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This paper discusses a methodology to manage wireless sensor networks (WSN) with self-organising feature maps, using cooperative 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 the WSN's external factors, to administer the WSN'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 cooperation of multiple self-organising EKMs to adapt to actively changing conditions in the environment.

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Traditionally, Information Fusion systems assume that the information is gathered from known sensors over proprietary communication networks and fuse using fixed rules of information fusion and designated computing and communication resources. Emerging technologies like wireless sensor networks, TEDS enabled legacy sensors, ubiquitous computing devices and all IP next generation networks are challenging the rationale of conventional information fusion systems. The technology has matured to a point where it is reasonable to discover sensors based on the context, establish relevance, query for appropriate data, and fuse it using the most appropriate fusion rule, using ubiquitous computing and communication environment in an opportunistic manner. We define such fusion systems as opportunistic information fusion systems. In this paper we introduce this new paradigm for information fusion and identify plausible approaches and challenges to design, develop and deploy the proposed next generation opportunistic information fusion systems.

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