Traditional methods for locating books and resources in libraries often entail browsing catalogsor manual searching that are time-consuming and inefficient. This thesis investigates thepotential of automated digital services to streamline this process, by utilizing Wi-Fi signal datafor precise indoor localization. Central to this study is the development of a model that employsWi-Fi signal strength (RSSI) and round-trip time (RTT) to estimate the locations of library userswith arm-length accuracy. This thesis aims to enhance the accuracy of location estimation byexploring the complex, nonlinear relationship between Received Signal Strength Indicator(RSSI) and Round-Trip Time (RTT) within signal fingerprints. The model was developed usingan artificial neural network (ANN) to capture the relationship between RSSI and RTT. Besides,this thesis introduces and evaluates the performance of a novel variant of the Particle SwarmOptimization (PSO) algorithm, named Randomized Particle Swarm Optimization (RPSO). Byincorporating randomness into the conventional PSO framework, the RPSO algorithm aims toaddress the limitations of the standard PSO, potentially offering more accurate and reliablelocation estimations. The PSO algorithms, including RPSO, were integrated into the trainingprocess of ANN to optimize the network’s weights and biases through direct optimization, aswell as to enhance the hyperparameters of the ANN’s built-in optimizer. The findings suggestthat optimizing the hyperparameters yields better results than direct optimization of weights andbiases. However, RPSO did not significantly enhance the performance compared to thestandard PSO in this context, indicating the need for further investigation into its application andpotential benefits in complex optimization scenarios.

近年來穿戴相關發展越來越蓬勃，特別是在虛擬-實境的綜合藝術表 演中，例如: 電影「猩球崛起」。然而大部分虛擬實境的綜合內容是 基於腳本預先錄製好的，而且演員需要大量的練習，使表演能夠完美 演出。此外，如果我們想要在兩人的相對方向定位上有特殊效果，那 麼預先錄製的的方法是不合適的。解決這個問題的一個方法是，使用 高品質的相機偵測身體的姿勢或位置。但是精准度常會受限於光線或 是障礙物。 本篇論文中，我們提出一個即時相對方向定位方法，這方法使用無線 可穿戴式設備解決這個問題。我們結合BLE 所發送的Received Signal Strength Indicator (RSSI)與IMU 感測器資訊，來追蹤兩個表演者的相對方向定位的位置。但是RSSI 資料有波動與不穩定性、IMU 會引起 累積的誤差。我們發明了「可靠程度」的RSSI 量測概念，並且把這 概念運用在IMU 定期校正上。我們實驗的情況是，兩個人的舞蹈來 驗證準確性，結果是令人滿意的。我們還使用Unity 來實踐人體骨架， 以便與兩個舞者動作做比較。在未來，我們開發的方案可以用於藝術 表演，使內容更豐富，更具互動性。 / In recent years, wearable-related applications are flourishing, especially in virtual-real integrated art performance, such as “Rise of the Planet of the Apes”. However, most of the virtual-real integrated contents are pre-recorded based on the script, and the performer needs a lot of practice to make the integration perfect. Moreover, if we want to make special effect based on the relative directional positions of two performers, the pre-recorded approach is not suitable. One way to tackle this problem is to use the high-quality camera to detect the body posture or position.But the accuracy is usually limited in light intensity or obstacles. In this thesis, we propose a real-time relative directional positioning approach using wireless wearable devices to solve this problem. We use Received Signal Strength Indicator (RSSI) of BLE, combined with IMU sensors to track two performers’ relative directional positions. The RSSI fluctuates and the IMU causes accumulated errors. We invent the concept of “reliable level” of RSSI measures to periodically correct the IMU errors. We experiment the scenario of two-person dance to validate the accuracy, and the result is satisfactory. We also use Unity to real-time render the human skeleton for comparison with the two dancers’ motion.In the future, our developed scheme can be used in the art performance to make the content richer and more interactive.

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.

