. .. Introduction, 105 6.2 Adaptation de l'approche aux environnements professionnels106 6.2.1 Spécificités de l'environnement

. .. , 109 6.3.2 Pré-traitement : suppression du bruit et passage à l'échelle . 111 6.3.3 Définition de l'erreur de reconstruction pour la détection, p.112

. .. ,

. .. Diagnostic-spatial, 117 6.5.1 État de l'art sur la localisation

.. .. Conclusion,

, Ce chapitre présente les éléments qui constituent la seconde proposition d'application de notre solution générique à un environnement spécifique, les environnements professionnels. Dans un premier temps, nous détaillons les spécificités et les besoins de ce contexte

, 2.1 Composition de l'environnement et

. .. Paramétrage-des-modèles, 3.2 Estimation de l'erreur de reconstruction

. .. Protocole-expérimental, 125 7.4.1 Évaluation de la détection d'anomalies

. .. Résultats-de-l'évaluation, 129 7.6.1 Résultats de la détection d'anomalies

. .. Discussions, 134 7.7.1 Limites globales de l'approche

.. .. Conclusion,

I. .. Synthèse-de-la-partie, , p.140

, Ce chapitre décrit les expérimentations réalisées pour évaluer le déploiement et l'implémentation de notre solution dans un contexte professionnel

, Dans la section 6.5.4, il a été défini que nous estimions la position de l'émetteur à l'aide de l'algorithme k-nn. Cependant, après plusieurs itérations sur les méthodes de localisation spatiale employées dans la littérature, nous avons pu améliorer nos résultats en utilisant l'algorithme du centroïde pondéré (Weighted Centroid Algorithm), 2007.

, de la sonde p et de la puissance médiane d'une anomalie MedianPower(p, t s , t e ,f ) mesurée par la sonde p, la position (x a , y a ) de l'émetteur d'une anomalie est estimée par, À partir de la position

. Medianpower, f ) ? s min ? d

, Où d représente la distance Euclidienne, s min représente la puissance seuil de détection et ? est un paramètre libre

, La puissance seuil de détection est établie à s min = ?65 dBm

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