High Mobility in a Realistic Wireless Environment: a Mobile IP Handoff Model for NS-2

Palazzi, Claudio E.; Chin, Brian; Ray, Phyllis; Pau, Giovanni; Gerla, Mário; Roccetti, Marco

doi:10.1109/tridentcom.2007.4444729

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…The MIH parameters CSThresh (link detection) and RXThresh (link utilisation) were set to -90dBm and -85dBm respectively. Simulation enhancements as described in [26] were included in the model. The UMTS core network was configured with a 622Mbit link capacity and a delay of 15ms.…”

Section: Simulated Evaluation Of the Trawl Algorithmmentioning

confidence: 99%

TRAWL – A Traffic Route Adapted Weighted Learning Algorithm

Fallon

Murphy

et al. 2011

Lecture Notes in Computer Science

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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.

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Section: Simulated Evaluation Of the Trawl Algorithmmentioning

confidence: 99%