BLE Localization using RSSI Measurements and iRingLA

International audienceOver the last few years, indoor localization has been a very dynamic research area that has drawn great attention. Many methods have been proposed for indoor positioning as well as navigation services. A big number of them were based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI) for their simplicity of use. The main issues of the studies conducted in this field are related to the improvement of localization factors like accuracy, computational complexity, easiness of deployment and cost. In our study, we used Bluetooth Low Energy (BLE) technology for indoor localization in the context a smart home where an elderly person can be located using an hybrid system that combines the radio, light and sound information. In this paper, we propose a model that averages the received signal strength indication (RSSI) at any the distance domain which offered accuracy down to 1 meter, depending on the deployment configuration

PINSPOT: An oPen platform for INtelligent context-baSed Indoor POsiTioning

This work proposes PINSPOT; an open-access platform for collecting and sharing of context, algorithms and results in the cutting-edge area of indoor positioning. It is envisioned that this framework will become reference point for knowledge exchange which will bring the research community even closer and potentially enhance collaboration towards more effective and efficient creation of indoor positioning-related knowledge and innovation. Specifically, this platform facilitates the collection of sensor data useful for indoor positioning experimentation, the development of novel, self-learning, indoor positioning algorithms, as well as the enhancement and testing of existing ones and the dissemination and sharing of the proposed algorithms along with their configuration, the data used, and with their results

An Open Platform for Studying and Testing Context-Aware Indoor Positioning Algorithms

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, our proposal features novelties related to the collection and use of additional context data. The platform is realized in the form of a mobile client, currently implemented on Android. It enables manual collection of radio-maps—i.e. fingerprints of Wi-Fi signals—while also allowing for amending the fingerprints with various context data which could help improve the accuracy of positioning algorithms. While this is a research-in-progress platform, an initial experiment was carried out and its results were used to justify its applicability and relevance

Desenvolvimento de aplicação para posicionamento indoor por meio das redes wifi em ambientes internos

Orientadora: Dra. Luciene Stamato DelazariDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 08/02/2019Inclui referências: p.122-131Área de concentração: CartografiaResumo: No ambiente interno a navegação de um usuário é baseada em seu conhecimento do local ou com base em pontos de referências que encontramse no ambiente, isto pode ser assistido por sistemas que forneçam a posição do usuário. Neste tipo de ambiente, deve-se usar um Sistema de Posicionamento Interno (IPS), sendo que os IPS mais usados são os que utilizam a tecnologia WIFI, já que encontra-se implantada na maioria dos prédios e dispositivos móveis atuais, tem um padrão de transmissão e infraestrutura estabelecida. Nesta pesquisa foi desenvolvida uma solução de código aberto para um IPS com a tecnologia WIFI em um dispositivo móvel com sistema operacional android, no contexto de aplicação UFPR CampusMap, que corresponde a um sistema desenvolvido pelo grupo de pesquisa de Cartografia e SIG da Universidade Federal do Paraná (UFPR), que tem por objetivo mapear os ambientes externos e internos dos diferentes campus da UFPR, o sistema atual corresponde a uma aplicação web e não tem disponível a funcionalidade do posicionamento. Para gerar a solução, foram feitos estudos com respeito ao Received Signal Strength Indicator (RSSI), e como este varia com respeito à distância, concluindo que este diminui com o aumento da distância, e devido aos fatores que o interferem deve ser modelado, para melhorar a qualidade do posicionamento obtido com base no RSSI. Além disso, foi avaliado o posicionamento no modo estático e cinemático para as técnicas do centróide, centróide ponderado e da trilateração, obtendo como acurácias médias 10,43; 7,39 e 8,38 metros respectivamente, com isso foi escolhida a técnica do centróide ponderado como a técnica para ser empregada na solução. Finalmente, foi avaliada a solução enquanto a detecção do prédio, andar e sala, concluindo que o aplicativo poderia ser utilizado para auxiliar ao usuário na sua localização, já que tem uma certeza de 100% na detecção do prédio e 79% do andar. Os resultados apontam à viabilidade de desenvolvimento e a possibilidade de gerar interfaces gráficas para dados geográficos em dispositivos móveis, que permitam uma melhor representação e interação entre o usuário e o ambiente interno. Palavras-chave: Posicionamento interno. Redes WIFI. Código aberto. Android.Abstract: In the indoor environment, a user's navigation is based on knowledge of the place or based on reference points found in the environment, this can be assisted by systems that provide the user's position. In this type of environment, one should use an Indoor Positioning System (IPS), the most used IPS are those that use WIFI technology, since it is implanted in most of the buildings and current mobile devices, it has a transmission pattern and established infrastructure. In this research was developed an open source solution for an IPS with this technology in a smartphone with android operating system, in the context of the UFPR Campus Map application, which corresponds to a system developed by the Cartography and GIS research group of the Federal University of Paraná (UFPR), which aims to map the indoor and outdoor environments of the different UFPR campus, the system corresponds to a web application and has no positioning functionality available. In order to generate the solution, studies were conducted regarding the Received Signal Strength Indicator (RSSI), how it varies with respect to distance, concluding that it decreases with increasing distance, and because of the factors that interfere in it must be modeled, to improve the quality of the positioning obtained from RSSI. In addition, the positioning in the static and kinematic mode was evaluated for the centroid, weighted centroid and trilateration techniques, obtaining as accuracy 10.43; 7.39 and 8.38 meters respectively, with this chose the weighted centroid technique as the technique to be employed in the solution. Finally, the solution was evaluated in detecting the building, floor and room, concluding that the application could be used to assist the user in its location, since it has a certainty of 100% in the detection of the building and 79% of the floor. The results point to the feasibility of development and the possibility of generating graphical interfaces for geographic data in mobile devices, allowing a better representation and interaction between the user and the indoor environment. Key-words: Indoor positioning. WIFI networks. Open source code. Android

