dynamic, the challenge comes when disseminating messages containing critical information that needs to be timely critical and as fast as possible for emergency and safety scenarios [5], [6]. Another challenge to consider in V2X communications is when the coverage area lacks infrastructure support. Although vehicular communications can rely on various radio access technologies [7], e.g. using 4G LTE technology to support communications in areas with limited infrastructure like rural areas [8], major problems typically arise when the communications take place in areas that have no infrastructure support at all.

Unmanned Aerial Vehicles (UAVs) or drones (we will use these two terms interchangeably in this paper) are currently becoming an emerging solution for critical situations, i.e. disaster response like Search And Rescue (SAR) [9] and fire fighting [10]. In addition, compared to terrestrial communications, the adoption of UAVs not only offers a quick and flexible deployment, but also the chances of having Line-Of-Sight (LOS) with the receiver increases due to their higher altitude [11]. Recently, thorough studies analyzed the capabilities of UAVs as communication agents, and their usefulness in several application scenarios [12], [13].

UAVs can also cooperate with ground vehicles in a particular network, allowing to improve the data exchanges between them. This approach offers benefits to multiple ITS applications [14], [15] like rescue and disaster assistance operations [16] and remote sensing [17]. In such cases, we typically rely on multiple UAVs to conform a network between themselves, creating what is known as a Flying Ad-Hoc NETwork (FANET). As a subclass of Vehicular Ad hoc NETworks (VANETs), FANETs differ from standard VANETs since they are characterized by highly mobile nodes moving freely in the 3D space; on the contrary, VANETs are restricted to 2D movements along streets. The challenges in FANET communications vary depending on the specific application. For instance, disaster monitoring introduces strong requirements such as low latency and very high information transmission rate (real-time video feed) [18]. The use of multi-UAV systems can also be beneficial for improving the attainable transmission range and efficiency, as packets can be relayed and forwarded between UAVs to minimize the drawbacks of link interruptions [19].

One of the challenges of FANETs is obtaining an accurate radio propagation model, as this problem differs from typical scenarios addressed in the literature. Most works focus on the link between UAVs and a static ground base [20], which typically has line-of-sight conditions. In another case [21], the authors modeled air-to-ground path losses with UAVs; however, the ground receiver was not a moving node. Instead, in this work, we focus on UAV-to-car communication, which is currently a very important topic [22], [23], [24], and that differs from the ground base case due to vehicular mobility. Thus, it becomes necessary to characterize the communications between UAVs and moving vehicles, and to derive a model that can be used in simulations combining FANET and VANET scenarios.

In this paper, we make a characterization of UAV-to-car communications based on real experiments aimed to foster the development of a communications model to be used in simulation studies. In our scenario, UAVs act as mobile RSUs (Road Side Units), enabling us to perform a study of vehicular communications between aerial and ground vehicles in the 5 GHz band. The experiments were performed in a rural area of Valencia with actual field tests using vehicles and drones to determine the communications performance. In addition, based on the results obtained, we have modeled the packet delivery ratio in different scenarios (drone altitude, antenna rotation, and antenna orientation) using a modified Gaussian function.

The remainder of this paper is organized as follows: in the next section, we provide an overview of related works regarding VANET scenarios that involve air-to-ground communications. In Section 3 we describe the methodology, hardware, and software involved in our experiments. Then, in Section 4, we provide details about the scenario used in our experiments. Experimental results are presented and discussed in Section 5, followed by the modeling of our obtained results in Section 6. Finally, in Section 8, we conclude the paper and refer to future works.

Section snippets

Related works

UAVs have recently been adopted for a wide range of ITS solutions since they can become multi-purpose platforms for both rural and urban areas. Among their many applications, they can be used for surveillance [25], or become an aerial relay when the existing infrastructure fails to provide the desired services adequately, i.e., cell overload or outage [26]. In addition, they can be deployed for establishing a communications system when a disaster occurs [27]. This would allow an emergency

Architecture overview

In this section we start by providing a general overview of the envisioned scenario, and we then detail the proposed architecture, including the data flow and the different elements involved. Our proposed on board unit, named GRCBox [41], is also introduced at the end of this section.

