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Published byCiera Coney Modified about 1 year ago

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Data mining in wireless sensor networks based on artificial neural-networks algorithms Authors: Andrea Kulakov and Danco Davcev Presentation by: Niyati Parikh

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Motivation Centralized data clustering in sensor networks is difficult, not scalable, limited communication bandwidth, limited power supply, data redundancy Advantage of Neural Networks: demand of compressed summaries of large spatio-temporal data, similarity queries – finding similar patterns or detecting correlations Unsupervised learning ANN perform dimensionality reduction or pattern clustering

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Adaptive Resonance Theory(ART1) F0 F1 F2 Input layer Comparison layer Category layer p - reset Orienting subsystem Binary input Attentional subsystem +

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Adaptive Resonance Theory(ART1) F0 F1 F2 Input layer Comparison layer Category layer p - reset Orienting subsystem Binary input Attentional subsystem T i = | w i. x | B + | w i | +

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Adaptive Resonance Theory(ART1) F0 F1 F2 Input layer Comparison layer Category layer p + - reset Orienting subsystem Binary input Attentional subsystem T i = | w i. x | B + | w i | | w i. x | | x |

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Adaptive Resonance Theory(ART1) F0 F1 F2 Input layer Comparison layer Category layer p + - reset Orienting subsystem Binary input Attentional subsystem T i = | w i. x | B + | w i | | w i. x | | x | W i new =

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ART1 Continue finding an F2 node until prototype matches the input well enough or else allocate a new F2 node Capable of refining learned categories and finding new patterns Value of p: higher the vigilance level, more specific clusters

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FuzzyART Same as ART1, but replace intersection operator of ART1 with fuzzy set theory conjunction MIN operator ^ ART1 and FuzzyART use complement coding – concatenate input pattern b with b’ or b i with (1-b i ) Look at the features consistently present or absent from a pattern

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Proposed architectures of sensor networks Clusterhead collecting all sensor data from its cluster of units

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One clusterhead collecting and classifying the data after they are once classified at the lower level

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Results p # categories

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Comparison Tested data robustness – made one sensor defective Architecture1: trained with p=0.93 and tested with p = 0.90 Architecture2: trained with p=0.80 and tested with p = 0.70 Architecture2 makes 0.75% classification error

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Future work Applying ARTMAP and FuzzyARTMAP - supervised learning versions

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