Strategies for principal component analysis in wireless sensor networks

Nisrine Ghadban (1, 2) , Paul Honeine (2) , Clovis Francis (1) , Farah Mourad-Chehade (2) , Joumana Farah (3)

1 LU / ULB - الجامعة اللبنانية [بيروت] = Lebanese University [Beirut] = Université libanaise [Beyrouth]
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
3 Holy-Spirit University of Kaslik [Jounieh]

This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.

Mots clés: 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]

Nisrine Ghadban, Paul Honeine, Clovis Francis, Farah Mourad-Chehade, Joumana Farah. Strategies for principal component analysis in wireless sensor networks. 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), Jun 2014, A Coruna, Spain. pp.233-236, ⟨10.1109/SAM.2014.6882383⟩. ⟨hal-01965990⟩