Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21560-5_1 | WWIC |
Keywords | Field | DocType |
performance predictability,network performance prediction,neural network,dynamic performance characteristic,traffic route,mih link,significant performance improvement,predictive network degradation event,predictable network behavior,heterogeneous network,commercial metropolitan heterogeneous network,handover,neural networks | Feedforward neural network,Computer science,Media-independent handover,Computer network,Algorithm,Real-time computing,Link layer,Heterogeneous network,Backpropagation,Artificial neural network,Handover,Network performance | Conference |
Volume | ISSN | Citations |
6649 | 0302-9743 | 2 |
PageRank | References | Authors |
0.41 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enda Fallon | 1 | 112 | 14.90 |
| Liam Murphy | 2 | 811 | 74.94 |
| John Murphy | 3 | 597 | 52.43 |
| Chi Ma | 4 | 2 | 0.41 |