Being able to keep track of ones assets is a very useful thing, from avoiding losing ones keys or phone to being able to find the needed equipment in a busy hospital or on a construction site. The area of localization is actively evolving to find the best ways to accurately track objects and devices in an energy efficient manner, at any range, and in any type of environment. This thesis focuses on the last aspect of maintaining accurate localization regardless of environment. For radio frequency based systems, challenging environments containing many obstacles, e.g., indoor or urban areas, have a detrimental effect on the measurements used for positioning, making them deceptive. In this work, a method for correcting range measurements is proposed for a narrowband sub-GHz radio frequency based localization system using Received Signal Strength Indicator (RSSI) and Time-of-Flight (ToF) measurements for positioning. Three different machine learning models were implemented: a linear regressor, a least squares support vector machine regressor and a gaussian process regressor. They were compared in their ability to predict the true range between devices based on raw range measurements. Achieved was a 69.96 % increase in accuracy compared to uncorrected ToF estimates and a 88.74 % increase in accuracy compared to RSSI estimates. When the corrected range estimates were used for positioning with a trilateration algorithm using least squares estimation, a 67.84 % increase in accuracy was attained compared to positioning with uncorrected range estimates. This shows that this is an effective method of improving range estimates to facilitate more accurate positioning. / Att kunna hålla reda på var ens tillgångar befinner sig kan vara mycket användbart, från att undvika att ens nycklar eller telefon tappas bort till att kunna hitta utrustningen man behöver i ett myllrande sjukhus eller på en byggarbetsplats. Området av lokalisering utvecklas aktivt för att hitta de bästa metoderna och teknologierna för att med precision kunna spåra fysiska objekt på ett energieffektivt sätt, på vilken räckvidd som helst, och i vilken miljö som helst. Detta arbete fokuserar på den sista aspekten av att uppnå precis positionering oavsett miljö. För radiofrekvensbaserade system har utmanande miljöer med många fysiska hinder som till exempel inomhus och stadsområden en negativ effekt på de mätningar som används för positionering, vilket gör dem vilseledande. I detta arbete föreslås en metod för att korrigera avståndsmätningar i ett narrowband sub-GHz radiofrekvensbaserat lokaliseringssystem som använder Received Signal Strength Indicator (RSSI)- och Time-of-Flight (ToF)-mätningar för positionering. Tre olika maskininlärningsmodeller har implementerats: en linear regressor, en least squares support vector machine regressor och en gaussian process regressor. Dessa jämfördes i sin förmåga att förutspå det sanna avståndet mellan enheter baserat på råa avståndsmätningar. De korrigerade avståndsmätningarna uppnådde 69.96 % högre nogrannhet jämfört med okorrigerade ToF-uppskattningar och 88.74 % högre nogrannhet jämfört med RSSI-uppskattningar. Avståndsuppskattningarna användes för positionering med trilateration och minsta kvadratmetoden. De korrigerade uppskattningarna gav 67.84 % mer precis positionering jämfört med de okorrigerde uppskattningarna. Detta visar att detta är en effektiv metod förbättra avståndsuppskattningarna för att i sin tur bidra till mer exakt positionering.

In this thesis the novel concept of Sycophant Wireless Sensors (SWS) is introduced. A SWS network is a static ectoparasitic clandestine sensor network mounted incognito on a mobile agent using only the agent&rsquo / s mobility without intervention. SWS networks not only communicate with each other through mobileWireless Sensor Networks (WSN) but also cooperate with them to form a global hybrid Wireless Sensor Network. Such a hybrid network has its own problems and opportunities, some of which have been studied in this thesis work. Assuming that direct position measurements are not always feasible tracking performance of the sycophant using range only measurements for various communication intervals is studied. Then this framework was used to create a hybrid 2D map of the environment utilizing the capabilities of the mobile network the sycophant. In order to show possible applications of a sycophant deployment, the sycophant sensor node was equipped with a laser ranger as its sensor, and it was let to create a 2D map of its environment. This 2D map, which corresponds to a height dierent than the follower network, was merged with the 2D map of the mobile network forming a novel rough 3D map. Then by giving up from the need to properly localize the sycophant even when it is disconnected to the rest of the network, a full 3D map of the environment is obtained by fusing 2D map and tracking capabilities of the mobile network with the 2D vertical scans of the environment by the sycophant. And finally connectivity problems that arise from the hybrid sensor/actuator network were solved. For this 2 new connectivity maintenance algorithms, one based on the helix structures of the proteins, and the other based on the acute triangulation of the space forming a Gabriel Graph, were introduced. In this new algorithms emphasis has been given to sparseness in order to increase fault tolerance to regional problems. To better asses sparseness a new measure, called Resistance was introduced, as well as another called updistance.

