Abstract
This paper presents a prototype for an aerial mobile network relay in the setting of pre-hospital emergency care. The requirements for such an unmanned aerial vehicle (UAV) are summarized and used to develop a hardware prototype. Gaussian processes and Bayesian optimization are implemented to find an optimal location for the aerial relay. The aerial relay enhances the network throughput by a factor of up to 7.2 in comparison to a relay on the ground. The findings show that UAV-based relays have the potential to play a vital role in emergency rescue.
Zusammenfassung
In diesem Beitrag wird der Prototyp eines fliegenden Netzwerkrelays für den Rettungsdienst vorgestellt. Die Anforderungen an ein solches unbemanntes Luftfahrtsystem (UAS) werden zusammengefasst und für die Entwicklung eines Hardware-Prototyps verwendet. Gauß’sche Prozesse und Bayes’sche Optimierung werden eingesetzt, um einen optimalen Standort für das Relay zu finden. Das fliegende Relay verbessert den Netzwerkdurchsatz um einen Faktor von bis zu 7,2 im Vergleich zu einem Relay am Boden. Die Ergebnisse zeigen, dass UAV-Relays eine wichtige Rolle in der Notfallversorgung spielen können.
About the authors
Jonas Gruner has held a B.Sc. and M.Sc. degree in Medical Engineering Science from Universität zu Lübeck since 2019. He is a Research Associate at the Institute of Electrical Engineering in Medicine at the Universität zu Lübeck. His research interests include intelligent path planning and control for autonomous systems.
Carlos Castelar holds a B.Sc. from Universidad Don Bosco and a M.Sc. from FH Aachen in Biomedical Engineering. He worked as a Research Associate at the Chair of Medical Information Technology, RWTH Aachen and is now at the Institute of Electrical Engineering in Medicine at Universität zu Lübeck working on the field of autonomous systems.
Tavia Plattenteich was a student Research Assistant at the Institute of Electrical Engineering in Medicine at the Universität zu Lübeck from 2019 to 2023. She has held a B.Sc. degree in Robotics and Autonomous Systems from Universität zu Lübeck since 2021. Since 2023 she has been Research Assistant at the Institute of Computer Engineering at the Universität zu Lübeck.
Jasper Pflughaupt was a student Research Assistant at the Institute of Electrical Engineering in Medicine at the Universität zu Lübeck from 2020 to 2023. He has held a B.Sc. and M.Sc. degree in Robotics and Autonomous Systems from Universität zu Lübeck since 2023. His research interests include the autonomous operation and data-driven control of mobile systems, especially drones.
Ievgen Zhavzharov is a Technical Engineer at the Institute of Electrical Engineering in Medicine at Universität zu Lübeck. In 2010 he received his Ph.D. from the PNU in Ivano-Frankivsk, UA. From 2010 to 2017, he worked as an associate professor at the Faculty of Electrical Engineering of the National Technical University of Zaporizhzhia, UA.
Georg Schildbach has been a Professor of Mechatronics at the Universität zu Lübeck since 2018. He received his Ph.D. from ETH Zurich, CH in 2014. His research interests are real-time optimization, predictive and learning-based control, and safety and reliability for autonomous systems.
Philipp Rostalski is a professor for Electrical Engineering in Medicine and head of the corresponding institute at the Universität zu Lübeck. He is also a director at the Fraunhofer Research Institution for Individualized and Cell-based Medical Engineering in Lübeck. He received his Ph.D. from ETH Zurich, CH in 2009.
-
Research ethics: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The authors state no conflict of interest.
-
Research funding: The work was funded by the German Federal Ministry of Education and Research under grant no.∼16KIS1027.
-
Data availability: The raw data can be obtained on request from the corresponding author.
