TRAWL: a traffic route adapted weighted learning algorithm

  • Authors:

  • Enda Fallon;Liam Murphy;John Murphy;Chi Ma

  • Affiliations:

  • School of Computer Science and Informatics, University College Dublin, Ireland and Software Research Institute, Athlone Institute of Technology, Athlone, Ireland;School of Computer Science and Informatics, University College Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Ireland;Software Research Institute, Athlone Institute of Technology, Athlone, Ireland

  • Venue:
  • WWIC'11 Proceedings of the 9th IFIP TC 6 international conference on Wired/wireless internet communications
  • Year:
  • 2011

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Abstract

Media Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms.