Co-operative Extended Kohonen Mapping (EKM) for wireless sensor networks

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5717 LNCS pp. 897 - 904
Issue Date:
2009-12-01
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This paper discusses a methodology to manage wireless sensor networks (WSN) with self-organising feature maps, using co-operative Extended Kohonen Maps (EKMs). EKMs have been successfully demonstrated in other machine-learning contexts such as learning sensori-motor control and feedback tasks. Through a quantitative analysis of the algorithmic process, an indirect-mapping EKM can self-organise from a given input space, such as theWSN's external factors, to administer theWSN's routing and clustering functions with a control parameter space. Preliminary results demonstrate indirect mapping with EKMs provide an economical control and feedback mechanism by operating in a continuous sensory control space when compared with direct mapping techniques. By training the control parameter, a faster convergence is made with processes such as the recursive least squares method. The management of a WSN's clustering and routing procedures are enhanced by the co-operation of multiple self-organising EKMs to adapt to actively changing conditions in the environment. © 2009 Springer-Verlag Berlin Heidelberg.
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