Experimental settings

In this section, we start by providing an overview of the location where experiments took place. Afterward, we detail the experimental tools used. Finally, we analyze the data gathered in our experiments.

Experimental results

The experimental results are presented in heatmaps (see Figs. 11 and 12) for different drone altitudes. Each point represents locations where groups of packets were successfully received. The points have different colors according to the associated delivery ratio. In terms of the relationship between communications range and packet delivery ratio, the results obtained are depicted in Fig. 13. These results will then be modeled in the following section.

Communications modeling

Using the results of the previous experiments, we now proceed by modeling communications based on the different factors being studied (drone’s altitude, transmitter antenna orientation, and receiver antenna location). Notice that our model was obtained based on the number of packets received at each registered position. As the packet delivery ratio is calculated for a small distance interval, we have performed curve fitting to derive the optimal parameters. The models generated can then be

Model applicability and comparison against existing models

In this section we discuss the novelty and applicability of our model by comparing it against other existing propagation models, and explaining how it could be used as a part of existing network simulators.

Conclusions

In this paper, we have studied the packet delivery effectiveness on UAV-to-car communications. For this study, we have varied the drone’s altitude, the transmitter antenna orientation, and the receiver antenna location. According to our experiments, the best scenario for UAV to car communications takes place when the drone’s antenna is pointing down (vertical), the car’s antenna is located outside the vehicle, and the drone’s altitude is very high (100 m, near the maximum allowed limit

Acknowledgment

This work was partially supported by the “Ministerio de Economía y Competividad, Programa Estatal de Investigación, Desarollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014”, Spain, under grants TEC2014-52690-R and BES-2015-075988.

Seilendria A. Hadiwardoyo is a Ph.D. candidate at the Department of Computer Engineering, Universitat Politécnica de Valéncia, Valencia, Spain. He received his B.Eng. degree in computer engineering from Fakultas Teknik, Universitas Indonesia, Depok, Indonesia, and his Pre-Master’s Diploma (Maîtrise, equivalent to Pg.Dip.) in computer science from Université de Lille 1, Villeneuve-d’Ascq, France, both in 2012. Hadiwardoyo obtained his M.S. degree in computer science: web, multimedia, and

References (48)

  • K. Sjoberg et al.

    Cooperative intelligent transport systems in europe: current deployment status and outlook

    IEEE Veh. Technol. Mag.

    (2017)
  • F.J. Martinez et al.

    Emergency services in future intelligent transportation systems based on vehicular communication networks

    IEEE Intell. Transp. Syst. Mag.

    (2010)
  • S.A. Hadiwardoyo et al.

    An intelligent transportation system application for smartphones based on vehicle position advertising and route sharing in vehicular ad-hoc networks

    J. Comput. Sci. Technol.

    (2018)
  • W. Zhu et al.

    A collision avoidance mechanism for emergency message broadcast in urban vanet

    Vehicular Technology Conference (VTC Spring), 2016 IEEE 83rd

    (2016)
  • M. Khabazian et al.

    Performance modeling of safety messages broadcast in vehicular ad hoc networks

    IEEE Trans. Intell. Transp. Syst.

    (2013)
  • S. Waharte et al.

    Supporting search and rescue operations with uavs

    Emerging Security Technologies (EST), 2010 International Conference on

    (2010)
  • C. Yuan et al.

    A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

    Can. J. For. Res.

    (2015)
  • A. Bujari et al.

    Flying ad-hoc network application scenarios and mobility models

    Int. J. Distrib. Sens. Netw.

    (2017)
  • L. Chaimowicz et al.

    Deploying air-ground multi-robot teams in urban environments

    Multi-Robot Systems. From Swarms to Intelligent Automata

    (2005)
  • H. Menouar et al.

