As portable computers and wireless networks are becoming ubiquitous, it is natural to consider the user’s position as yet another aspect to take into account when providing services that are tailored to meet the needs of the consumers. Location aware systems could guide persons through buildings, to a particular bookshelf in a library or assist in a vast variety of other applications that can benefit from knowing the user’s position. In indoor positioning systems, the most commonly used method for determining the location is to collect samples of the strength of the received signal from each base station that is audible at the client’s position and then pass the signal strength data on to a positioning server that has been previously fed with example signal strength data from a set of reference points where the position is known. From this set of reference points, the positioning server can interpolate the client’s current location by comparing the signal strength data it has collected with the signal strength data associated with every reference point. Our work proposes the use of multiple successive received signal strength samples in order to capture periodic signal strength variations that are the result of effects such as multi-path propagation, reflections and other types of radio interference. We believe that, by capturing these variations, it is possible to more easily identify a particular point; this is due to the fact that the signal strength fluctuations should be rather constant at every position, since they are the result of for example reflections on the fixed surfaces of the building’s interior. For the purpose of investigating our assumptions, we conducted measurements at a site at Växjö university, where we collected signal strength samples at known points. With the data collected, we performed two different experiments: one with a neural network and one where the k-nearest-neighbor method was used for position approximation. For each of the methods, we performed the same set of tests with single signal strength samples and with multiple successive signal strength samples, to evaluate their respective performances. We concluded that the k-nearest-neighbor method does not seem to benefit from multiple successive signal strength samples, at least not in our setup, compared to when using single signal strength samples. However, the neural network performed about 17% better when multiple successive signal strength samples were used.

<p>As portable computers and wireless networks are becoming ubiquitous, it is natural to consider the user’s position as yet another aspect to take into account when providing services that are tailored to meet the needs of the consumers. Location aware systems could guide persons through buildings, to a particular bookshelf in a library or assist in a vast variety of other applications that can benefit from knowing the user’s position.</p><p>In indoor positioning systems, the most commonly used method for determining the location is to collect samples of the strength of the received signal from each base station that is audible at the client’s position and then pass the signal strength data on to a positioning server that has been previously fed with example signal strength data from a set of reference points where the position is known. From this set of reference points, the positioning server can interpolate the client’s current location by comparing the signal strength data it has collected with the signal strength data associated with every reference point.</p><p>Our work proposes the use of multiple successive received signal strength samples in order to capture periodic signal strength variations that are the result of effects such as multi-path propagation, reflections and other types of radio interference. We believe that, by capturing these variations, it is possible to more easily identify a particular point; this is due to the fact that the signal strength fluctuations should be rather constant at every position, since they are the result of for example reflections on the fixed surfaces of the building’s interior.</p><p>For the purpose of investigating our assumptions, we conducted measurements at a site at Växjö university, where we collected signal strength samples at known points. With the data collected, we performed two different experiments: one with a neural network and one where the k-nearest-neighbor method was used for position approximation. For each of the methods, we performed the same set of tests with single signal strength samples and with multiple successive signal strength samples, to evaluate their respective performances.</p><p>We concluded that the k-nearest-neighbor method does not seem to benefit from multiple successive signal strength samples, at least not in our setup, compared to when using single signal strength samples. However, the neural network performed about 17% better when multiple successive signal strength samples were used.</p>

The main objective of this thesis is to propose a UAV (also called as drones) location estimation system based on LoRaWAN using received signal strength indicator in a GPS denied environment. The drones are finding new applications in areas such as surveillance, search, rescue missions, package delivery, and precision agriculture. Nearly all applications require the localization of UAV during flight. Localization is the method of determining a UAVs physical position using a real or virtual coordinate system. This thesis proposes a LoRaWAN-based UAV location method and presents experimental findings from a prototype. The thesis mainly consists of two different sections: one is the distance estimation and the other is the location estimation. First, the distance is estimated based on the mean RSSI values which are recorded at the ground stations using the path loss model. Later using the slant distance estimation technique, the path loss model parameters L and C are estimated whose values are unknown at the beginning. These values completely depend on the environment. Finally, the trilateration system architecture is employed to find the 3-D location of the UAV.

