Name
Abstract | ||
|---|---|---|
One of the key features of the media independent handover (MIH) framework, introduced by the IEEE 802.21 standard, is the support for events, including network degradation events which can be triggered based on link layer metrics and propagated to upper layer mobility protocols. As a framework, MIH does not provide specifics on how these events are triggered. Typically events are triggered when performance parameters such as received signal strength and link loss rate exceed a predefined threshold. In this paper we suggest that for vehicular systems, the constrained nature of movement enables network performance prediction. We propose to capture this performance predictability through a fixed route adapted media-streaming enhanced handover algorithm (FRAME). FRAME uses a directed feed forward neural network to trigger MIH link events. FRAME provides a pluggable learning mechanism which allows for the extensible definition of performance and learning metrics. FRAME is evaluated using a commercial metropolitan network implementation. Results show that FRAME has significant performance improvements over existing MIH link triggering mechanisms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TBC.2012.2219232 | Broadcasting, IEEE Transactions |
Keywords | DocType | Volume |
Measurement,Artificial neural networks,Media,Protocols,Neurons | Journal | 59 |
Issue | ISSN | Citations |
1 | 0018-9316 | 3 |
PageRank | References | Authors |
0.42 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enda Fallon | 1 | 112 | 14.90 |
| Liam Murphy | 2 | 811 | 74.94 |
| John Murphy | 3 | 597 | 52.43 |
| Gabriel-Miro Muntean | 4 | 1880 | 143.82 |