research-article

A Multilayer Low-Altitude Airspace Model for UAV Traffic Management

Authors:

Nader S. Labib,

Grégoire Danoy,

Jedrzej Musial,

Matthias R. Brust,

Pascal BouvryAuthors Info & Claims

DIVANet '19: Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Pages 57 - 63

https://doi.org/10.1145/3345838.3355998

Published: 25 November 2019 Publication History

Abstract

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly, in turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and surveillance, parcel delivery to more demanding ones that require UAVs to operate in heterogeneous swarms. UAVs are thus expected to soon dominate the shared, low-altitude airspace over populated cities, introducing multiple new research challenges in safely managing the unprecedented traffic demands. The main contribution of this work is addressing the complex problem of UAV traffic management at an abstract level by proposing a structure for the uncontrolled low-altitude airspace. The paper proposes a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulations of UAV traffic for the validation of the model.

References

[1]

Federico Battiston, Matjavz Perc, and Vito Latora. 2017. Determinants of public cooperation in multiplex networks. New Journal of Physics, Vol. 19, 7 (2017), 073017.

[2]

Matthias R Brust, Grégoire Danoy, Pascal Bouvry, Dren Gashi, Himadri Pathak, and Mike P Goncc alves. 2017. Defending against intrusion of malicious UAVs with networked UAV defense swarms. In 42nd IEEE Conference on Local Computer Networks Workshops. IEEE, 103--111.

[3]

Philip Butterworth-Hayes. [n. d.]. The Unmanned Air System Traffic Management UTM directory. https://www.unmannedairspace.info.

[4]

Alessio Cardillo, Massimiliano Zanin, Jesús Gómez-Gardenes, Miguel Romance, Alejandro J Garc'ia del Amo, and Stefano Boccaletti. 2013. Modeling the multi-layer nature of the European Air Transport Network: Resilience and passengers re-scheduling under random failures. The European Physical Journal Special Topics, Vol. 215, 1 (2013), 23--33.

[5]

Jungwoo Cho and Yoonjin Yoon. 2018. Assessing the airspace availability for sUAV operations in urban environments: A topological approach using keep-in and keep-out geofence. In 2018 International Conference on Research in Air Transportation (ICRAT). ICRAT.

[6]

Gregorio D'Agostino and Antonio Scala. 2014. Networks of networks: the last frontier of complexity. Vol. 340. Springer.

[7]

Grégoire Danoy, Matthias R. Brust, and Pascal Bouvry. 2015. Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring. In Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications (DIVANet '15). ACM, New York, NY, USA, 1--8.

Digital Library

[8]

José Capela Dias, Penousal Machado, Daniel Castro Silva, and Pedro Henriques Abreu. 2014. An inverted ant colony optimization approach to traffic. Engineering Applications of Artificial Intelligence, Vol. 36 (2014), 122--133.

Digital Library

[9]

Mark Dickison, Shlomo Havlin, and H Eugene Stanley. 2012. Epidemics on interconnected networks. Physical Review E, Vol. 85, 6 (2012), 066109.

[10]

Marco Dorigo, Vittorio Maniezzo, Alberto Colorni, et almbox. 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, man, and cybernetics, Vol. 26, 1 (1996), 29--41.

Digital Library

[11]

Herbert Edelsbrunner, David Letscher, and Afra Zomorodian. 2000. Topological persistence and simplification. In Proceedings 41st Annual Symposium on Foundations of Computer Science. IEEE, 454--463.

Digital Library

[12]

Riccardo Gallotti and Marc Barthelemy. 2015. The multilayer temporal network of public transport in Great Britain. Scientific data, Vol. 2 (2015).

[13]

Chad Goerzen, Zhaodan Kong, and Bernard Mettler. 2010. A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent and Robotic Systems, Vol. 57, 1--4 (2010), 65.

Digital Library

[14]

Marko Gosak, Rene Markovivc, Jurij Dolenvs ek, Marjan Slak Rupnik, Marko Marhl, Andravz Stovz er, and Matjavz Perc. 2018. Network science of biological systems at different scales: a review. Physics of life reviews, Vol. 24 (2018), 118--135.

[15]

Chen Hong, Jun Zhang, Xian-Bin Cao, and Wen-Bo Du. 2016. Structural properties of the Chinese air transportation multilayer network. Chaos, Solitons & Fractals, Vol. 86 (2016), 28--34.