References
[1] J. Gaebel, et al., “Requirements for 5G integrated data transfer in German prehospital emergency care,” Curr. Dir. Biomed. Eng., vol. 6, no. 3, pp. 9–12, 2020. https://doi.org/10.1515/cdbme-2020-3003.Search in Google Scholar
[2] M. Rockstroh, et al., “Towards an integrated emergency medical care using 5G networks,” Curr. Dir. Biomed. Eng., vol. 6, no. 3, pp. 5–8, 2020. https://doi.org/10.1515/cdbme-2020-3002.Search in Google Scholar
[3] J. Gruner, A. Bloße, M. Rockstroh, T. Neumuth, and P. Rostalski, “Requirement analysis for an aerial relay in emergency response missions,” Curr. Dir. Biomed. Eng., vol. 6, no. 3, pp. 16–19, 2020. https://doi.org/10.1515/cdbme-2020-3005.Search in Google Scholar
[4] A. Colpaert, E. Vinogradov, and S. Pollin, “Aerial coverage analysis of cellular systems at LTE and mmWave frequencies using 3D city models,” Sensors, vol. 18, no. 12, p. 4311, 2018. https://doi.org/10.3390/s18124311.Search in Google Scholar PubMed PubMed Central
[5] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun., vol. 15, no. 6, pp. 3949–3963, 2016. https://doi.org/10.1109/twc.2016.2531652.Search in Google Scholar
[6] L. Sundqvist, “Cellular controlled drone experiment: evaluation of network requirements,” Master’s thesis, Aalto University. School of Electrical Engineering, 2015.Search in Google Scholar
[7] S. C. Arum, D. Grace, and P. D. Mitchell, “A review of wireless communication using high-altitude platforms for extended coverage and capacity,” Comput. Commun., vol. 157, pp. 232–256, 2020. https://doi.org/10.1016/j.comcom.2020.04.020.Search in Google Scholar
[8] I. Nagata, T. Abe, Y. Nakata, and N. Tamiya, “Factors related to prolonged on-scene time during ambulance transportation for critical emergency patients in a big city in Japan: a population-based observational study,” BMJ Open, vol. 6, no. 1, p. e009599, 2016. https://doi.org/10.1136/bmjopen-2015-009599.Search in Google Scholar PubMed PubMed Central
[9] E. A. Ranquist, M. Steiner, and B. Argrow, “Exploring the range of weather impacts on UAS operations,” in 18th Conference on Aviation, Range, and Aerospace Meteorology, American Meteorological Society, 2017.Search in Google Scholar
[10] N. Schmid, J. Gruner, H. S. Abbas, and P. Rostalski, “A real-time GP based MPC for quadcopters with unknown disturbances,” in 2022 American Control Conference (ACC), IEEE, 2022, pp. 2051–2056.10.23919/ACC53348.2022.9867594Search in Google Scholar
[11] J. Gruner, N. Schmid, G. Männel, J. Grasshof, H. S. Abbas, and P. Rostalski, “Recursively feasible model predictive control using latent force models applied to disturbed quadcopters,” in 2022 IEEE 61st Conference on Decision and Control (CDC), IEEE, 2022, pp. 1013–1020.10.1109/CDC51059.2022.9992944Search in Google Scholar
[12] L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, “PIXHAWK: a system for autonomous flight using onboard computer vision,” in 2011 IEEE International Conference on Robotics and Automation, IEEE, 2011, pp. 2992–2997.10.1109/ICRA.2011.5980229Search in Google Scholar
[13] M. Colledanchise and P. Ogren, “How behavior trees modularize robustness and safety in hybrid systems,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2014.10.1109/IROS.2014.6942752Search in Google Scholar
[14] E. Eyceyurt, Y. Egi, and J. Zec, “Machine-learning-based uplink throughput prediction from physical layer measurements,” Electronics, vol. 11, no. 8, p. 1227, 2022. https://doi.org/10.3390/electronics11081227.Search in Google Scholar
[15] C. K. I. Williams and C. E. Rasmussen, Gaussian Processes for Machine Learning, vol. 2 TS – RI, Cambridge, MA, MIT press, 2006.Search in Google Scholar
[16] R. D. Taranto, S. Muppirisetty, R. Raulefs, D. Slock, T. Svensson, and H. Wymeersch, “Location-aware communications for 5G networks: how location information can improve scalability, latency, and robustness of 5g,” IEEE Signal Process. Mag., vol. 31, no. 6, pp. 102–112, 2014. https://doi.org/10.1109/msp.2014.2332611.Search in Google Scholar
[17] 3GPP, Study on Channel Model for Frequencies from 0.5 to 100 GHz, Valbonne, ETSI, 2017, pp. 26–28.Search in Google Scholar
[18] 3GPP, Study on Enhanced LTE Support for Aerial Vehicles, Valbonne, 3GPP, 2017, pp. 26–27.Search in Google Scholar
[19] E. Brochu, V. M. Cora, and N. de Freitas, “A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning,” CoRR, 2010.Search in Google Scholar
[20] Data for Good at Meta, “Germany: high resolution population density maps + demographic estimates,” [Online], 2020. Available at: https://data.humdata.org/dataset/germany-high-resolution-population-density-maps-demographic-estimates.Search in Google Scholar
[21] S. Yang, N. Wei, S. Jeon, R. Bencatel, and A. Girard, “Real-time optimal path planning and wind estimation using Gaussian process regression for precision airdrop,” in 2017 American Control Conference (ACC), IEEE, 2017, pp. 2582–2587.10.23919/ACC.2017.7963341Search in Google Scholar
[22] A. G. D. G. Matthews, et al.., “GPflow: a Gaussian process library using TensorFlow,” J. Mach. Learn. Res., vol. 18, no. 40, pp. 1–6, 2017.Search in Google Scholar
[23] M. Abadi, et al.., “TensorFlow: large-scale machine learning on heterogeneous systems,” [Online], 2015. Available at: https://www.tensorflow.org/.Search in Google Scholar
[24] C. Castelar Wembers, J. Pflughaupt, L. Moshagen, M. Kurenkov, T. Lewejohann, and G. Schildbach, “LiDAR-based automated UAV inspection of wind turbine rotor blades,” J. Field Robot., vol. 41, no. 4, pp. 1116–1132, 2024. https://doi.org/10.1002/rob.22309.Search in Google Scholar
[25] L. Afonso, N. Souto, P. Sebastiao, M. Ribeiro, T. Tavares, and R. Marinheiro, “Cellular for the skies: exploiting mobile network infrastructure for low altitude air-to-ground communications,” IEEE Aerosp. Electron. Syst. Mag., vol. 31, no. 8, pp. 4–11, 2016. https://doi.org/10.1109/maes.2016.150170.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston