Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2021 | OriginalPaper | Chapter

Integration of a RTT Prediction into a Multi-path Communication Gateway

Authors : Josef Schmid, Patrick Purucker, Mathias Schneider, Rick vander Zwet, Morten Larsen, Alfred Höß

Published in: Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops

Publisher: Springer International Publishing

share
SHARE

Abstract

Reliable communication between the vehicle and its environment is an important aspect, to enable automated driving functions that include data from outside the vehicle. One way to achieve this is presented in this paper, a pipeline, that represents the entire process from data acquisition up to model inference in production. In this paper, a pipeline is developed to conduct a round-trip time prediction for TCP in the 4th generation of mobile network, called LTE. The pipeline includes data preparation, feature selection, model training and evaluation, and deployment of the model. In addition to the technical backgrounds of the design of the required steps for the deployment of a model on a target platform within the vehicle, a concrete implementation how such a model enables more reliable scheduling between multiple communication paths is demonstrated. Finally, the work outlines how such a feature can be applied beyond the field of automated vehicles, e.g. to the domain of unmanned aerial vehicles.
Literature
1.
go back to reference Abbasloo, S., Xu, Y., Chao, H.J.: C2TCP: a flexible cellular TCP to meet stringent delay requirements. IEEE J. Sel. Areas Commun. 37(4), 918–932 (2019) CrossRef Abbasloo, S., Xu, Y., Chao, H.J.: C2TCP: a flexible cellular TCP to meet stringent delay requirements. IEEE J. Sel. Areas Commun. 37(4), 918–932 (2019) CrossRef
2.
go back to reference Belhaj, S., Tagina, M.: Modeling and prediction of the internet end-to-end delay using recurrent neural networks. J. Networks 4(6), 528–535 (2009) CrossRef Belhaj, S., Tagina, M.: Modeling and prediction of the internet end-to-end delay using recurrent neural networks. J. Networks 4(6), 528–535 (2009) CrossRef
3.
go back to reference Beverly, R., Sollins, K., Berger, A.: SVM learning of IP address structure for latency prediction. In: Proceedings of the 2006 SIGCOMM workshop on Mining Network Data. MineNet 2006, Pisa, Italy, pp. 299–304. Association for Computing Machinery, September 2006 Beverly, R., Sollins, K., Berger, A.: SVM learning of IP address structure for latency prediction. In: Proceedings of the 2006 SIGCOMM workshop on Mining Network Data. MineNet 2006, Pisa, Italy, pp. 299–304. Association for Computing Machinery, September 2006
4.
go back to reference Bhoi, S.K., Khilar, P.M.: Vehicular communication: a survey. IET Networks 3(3), 204–217 (2014) CrossRef Bhoi, S.K., Khilar, P.M.: Vehicular communication: a survey. IET Networks 3(3), 204–217 (2014) CrossRef
5.
go back to reference Christen, M., Guillaume, M., Jablonowski, M., Moll, K.: Zivile Drohnen-Herausforderungen und Perspektiven. No. 66/2018 in TA-SWISS, vdf, Zürich (2018) Christen, M., Guillaume, M., Jablonowski, M., Moll, K.: Zivile Drohnen-Herausforderungen und Perspektiven. No. 66/2018 in TA-SWISS, vdf, Zürich (2018)
6.
go back to reference Garcia-Roger, D., González, E.E., Martín-Sacristán, D., Monserrat, J.F.: V2X Support in 3GPP Specifications: From 4G to 5G and Beyond. IEEE Access 8, 190946–190963 (2020) Garcia-Roger, D., González, E.E., Martín-Sacristán, D., Monserrat, J.F.: V2X Support in 3GPP Specifications: From 4G to 5G and Beyond. IEEE Access 8, 190946–190963 (2020)
7.
go back to reference Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017) Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017)
8.
go back to reference Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002) CrossRef Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002) CrossRef
9.
go back to reference Jacobson, V.: Congestion avoidance and control. Comput. Commun. Rev. 17 (1988) Jacobson, V.: Congestion avoidance and control. Comput. Commun. Rev. 17 (1988)
10.