[16]

Mikko Huttunen. 2019. The U-space Concept. Air and Space Law, Vol. 44, 1 (2019), 69--89.

[17]

Myeong-hwan Hwang, Hyun-Rok Cha, and Sung Jung. 2018. Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles. Energies, Vol. 11, 9 (2018), 2221.

[18]

Parimal Kopardekar and Joseph Rios. 2015. Enabling Civilian Low-Altitude Airspace and Unmanned Aerial System (UAS) Operations by UTM. (2015).

[19]

Fernando Kuipers and Freek Dijkstra. 2009. Path selection in multi-layer networks. Computer Communications, Vol. 32, 1 (2009), 78--85.

Digital Library

[20]

Maxim Likhachev, David I Ferguson, Geoffrey J Gordon, Anthony Stentz, and Sebastian Thrun. 2005. Anytime Dynamic A*: An Anytime, Replanning Algorithm. In ICAPS, Vol. 5. 262--271.

[21]

Maxim Likhachev, Geoffrey J Gordon, and Sebastian Thrun. 2004. ARA*: Anytime A* with provable bounds on sub-optimality. In Advances in neural information processing systems. 767--774.

[22]

Matteo Magnani and Luca Rossi. 2011. The ml-model for multi-layer social networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on. IEEE, 5--12.

Digital Library

[23]

Nicoló Musmeci, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, and Vito Latora. 2017. The multiplex dependency structure of financial markets. Complexity (2017).

[24]

Alex Nash, Sven Koenig, and Craig Tovey. 2010. Lazy Theta*: Any-angle path planning and path length analysis in 3D. In Twenty-Fourth AAAI Conference on Artificial Intelligence .

[25]

Shai Pilosof, Mason A Porter, Mercedes Pascual, and Sonia Kéfi. 2017. The multilayer nature of ecological networks. Nature Ecology & Evolution, Vol. 1, 4 (2017), 0101.

[26]

Nader S. Labib, Matthias R. Brust, Grégoire Danoy, and Pascal Bouvry. 2018. On Standardised Localisation and Tracking Systems for UAVs in Smart Cities. In 17th Annual STS Conference Graz, Critical Issues in Science, Technology and Society Studies .

[27]

Jose Luis Sanchez-Lopez, Min Wang, Miguel A. Olivares-Mendez, Martin Molina, and Holger Voos. 2019. A Real-Time 3D Path Planning Solution for Collision-Free Navigation of Multirotor Aerial Robots in Dynamic Environments. Journal of Intelligent & Robotic Systems, Vol. 93, 1 (01 Feb 2019), 33--53.

Digital Library

[28]

Lukas Marcel Schalk. 2017. Communication links for unmanned aircraft systems in very low level airspace. In Integrated Communications, Navigation and Surveillance Conference (ICNS), 2017. IEEE, 1--26.

[29]

A Schwithal, C Tonh"auser, S Wolkow, M Angermann, P Hecker, N Mumm, and F Holzapfel. 2017. Integrity monitoring in GNSS/INS systems by optical augmentation. In Inertial Sensors and Systems (ISS), 2017. IEEE, 1--22.

[30]

S. Singh, R. P. Joshi, and H. Kohli. 2015. Optimal Route Searching in Networks with Dynamic Weights Using Flow Algorithms. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). 146--155.

[31]

Claudia Stöcker, Rohan Bennett, Francesco Nex, Markus Gerke, and Jaap Zevenbergen. 2017. Review of the current state of UAV regulations. Remote sensing, Vol. 9, 5 (2017), 459.

[32]

Emmanuel Sunil, Jacco Hoekstra, Joost Ellerbroek, Frank Bussink, Andrija Vidosavljevic, Daniel Delahaye, and Roalt Aalmoes. 2016. The influence of traffic structure on airspace capacity. In ICRAT2016--7th International Conference on Research in Air Transportation .

[33]

Dimitrios Tsiotas and Serafeim Polyzos. 2015. Decomposing multilayer transportation networks using complex network analysis: a case study for the Greek aviation network. Journal of Complex Networks, Vol. 3, 4 (2015), 642--670.

[34]

Ludwig Weber. 2007. International Civil Aviation Organization. An Introduction. Air and Space Law, Vol. 32, 4 (2007), 417--417.

[35]

Qiyue Wu, Peng Sun, and Azzedine Boukerche. 2018. An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks. In Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet'18). ACM, New York, NY, USA, 34--40.