Ανάπτυξη συστήματος εντοπισμού θέσης σε εσωτερικούς χώρους με χρήση ασύρματων σημάτων και επαυξημένης πραγματικότητας

Τα συστήματα εντοπισμού θέσης σε εσωτερικούς χώρους ασχολούνται με την εύρεση της ακριβής θέσης ενός χρήστη σε εσωτερικούς χώρους αξιοποιώντας τεχνολογίες διαφορετικές από αυτή του GPS. Τα συστήματα αυτά έχουν γνωρίσει ιδιαίτερο ενδιαφέρον λόγω της πλειονότητας των σεναρίων που βρίσκουν εφαρμογή. Έχουν μελετηθεί πολλές τεχνολογίες για τη δημιουργία τέτοιων συστημάτων καθώς και διάφορες τεχνικές για την επεξεργασία των δεδομένων από τις τεχνολογίες αυτές με σκοπό την καλύτερη ακρίβεια των συστημάτων. Στόχος της πτυχιακής αυτής είναι η μελέτη και η αξιολόγηση των τεχνολογιών αυτών καθώς και η ανάπτυξη ενός δικού μας συστήματος. Για την ανάπτυξη του συστήματος μας μελετήσαμε τις καινοτόμες τεχνολογίες της αναγνώρισης αντικειμένων μέσω μοντέλων μηχανικής μάθησης και της επαυξημένης πραγματικότητας στο λειτουργικό Android. Καταλήξαμε να συνδυάσουμε την επαυξημένη πραγματικότητα με τις τεχνολογίες του WiFi και των ραδιοσυχνοτήτων μέσω της τεχνικής της χαρτογράφησης (fingerprinting) για την ανάπτυξη του συστήματος μας. Δημιουργήσαμε επομένως ένα σύστημα που μπορεί να χαρτογραφεί τα σήματα ενός χώρου σε συγκεκριμένα σημεία και να τοποθετεί αντικείμενα επαυξημένης πραγματικότητας στον χώρο. Τα σήματα αυτά χρησιμοποιούνται σε συνδυασμό με τις αποστάσεις από τα επαυξημένα αντικείμενα ώστε να προσδιορίζεται η θέση ενός χρήστη. Από μετρήσεις που διενεργήσαμε, διαπιστώσαμε ότι το σύστημα μας επιτυγχάνει μεγάλη ακρίβεια και είναι δυνατόν να χρησιμοποιηθεί και ως εμπορική εφαρμογή αν ξεπεραστεί ένας περιορισμός.Indoor positioning systems deal with the task of finding the location of a user (or a device) in an indoor space. These systems are quite famous nowadays due to the many uses their applications can find. There are many different technologies used for creating such systems and many techniques for assessing the data from these technologies in order to maximize their accuracy. In this thesis we study and evaluate the different technologies used to create indoor positioning systems and create one of our own. Firstly we examined the novel technologies of object detection from the machine learning area and augmented reality in order to create such a system. We finally managed to create a system with the use of augmented reality and the WiFi and Radio technologies utilizing the fingerprinting technique. Our system maps a point in space both for WiFi and radio signals and visually using an augmented object. After a complete fingerprinting map is created, a user can find his location by using the signals that are available on his device and the distances from the augmented objects he finds. Our system has been tested thoroughly and compared to other systems. We concluded that it achieves very good accuracy if it is tuned and can be used as a commercial application if a certain restriction is lifted

