Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. The features of the proposed approach are illustrated through the description of a sample application for wildfire detection. © 2007 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gaglio, S., Gatani, L., Re, G., & Ortolani, M. (2007). Knowledge extraction from environmental data through a cognitive architecture. In Advances in Soft Computing (Vol. 44, pp. 329–336). https://doi.org/10.1007/978-3-540-74972-1_43
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