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Efficient Online Data Reconstruction in Sensor Networks using k-NN Classifiers

This research paper presents a novel ensemble model for effective online reconstruction of missing data in sensor networks using k-nearest neighbors (k-NN) classifiers. The algorithm eliminates the need for a dedicated training phase, reducing computational complexity. Consistency is maintained through Bayes error minimization. The proposed methodology minimizes the theoretical gap and optimizes k selection based on network characteristics. The Distributed Change-Detection Test is employed to detect and validate data changes in stationary network conditions, showcasing improved performance and accuracy compared to existing approaches. Experimental results demonstrate successful mono and two-dimensional classification tasks using Gaussian and chi-square distributions. The theoretical derivation is empirically validated.

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Efficient Online Data Reconstruction in Sensor Networks using k-NN Classifiers

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  1. class class point Model ensemble for an effective on-line reconstruction of missing data in sensor networks Author 1, Author 2, Author 3 Affiliation k-NN classifiers associate a classification label to an input as the majority of its k nearest training samples No proper training phase – Reduced computational complexity Consistency: Bayes errore Sensing node 2 Sensing node 2 Sensing node 1 Sensing node 1 Sensing node 3 Sensing node 3 • Leave-One-Out (LOO) • Fukunaga et Al. • Lack of theoretical results Cluster head Cluster head How to select k given n? Sensing node 5 Sensing node 4 Sensing node 5 Sensing node 4 The proposed algorithm Network in stationary conditions . The Distributed Change-Detection Test: • Each unit: configure the ICI-based CDT using {, 1≤ i≤ N}; • Each unit: send feature extracted from to the cluster-head. • while(units acquire new observations at time T){ • Each unit: run the ICI-based CDT at time T; • let ST be the set of units where the ICI-based CDT detects a change at time T; • if (ST is not empty) { • Each unit in ST: run the refinement procedure, sent Tref,i to the cluster-head. • Cluster-head: compute Tref out of Tref,i, 1≤ i≤ N , send Tref to each unit. • Each unit: send to the cluster-head the values in [Tref ,T] of the feature detecting the change. • Cluster-head: run the Hotelling T2 test to assess stationarity of features • if (second-level test detects a change){ • Change is validated. • Each unit in ST the ICI-based CDT is re-trained on the new process status} • else{ • Change is discarded (false positive); • Each unit in ST: reconfigure the ICI-based CDT to improve its performance }}} Configuration H.T. Execution Experimental Results • a) a mono-dimensional classification problem with • equi-probable classes ruled by Gaussian distributions (with T=[-4,6]: , • b) a two-dimensional classification problem • characterized by the two equi-probable classes ruled by chi-square distributions: The theoretical derivation is experimentally sound Number of k within [Pe(ko),Pe(ko)+δ] ko w.r.t. n Pe(k) w.r.t. k Number of k within [Pe(ko),Pe(ko)+δ] ko w.r.t. n Pe(k) w.r.t. k

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