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Machine Learning for Autonomic Network Management in a Connected Cars Scenario

  • Conference paper
  • First Online:
Communication Technologies for Vehicles (Nets4Cars/Nets4Trains/Nets4Aircraft 2016)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 9669))

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Abstract

Current 4G networks are approaching the limits of what is possible with this generation of radio technology. Future 5G networks will be highly based on software, with the ultimate goal of being self-managed. Machine Learning is a key technology to reach the vision of a 5G self-managing network. This new paradigm will significantly impact on connected vehicles, fostering a new wave of possibilities. This paper presents a preliminary approach towards Autonomic Network Management on a connected cars scenario. The focus is on the machine learning part, which will allow forecasting resource demand requirements, detecting errors, attacks and outlier events, and responding and taking corrective actions.

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Notes

  1. 1.

    http://www.openstack.org/.

  2. 2.

    https://github.com/nfvlabs/openmano.

  3. 3.

    http://openbaton.github.io/.

  4. 4.

    https://www.sdxcentral.com/listings/opnfv/.

  5. 5.

    https://www.opennetworking.org/sdn-resources/openflow.

  6. 6.

    https://github.com/etsy/statsd.

  7. 7.

    https://blueprints.launchpad.net/neutron/+spec/quantum-qos-api, https://blueprints.launchpad.net/neutron/+spec/ml2-qos.

  8. 8.

    http://osrg.github.io/ryu/.

  9. 9.

    https://wiki.opendaylight.org/.

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Acknowledgments

This work was fully supported by the EC project CogNet, 671625 (H2020-ICT-2014-2, Research and Innovation action).

Author information

Authors and Affiliations

  1. Vicomtech-IK4, Paseo Mikeletegi 57, 20009, San Sebastian, Spain

    Gorka Velez, Marco Quartulli, Angel Martin & Oihana Otaegui

  2. Cognitive Computing Group, Innovation Exchange, IBM Ireland, Shelbourne Road, Ballsbridge, Ireland

    Haytham Assem

Authors
  1. Gorka Velez
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  2. Marco Quartulli
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  3. Angel Martin
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  4. Oihana Otaegui
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  5. Haytham Assem
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Corresponding author

Correspondence to Gorka Velez .

Editor information

Editors and Affiliations

  1. Ceit and tecnun, University of Navarra, Donostia-San Sebastián, Spain

    Jaizki Mendizabal

  2. IFSTTAR, Villeneuve d`Ascq, France

    Marion Berbineau

  3. Halmstad University, Halmstad, Sweden

    Alexey Vinel

  4. Dtsch. Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen-Wessling, Germany

    Stephan Pfletschinger

  5. Mitsubishi Electric R&D Centre Europe, Rennes, France

    Hervé Bonneville

  6. Ecole Nationale de l'Aviation Civile, Toulouse, France

    Alain Pirovano

  7. Dtsch. Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen-Wessling, Bayern, Germany

    Simon Plass

  8. Istituto Superiore Mario Boella, Turin, Italy

    Riccardo Scopigno

  9. IFSTTAR, Versailles, France

    Hasnaa Aniss

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Velez, G., Quartulli, M., Martin, A., Otaegui, O., Assem, H. (2016). Machine Learning for Autonomic Network Management in a Connected Cars Scenario. In: Mendizabal, J., et al. Communication Technologies for Vehicles. Nets4Cars/Nets4Trains/Nets4Aircraft 2016. Lecture Notes in Computer Science(), vol 9669. Springer, Cham. https://doi.org/10.1007/978-3-319-38921-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-38921-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38920-2

  • Online ISBN: 978-3-319-38921-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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