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Submitted by Geyciane Santos (geyciane_thamires@hotmail.com) on 2015-11-23T21:24:44Z No. of bitstreams: 1 Dissertação - Walfredo da Costa Lucena Filho.pdf: 2083187 bytes, checksum: 72f63311dba60bbea7ef2d5cc474c601 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-30T19:51:08Z (GMT) No. of bitstreams: 1 Dissertação - Walfredo da Costa Lucena Filho.pdf: 2083187 bytes, checksum: 72f63311dba60bbea7ef2d5cc474c601 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-30T19:55:39Z (GMT) No. of bitstreams: 1 Dissertação - Walfredo da Costa Lucena Filho.pdf: 2083187 bytes, checksum: 72f63311dba60bbea7ef2d5cc474c601 (MD5) / Made available in DSpace on 2015-11-30T19:55:40Z (GMT). No. of bitstreams: 1 Dissertação - Walfredo da Costa Lucena Filho.pdf: 2083187 bytes, checksum: 72f63311dba60bbea7ef2d5cc474c601 (MD5) Previous issue date: 2015-08-03 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / An RFID system is typically composed of a reader and a set of tags. An anti-collision algorithm is necessary to avoid collision between tags that respond simultaneously to a reader. The most widely used anti-collision algorithm is DFSA (Dynamic Framed Slotted ALOHA) due to its simplicity and low computational cost. In DFSA algorithms, the optimal TDMA (Time Division Multiple Access) frame size must be equal to the number of unread tags. If the exact number of tags is unknown, the DFSA algorithm needs a tag estimator to get closer to the optimal performance. Currently, applications have required the identification of large numbers of tags, which causes an increase in collisions and hence the degradation in performance of the traditional algorithms DFSA. This work proposes a power control mechanism to estimate the number of tags for radio frequency identification networks (RFID). The mechanism divides the interrogation zone into subgroups of tags and then RSSI (Received Signal Strength Indicator) measurements estimate the number of tags in a subarea. The mechanism is simulated and evaluated using a simulator developed in C/C++ language. In this study, we compare the number of slots and identification time, with ideal DFSA algorithm and Q algorithm EPCglobal standard. Simulation results shows the proposed mechanism provides 99% performance of ideal DFSA in dense networks, where there are many tags. Regarding the Q algorithm, we can see the improvement in performance of 6.5%. It is also important to highlight the lower energy consumption of the reader comparing to ideal DFSA is 63%. / Um sistema de identificação por rádio frequência (RFID) é composto basicamente de um leitor e etiquetas. Para que o processo de identificação das etiquetas seja bem sucedido, é necessário um algoritmo anticolisão a fim de evitar colisões entre etiquetas que respondem simultaneamente à interrogação do leitor. O algoritmo anticolisão mais usado é o DFSA (Dynamic Framed Slotted ALOHA) devido à sua simplicidade e baixo custo computacional. Em algoritmos probabilísticos, tal como o DFSA, o tamanho ótimo do quadro TDMA (Time Division Multiple Access) utilizado para leitura das etiquetas deve ser igual à quantidade de etiquetas não lidas. Uma vez que no processo de leitura, normalmente não se sabe a quantidade exata de etiquetas, o algoritmo DFSA faz uso de um estimador para obter um desempenho mais próximo do ideal. Atualmente, as aplicações têm demandado a identificação de grandes quantidades de etiquetas, o que ocasiona um aumento das colisões e, consequentemente, a degradação no desempenho dos algoritmos DFSA tradicionais. Este trabalho propõe um mecanismo de controle de potência para estimar a quantidade de etiquetas em redes de identificação por rádio frequência (RFID). O mecanismo baseia-se na divisão da área de interrogação em subáreas e, consequentemente, subgrupos de etiquetas. Tal divisão é utilizada para realizar medições de RSSI (Received Signal Strength Indicator) e, assim, estimar a quantidade de etiquetas por subárea. O mecanismo é simulado e avaliado utilizando um simulador próprio desenvolvido em linguagem C/C++. Neste estudo, comparam-se os resultados de quantidade de slots e tempo de identificação das etiquetas, com os obtidos a partir da utilização dos algoritmos DFSA ideal e algoritmo padrão Q da norma EPCglobal. A partir dos resultados da simulação, é possível perceber que o mecanismo proposto apresenta desempenho 99% do DFSA ideal em redes densas, onde há grande quantidade de etiquetas. Em relação ao algoritmo Q, percebe-se a melhoria de 6,5% no desempenho. É importante ressaltar também a redução no consumo de energia do leitor em torno de 63% em relação ao DFSA ideal.

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.