Abstract
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.
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Gaglio, S., Gatani, L., Lo Re, G., Ortolani, M. (2007). Knowledge Extraction from Environmental Data Through a Cognitive Architecture. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_43
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DOI: https://doi.org/10.1007/978-3-540-74972-1_43
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