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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 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 Adaptive Resonance Theory(ART1) Attentional subsystem Category layer F2 reset Comparison layer F1 - p + Input layer F0 Binary input Orienting subsystem Adaptive Resonance Theory(ART1) Attentional subsystem Category layer F2 Ti = | wi . x | reset B + | wi | Comparison layer F1 - p + Input layer F0 Binary input Orienting subsystem Adaptive Resonance Theory(ART1) Attentional subsystem Category layer F2 Ti = | wi . x | reset B + | wi | Comparison layer F1 - p + Input layer F0 Binary input Orienting subsystem | wi . x | |x| Adaptive Resonance Theory(ART1) Attentional subsystem Category layer Ti = | wi . x | F2 Winew = reset B + | wi | Comparison layer F1 - p + Input layer F0 Binary input Orienting subsystem | wi . x | |x| 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 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 bi with (1-bi) • Look at the features consistently present or absent from a pattern Proposed architectures of sensor networks Clusterhead collecting all sensor data from its cluster of units One clusterhead collecting and classifying the data after they are once classified at the lower level Results p 0.70 # categories 2 0.85 0.9 0.93 0.95 0.97 0.98 0.99 3 8 19 36 87 151 370 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 Future work • Applying ARTMAP and FuzzyARTMAP - supervised learning versions
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