Co-operative Extended Kohonen Mapping (EKM) for Wireless Sensor Networks

Springer Berlin Heidelberg
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Conference Proceeding
Computer Aided Systems Theory, Eurocast 2009, 2009, pp. 897 - 904
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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 theWSNs external factors, to administer theWSNs 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 WSNs clustering and routing procedures are enhanced by the co-operation of multiple self-organising EKMs to adapt to actively changing conditions in the environment.
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