A Weighted Kernel-based Hierarchical Classification Method for Zoning of Sensors in Indoor Wireless Networks

Daniel Alshamaa (1) , Farah Mourad-Chehade (1) , Paul Honeine (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

This paper presents a solution for localization of sensors by zoning, in indoor wireless networks. The problem is tackled by a classification technique, where the objective is to classify the zone of the mobile sensor for any observation. The method is hierarchical and uses the belief functions theory to assign confidence levels for zones. For this purpose, kernel density estimation is used first to model the features observations. The algorithm then uses hierarchical clustering and similarity divergence, creating a two-level hierarchy, to reduce the number of zones to be classified at a time. At each level of the hierarchy, a feature selection technique is carried to optimize the misclassification rate and feature redundancy. Experiments are realized in a wireless sensor network to evaluate the performance of the proposed method.

Mots clés: Kernel; Sensors; Wireless communication; Estimation; Feature extraction; Clustering algorithms; Belief functions; classification; feature selection; hierarchical clustering; kernel density estimation; zoning; machine learning; wireless sensor networks

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Daniel Alshamaa, Farah Mourad-Chehade, Paul Honeine. A Weighted Kernel-based Hierarchical Classification Method for Zoning of Sensors in Indoor Wireless Networks. Proc. 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2018, Kalamata, Greece. ⟨10.1109/SPAWC.2018.8445918⟩. ⟨hal-01965905⟩