    Uav-enabled intelligent transportation systems for the smart city: applications and challenges

    IEEE Commun. Mag.

    (2017)
  • E. Yanmaz et al.

    Communication and coordination for drone networks

    Ad Hoc Networks

    (2017)
  • K. Daniel et al.

    Airshield: a system-of-systems muav remote sensing architecture for disaster response

    Systems conference, 2009 3rd Annual IEEE

    (2009)
  • W. Zafar et al.

    Flying ad-hoc networks: technological and social implications

    IEEE Technol. Soc. Mag.

    (2016)
  • J. Wang et al.

    Taking drones to the next level: cooperative distributed unmanned-aerial-vehicular networks for small and mini drones

    IEEE Veh. Technol. Mag.

    (2017)
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    Seilendria A. Hadiwardoyo is a Ph.D. candidate at the Department of Computer Engineering, Universitat Politécnica de Valéncia, Valencia, Spain. He received his B.Eng. degree in computer engineering from Fakultas Teknik, Universitas Indonesia, Depok, Indonesia, and his Pre-Master’s Diploma (Maîtrise, equivalent to Pg.Dip.) in computer science from Université de Lille 1, Villeneuve-d’Ascq, France, both in 2012. Hadiwardoyo obtained his M.S. degree in computer science: web, multimedia, and networks from Université de Bretagne-Sud, Vannes, France and his Predoctoral Diploma (equivalent to M.Phil.) in informatics from Universidade do Minho, Braga, Portugal, in 2013 and 2015 respectively. His research interests include ad hoc and vehicular networks, mobile applications, and implementation of intelligent transportation systems (ITS).

    Enrique Hernández-Orallo is an assistant professor at the Department of Computer Engineering in the Universitat Politécnica de Valéncia (Spain). He earned an MSc and a Ph.D. in Computer Science from the Universitat Politécnica de Valéncia in 1992 and 2001 respectively. From 1991–2005 he worked at several companies in real-time and computer networks projects. He is a member of the Computer Networks Research Group (GRC) and has participated in over 10 Spanish and European research projects and is author of about 50 journal and conference papers and co-author of two successful books of C++ in Spanish language. His areas of interest include distributed systems, performance evaluation, mobile and pervasive computing, and Real-time systems. He is now mainly working in Performance Evaluation of MANETs and Opportunistic Networks.

    Carlos T. Calafate is an associate professor in the Department of Computer Engineering at the Technical University of Valencia (UPV) in Spain. He graduated with honours in Electrical and Computer Engineering at the University of Oporto (Portugal) in 2001. He received his Ph.D. degree in Informatics from the Technical University of Valencia in 2006, where he has worked since 2002. His research interests include ad-hoc and vehicular networks, UAVs, Smart Cities & IoT, QoS, network protocols, video streaming, and network security. To date he has published more than 300 articles, several of them in journals including IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, IEEE/ACM Transactions on Networking, Elsevier Ad hoc Networks and IEEE Communications Magazine. He has participated in the TPC of more than 150 international conferences. He is a founding member of the IEEE SIG on Big Data with Computational Intelligence.

    Juan Carlos Cano is a full professor in the Department of Computer Engineering at the Polytechnic University of Valencia (UPV) in Spain. He earned an MSc and a Ph.D. in Computer Science from the UPV in 1994 and 2002 respectively. From 1995–1997 he worked as a programming analyst at IBM’s manufacturing division in Valencia. His current research interests include Vehicular Networks, Mobile Ad Hoc Networks, and Pervasive Computing.

    Pietro Manzoni is a full professor in the Department of Computer Engineering at the Polytechnic University of Valencia (UPV) in Spain. He received the MS degree in computer science from the “Universitá degli Studi” of Milan, Italy, in 1989, and the Ph.D. degree in computer science from the Polytechnic University of Milan, Italy, in 1995. His research activity is related to wireless networks protocol design, modelling, and implementation. He is member of the IEEE.

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