This study is devoted to investigating and developing WSN based localisation approaches with high position accuracies indoors. The study initially summarises the design and implementation of localisation systems and WSN architecture together with the characteristics of LQI and RSSI values. A fingerprint localisation approach is utilised for indoor positioning applications. A k-nearest neighbourhood algorithm (k-NN) is deployed, using Euclidean distances between the fingerprint database and the object fingerprints, to estimate unknown object positions. Weighted LQI and RSSI values are calculated and the k-NN algorithm with different weights is utilised to improve the position detection accuracy. Different weight functions are investigated with the fingerprint localisation technique. A novel weight function which produced the maximum position accuracy is determined and employed in calculations. The study covered designing and developing the centroid localisation (CL) and weighted centroid localisation (WCL) approaches by using LQI values. A reference node localisation approach is proposed. A star topology of reference nodes are to be utilized and a 3-NN algorithm is employed to determine the nearest reference nodes to the object location. The closest reference nodes are employed to each nearest reference nodes and the object locations are calculated by using the differences between the closest and nearest reference nodes. A neighbourhood weighted localisation approach is proposed between the nearest reference nodes in star topology. Weights between nearest reference nodes are calculated by using Euclidean and physical distances. The physical distances between the object and the nearest reference nodes are calculated and the trigonometric techniques are employed to derive the object coordinates. An environmentally adaptive centroid localisation approach is proposed. Weighted standard deviation (STD) techniques are employed adaptively to estimate the unknown object positions. WSNs with minimum RSSI mean values are considered as reference nodes across the sensing area. The object localisation is carried out in two phases with respect to these reference nodes. Calculated object coordinates are later translated into the universal coordinate system to determine the actual object coordinates. Virtual fingerprint localisation technique is introduced to determine the object locations by using virtual fingerprint database. A physical fingerprint database is organised in the form of virtual database by using LQI distribution functions. Virtual database elements are generated among the physical database elements with linear and exponential distribution functions between the fingerprint points. Localisation procedures are repeated with virtual database and localisation accuracies are improved compared to the basic fingerprint approach. In order to reduce the computation time and effort, segmentation of the sensing area is introduced. Static and dynamic segmentation techniques are deployed. Segments are defined by RSS ranges and the unknown object is localised in one of these segments. Fingerprint techniques are applied only in the relevant segment to find the object location. Finally, graphical user interfaces (GUI) are utilised with application program interfaces (API), in all calculations to visualise unknown object locations indoors.

L'objectif de la thèse est d'étudier la variation temporelle de la qualité des liens dans les réseaux de capteurs sans fil à grande échelle, de concevoir des estimateurs permettant la différenciation, à court terme et long terme, entre liens de qualité hétérogène. Tout d'abord, nous étudions les caractéristiques de deux paramètres de la couche physique: RSSI (l'indicateur de puissance du signal reçu) et LQI (l'indicateur de la qualité de liaison) sur SensLab, une plateforme expérimentale de réseau de capteurs à grande échelle situé à l'intérieur de bâtiments. Nous observons que le RSSI et le LQI permettent de discriminer des liens de différentes qualités. Ensuite, pour obtenir un estimateur de PRR, nous avons approximé le diagramme de dispersion de la moyenne et de l'écart-type du LQI et RSSI par une fonction Fermi-Dirac. La fonction nous permet de trouver le PRR à partir d'un niveau donné de LQI. Nous avons évalué l'estimateur en calculant le PRR sur des fenêtres de tailles variables et en le comparant aux valeurs obtenues avec l'estimateur. Par ailleurs, nous montrons en utilisant le modèle de Gilbert-Elliot (chaîne de Markov à deux états) que la corrélation des pertes de paquets dépend de la catégorie de lien. Le modèle permet de distinguer avec précision les différentes qualités des liens, en se basant sur les probabilités de transition dérivées de la moyenne et de l'écart-type du LQI. Enfin, nous proposons un modèle de routage basé sur la qualité de lien déduite de la fonction de Fermi-Dirac approximant le PRR et du modèle Markov Gilbert-Elliot à deux états. Notre modèle est capable de distinguer avec précision les différentes catégories de liens ainsi que les liens fortement variables. / The goal of the thesis is to investigate the issues related to the temporal link quality variation in large scale WSN environments, to design energy efficient link quality estimators able to distinguish among links with different quality on a short and a long term. First, we investigate the characteristics of two physical layer metrics: RSSI (Received Signal Strength Indication) and LQI (Link Quality Indication) on SensLAB, an indoor large scale wireless sensor network testbed. We observe that RSSI and LQI have distinct values that can discriminate the quality of links. Second, to obtain an estimator of PRR, we have fitted a Fermi-Dirac function to the scatter diagram of the average and standard variation of LQI and RSSI. The function enables us to find PRR for a given level of LQI. We evaluate the estimator by computing PRR over a varying size window of transmissions and comparing with the estimator. Furthermore, we show using the Gilbert-Elliot two-state Markov model that the correlation of packet losses and successful receptions depend on the link category. The model allows to accurately distinguish among strongly varying intermediate links based on transition probabilities derived from the average and the standard variation of LQI. Finally, we propose a link quality routing model driven from the F-D fitting functions and the Markov model able to discriminate accurately link categories as well as high variable links.