Name
Abstract | ||
|---|---|---|
Backbone formation has been used extensively in various aspects for wireless sensor networks recently. Many methods are mostly designed to minimize the size of the backbone or find small cost path. This paper propose the Artificial Neural Networks Routing (ANNR) algorithm which use self-organizing map to measure the Quality of Service supported by the networks. A method of offline process and online process is discussed to deal with constrains on sensor data computing and power consumption. Experimental results have confirmed the algorithm feasibility and the validity. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.55 | ICNC (3) |
Keywords | Field | DocType |
power consumption,online process,small cost path,quality of service,wireless sensor network,sensor data computing,self-organizing map,artificial neural networks routing,telecommunication computing,artificial neural networks routing algorithm,annr algorithm,algorithm feasibility,backbone formation,wireless sensor networks,telecommunication network routing,self-organising feature maps,offline process,base stations,noise,self organizing map,artificial neural networks,artificial neural network | Base station,Computer science,Quality of service,Computer network,Self-organizing map,Artificial intelligence,Artificial neural network,Distributed computing,Power consumption,Key distribution in wireless sensor networks,Backbone network,Wireless sensor network,Machine learning | Conference |
Volume | ISBN | Citations |
3 | 978-0-7695-3736-8 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Ji | 1 | 36 | 7.10 |
| Shenfang Yuan | 2 | 76 | 12.49 |
| Meng-meng Cui | 3 | 0 | 0.68 |