go back to reference Khatouni, A.S., Soro, F., Giordano, D.: A machine learning application for latency prediction in operational 4G newtorks. In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 71–74 (2019) Khatouni, A.S., Soro, F., Giordano, D.: A machine learning application for latency prediction in operational 4G newtorks. In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 71–74 (2019)
11.
go back to reference Ludwig, R., Sklower, K.: The Eifel retransmission timer. ACM SIGCOMM Comput. Commun. Rev. 30(3), 17–27 (2000) CrossRef Ludwig, R., Sklower, K.: The Eifel retransmission timer. ACM SIGCOMM Comput. Commun. Rev. 30(3), 17–27 (2000) CrossRef
12.
go back to reference Ma, L., Arce, G., Barner, K.: TCP retransmission timeout algorithm using weighted medians. IEEE Signal Process. Lett. 11(6), 569–572 (2004) CrossRef Ma, L., Arce, G., Barner, K.: TCP retransmission timeout algorithm using weighted medians. IEEE Signal Process. Lett. 11(6), 569–572 (2004) CrossRef
13.
go back to reference Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014) Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)
15.
go back to reference Rizo-Dominguez, L., Munoz-Rodriguez, D., Vargas-Rosales, C., Torres-Roman, D., Ramirez-Pacheco, J.: RTT prediction in heavy tailed networks. IEEE Commun. Lett. 18(4), 700–703 (2014) CrossRef Rizo-Dominguez, L., Munoz-Rodriguez, D., Vargas-Rosales, C., Torres-Roman, D., Ramirez-Pacheco, J.: RTT prediction in heavy tailed networks. IEEE Commun. Lett. 18(4), 700–703 (2014) CrossRef
16.
go back to reference Scholkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000) CrossRef Scholkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000) CrossRef
17.
go back to reference Schneider, M.: Online and microservice-based data throughput prediction framework in context of mobile-based vehicle-to-server communication for automated driving (2018) Schneider, M.: Online and microservice-based data throughput prediction framework in context of mobile-based vehicle-to-server communication for automated driving (2018)
18.
19.
go back to reference Sulei, X., Liang, W.: Smoothly estimate the RTT of fast TCP by ARMA function model. In: 10th International Conference on Wireless Communications. Networking and Mobile Computing (WiCOM 2014), Beijing, China, pp. 333–339. Institution of Engineering and Technology (2014) Sulei, X., Liang, W.: Smoothly estimate the RTT of fast TCP by ARMA function model. In: 10th International Conference on Wireless Communications. Networking and Mobile Computing (WiCOM 2014), Beijing, China, pp. 333–339. Institution of Engineering and Technology (2014)
20.
go back to reference Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.: Use of the zero-norm with linear models and kernel methods. J. Mach. Learn. Res. 3, 1439–1461 (2003) MathSciNetMATH Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.: Use of the zero-norm with linear models and kernel methods. J. Mach. Learn. Res. 3, 1439–1461 (2003) MathSciNetMATH
21.
go back to reference Yang, G., et al.: A telecom perspective on the internet of drones: from LTE-advanced to 5G, 8 (2018) Yang, G., et al.: A telecom perspective on the internet of drones: from LTE-advanced to 5G, 8 (2018)
22.
go back to reference Zhang, S., Chen, J., Lyu, F., Cheng, N., Shi, W., Shen, X.: Vehicular communication networks in the automated driving era. IEEE Commun. Mag. 56(9), 26–32 (2018) CrossRef Zhang, S., Chen, J., Lyu, F., Cheng, N., Shi, W., Shen, X.: Vehicular communication networks in the automated driving era. IEEE Commun. Mag. 56(9), 26–32 (2018) CrossRef
23.
go back to reference Zheng, A., Casari, A.: Feature engineering for machine learning: principles and techniques for data scientists. O’Reilly Media, Inc. (2018) Zheng, A., Casari, A.: Feature engineering for machine learning: principles and techniques for data scientists. O’Reilly Media, Inc. (2018)
Metadata
Title
Integration of a RTT Prediction into a Multi-path Communication Gateway
Authors
Josef Schmid
Patrick Purucker
Mathias Schneider
Rick vander Zwet
Morten Larsen
Alfred Höß
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-83906-2_16

Premium Partner