Les avancements récents dans les technologies sans fil ont vu l'émergence de techniques de localisation qui constitue une base utile et rentable pour offrir des nouveaux services. Ces services topo-dépendants ont été de plus en plus bénéfiques pour les opérateurs et les entreprises de télécommunications. Divers services topo-dépendants peuvent être offerts 'a l'utilisateur tels que le suivi, la publicité, la sécurité, et la gestion. Les réseaux sans fil eux-mêmes peuvent bénéficier de l'information de localisation pour améliorer les performances de leurs différentes couches. Le routage, la synchronisation et l'annulation d'interférences sont quelques exemples o'u l'information de localisation peut être fructueuse. Un système de localisation doit être capable d'exécuter deux tâches principales : la mesure des paramètres topo-dépendants (RSSI, TOA, et TDOA) et l'estimation de la position en utilisant des estimateurs appropriés. L'objectif principal de cette thèse est l'étude de différentes techniques d'estimation de la position: algébriques et géométriques. Les techniques algébriques étudiées sont les moindres carrés, le maximum de vraisemblance, et la programmation semi-définie. La technique géométrique RGPA proposée est basée sur l'analyse par intervalles et la représentation géométrique des paramètres topo-dépendants. L'accent est mis sur la fusion de différents paramètres topo-dépendants et son influence sur la précision de positionnement. L'estimation et la mesure des paramètres topo-dépendants sont également étudiées en utilisant une campagne de mesures ULB afin d'avoir une compréhension complète du domaine de localisation.

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.

The aim of this work was to create a module for home automation tools Home Assistant. The module is able to determine  which room is inhabited and estimate more accurate position of people inside the room. Known GPS location cannot be used for this purpose because it is inaccurate inside buildings and therefore one of the indoor location techniques needs to be used. Solution based on Bluetooth Low Energy wireless technology was chosen. The localization technique is the fingerprinting method, which is based on estimating the position according to the signal strength at any point in space, which are compared with a database of these points using machine learning. The system can be supplemented with motion sensors that ensure a quick response when entering the room. This system can be deployed within a house, apartment or small to medium-sized company to determine the position of people in the building and can serve as a very powerful element of home automation.

The purpose of this thesis is to investigate how the LoRa modulation configuration affects precision and accuracy when using LoRa for indoor positioning in non-line-of-sight conditions. The aim is to research if an optimum combination of spreading factor, bandwidth, and code rate factor can be found to result in the best possible positioning accuracy and precision, under certain predefined conditions. An experiment was conducted, where quantitative data was collected from an experimental setup. The experiment consisted of two testbeds which also included an analysis between them. Two kinds of test units were used in the experiment. The LoRa 868 MHz transmitter, which represented the unknown position, and the receivers that were used to estimate the position of the transmitter. The experiment gathered the RSSI values between the transmitter and receivers at different configurations and locations. The data collected from the experiment were analyzed using mathematical theory to answer the research question. The most accurate and precise value for each individual transmission parameter was established in the first testbed and used as the base data rates in the second testbed, to evaluate the best performing parameters simultaneously. The mean accuracy in testbed 1 varied from 180 cm to 388 cm, and the mean precision ranged between 0.432 dBm to 1.298 dBm. The mean accuracy in testbed 2 varied from 341 cm to 455 cm, and the mean precision ranged between 0.275 dBm to 1.495 dBm.  The experimental results indicate no connections between the data rate and precision. No correlation is found between the accuracy and the data rate. The standard deviation and absolute error fluctuate independently of the data rate and the transmitter position. In regard to the given results, the authors conclude that no linear relationship is found between the LoRa modulation configuration and the precision and accuracy of a position, in the selected environment. The experimental results show that LoRa could be used for indoor positioning applications where a rough estimation of a position is adequate since the mean accuracy is quite low for almost all tested modulation configurations. There could be applications where other factors, such as the energy consumption or communication range, are of more importance than accuracy. For those applications, LoRa could still be an adequate choice of technology.

No description available.