Digital Library

[36]

Liang Yang, Juntong Qi, Dalei Song, Jizhong Xiao, Jianda Han, and Yong Xia. 2016. Survey of robot 3D path planning algorithms. Journal of Control Science and Engineering, Vol. 2016 (2016), 5.

Digital Library

Cited By

Tuncal AErol U(2024)Integrating Unmanned Aerial Vehicles in Airspace: A Systematic ReviewJournal of Aviation Research10.51785/jar.13932716:1(89-115)Online publication date: 28-Feb-2024
https://doi.org/10.51785/jar.1393271
Xiao JZhang HShao ZZheng YDing W(2024)Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication ScenariosRemote Sensing10.3390/rs1619369616:19(3696)Online publication date: 4-Oct-2024
https://doi.org/10.3390/rs16193696
Zhang WWu HLiu YZhou SDong HLiu H(2024)Dual-Metric-Based Assessment and Topology Generation of Urban Airspace with Quadrant Analysis and Pareto RankingAerospace10.3390/aerospace1112097811:12(978)Online publication date: 27-Nov-2024
https://doi.org/10.3390/aerospace11120978
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Index Terms

A Multilayer Low-Altitude Airspace Model for UAV Traffic Management
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
    2. Planning and scheduling
      1. Robotic planning
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Network optimization

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Published In

cover image ACM Conferences

DIVANet '19: Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

November 2019

119 pages

ISBN:9781450369077

DOI:10.1145/3345838

General Chair:
Mirela Notare
Technology University on Fly Transportation, Brazil
,
Program Chair:
Peng Sun
University of Ottawa, Canada

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 25 November 2019

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MSWiM '19: 22nd Int'l ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 25 - 29, 2019

FL, Miami Beach, USA

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Overall Acceptance Rate 70 of 308 submissions, 23%

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Cited By

Tuncal AErol U(2024)Integrating Unmanned Aerial Vehicles in Airspace: A Systematic ReviewJournal of Aviation Research10.51785/jar.13932716:1(89-115)Online publication date: 28-Feb-2024
https://doi.org/10.51785/jar.1393271
Xiao JZhang HShao ZZheng YDing W(2024)Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication ScenariosRemote Sensing10.3390/rs1619369616:19(3696)Online publication date: 4-Oct-2024
https://doi.org/10.3390/rs16193696
Zhang WWu HLiu YZhou SDong HLiu H(2024)Dual-Metric-Based Assessment and Topology Generation of Urban Airspace with Quadrant Analysis and Pareto RankingAerospace10.3390/aerospace1112097811:12(978)Online publication date: 27-Nov-2024
https://doi.org/10.3390/aerospace11120978
Gong TNnamani CXu YTsourdos A(2023)Impact of Flight Speed Distribution on Efficiency of Urban Air Traffic Network2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)10.1109/DASC58513.2023.10311281(1-7)Online publication date: 1-Oct-2023
https://doi.org/10.1109/DASC58513.2023.10311281
Rubenecia AChoi MChoi H(2022)Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial VehiclesElectronics10.3390/electronics1103030911:3(309)Online publication date: 19-Jan-2022
https://doi.org/10.3390/electronics11030309
El-Zawawy MBrighente AConti M(2022)SETCAPComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.108804206:COnline publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.108804
Davies LVagapov YGrout VCunningham SAnuchin A(2021)Review of Air Traffic Management Systems for UAV Integration into Urban Airspace2021 28th International Workshop on Electric Drives: Improving Reliability of Electric Drives (IWED)10.1109/IWED52055.2021.9376343(1-6)Online publication date: 27-Jan-2021
https://doi.org/10.1109/IWED52055.2021.9376343
Labib NBrust MDanoy GBouvry P(2021)The Rise of Drones in Internet of Things: A Survey on the Evolution, Prospects and Challenges of Unmanned Aerial VehiclesIEEE Access10.1109/ACCESS.2021.31049639(115466-115487)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3104963
Brust MDanoy GStolfi DBouvry P(2021)Swarm-based counter UAV defense systemDiscover Internet of Things10.1007/s43926-021-00002-x1:1Online publication date: 24-Feb-2021
https://doi.org/10.1007/s43926-021-00002-x
Samir Labib NDanoy GMusial JBrust MBouvry P(2019)Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic ManagementSensors10.3390/s1921477919:21(4779)Online publication date: 3-Nov-2019
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