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Machine Learning for Autonomic Network Management in a Connected Cars Scenario

  • Gorka VelezEmail author
  • Marco Quartulli
  • Angel Martin
  • Oihana Otaegui
  • Haytham Assem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9669)

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.

Keywords

5G Connected cars Machine learning Network management 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gorka Velez
    • 1
    Email author
  • Marco Quartulli
    • 1
  • Angel Martin
    • 1
  • Oihana Otaegui
    • 1
  • Haytham Assem
    • 2
  1. 1.Vicomtech-IK4San SebastianSpain
  2. 2.Cognitive Computing Group, Innovation ExchangeIBM IrelandBallsbridgeIreland

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