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Méthodes de localisation de capteurs dans le contexte de l'Internet des Objets

Abstract : With the growing emergence of the Internet of Things and the importance of position information in this context, localization is attracting more and more attention in the researchers' community. The outdoor location is provided by GPS which is not suitable for indoors environments. Several indoor localization techniques exist, but there is not yet a standard.Existing methods are mainly based on trilateration or fingerprinting. Trilateration is a geometric method that exploits thedistances between an object and reference points to locate it. This method only works when we have at least 3 access points detected and is strongly affected by multi paths. In order to overcome these disadvantages, the fingerprinting methodcompares the fingerprint associated to the object to be located to a fingerprints' database constructed on offline. The estimated position is a combination of the selected training positions. This method is of great interest. However, it requiressignificant computing and storage capabilities. The aim of this thesis is to improve the existing localization techniqueswhile maintaining a satisfying localization accuracy with low computational complexity. In order to overcome the disadvantages of these two classes of localization techniques, we propose alternative approaches. For trilateration, it hasbeen combined with an optimization process that aims at completing the inter-node distance matrix from partially knowndata. Advanced optimization algorithms have been used in developing the mathematical equation corresponding to eachone. Using this method, we came up with a localization solution for a distributed IoT architecture. As for fingerprinting, we have exploited it to develop localization systems for a centralized IoT architecture. A comparative study between different metrics of similarity evaluation is conducted. This study was followed by the development of a linear model generating a mathematical relation that links the powers of the signal received by an object to its coordinates. This helps to reduce the online complexity of and adapts our system to real time. This is also ensured by the development of a CNN model which deal with the localization problem as radio images classification problem. The performances of all proposed approaches are evaluated and discussed. These results show the improvement of the performances of basic techniques in terms of localization accuracy and complexity.
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Wafa Njima. Méthodes de localisation de capteurs dans le contexte de l'Internet des Objets. Traitement des images [eess.IV]. Conservatoire national des arts et metiers - CNAM; École supérieure des communications de Tunis (Tunisie), 2019. Français. ⟨NNT : 2019CNAM1264⟩. ⟨tel-02484757⟩

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