Li-Fi: Navegação Assistida por LEDs

Trabalho Final de Mestrado para Obtenção do Grau de Mestre em Engenharia Eletrónica e Telecomunicações na Área de Especialização em TelecomunicaçõesA integração de dispositivos de Internet of Things (IoT) e Light Fidelity (Li-Fi) revela-se um campo promissor na evolução tecnológica, associada à tendência atual de incorporação de serviços de localização, em equipamentos inteligentes. Apesar do Sistema de Posicionamento Global (GPS) funcionar bem em ambientes exteriores, o mesmo não acontece em ambientes fechados, como edifícios ou locais subterrâneos, em que o sinal é atenuado ou refletido pelos materiais que os constituem, levando à falha de serviço ou imprecisão no posicionamento. Atualmente existem diferentes tecnologias de navegação, para uso em ambientes interiores. Grande parte é baseada em sinais de Radiofrequência (RF), como é o caso do Wireless Fidelity (Wi-Fi), que tem uma precisão baixa para posicionamento, quando é mais acessível. No entanto o espectro RF está cada vez mais limitado, devido ao aumento de tráfego. Nem mesmo com a eficiência espectral e reutilização de frequência, se consegue resolver esta questão. Por outro lado, o espectro do visível, no qual o Li-Fi se baseia, encontra-se totalmente livre. A grande vantagem de usar Li-Fi em vez de Wi-Fi, para localização em ambientes interiores, é a obtenção de melhor desempenho a um preço menor. Isto porque, num edifício, existem dez vezes mais Díodos Emissores de Luz(LEDs), em comparação com os Pontos de Acesso (APs) Wi-Fi. Assim, os LEDs permitem uma triangulação mais rápida e precisa, do dispositivo móvel e a reutilização dos mesmos para comunicação torna-os na opção mais sustentável. Por estas razões, a tecnologia de Comunicação por Luz Visível (VLC), no qual o Li-Fi se baseia, estará na vanguarda dos futuros serviços baseados em localização indoor e constitui uma das tecnologias que a Sexta Geração (6G) das telecomunicações irá incluir. Nesta tese descreve-se a caracterização de um sistema de comunicação através de luz visível, em que os dados são transmitidos através de uma lâmpada tetracromática de LEDs (RGBV-LED). Os LEDs permitem comutar diferentes níveis de intensidade de luz, a uma taxa elevada e impercetível pelo olho humano. Esta funcionalidade pode ser usada para comunicação, onde os dados são codificados na luz emitida, permitindo que os LEDs possam ser utilizados para fornecer iluminação e também para comunicação. Assim, a lâmpada de iluminação, envia dados (incorporados no sinal ótico emitido), que são transmitidos e recebidos por um fotodetetor, baseado em ligas de Silício Amorfo Hidrogenado (a-SiC:H) com propriedades de filtragem e de desmultiplexagem. A caracterização do canal de comunicação por luz visível, estabelecido entre a infraestrutura de iluminação e comunicação e o utilizador móvel, em condições de linha de vista, é discutida neste trabalho. Recorreu-se ao modelo Lambertiano para caracterizar a distribuição de sinal ótico do LED. O ganho do canal foi calculado, assumindo uma configuração quadrangular dos LEDs tetracomáticos, em que os dados a transmitir, juntamente com os identificadores, atribuídos à localização física dos transmissores, foram enviados usando um esquema com modulação On-Off Keying (OOK). Foi determinada a cobertura do sinal e as respetivas regiões footprint, dentro de cada célula unitária, o que permitiu estimar o sinal multiplexado recebido pelo fotodetetor. Este modelo foi calibrado com os dados experimentais obtidos no protótipo laboratorial. A descodificação do sinal multiplexado ocorreu, numa primeira fase, a uma curva de calibração e, posteriormente, foi otimizada através da metodologia de bits de controlo de paridade, o que permitiu melhorar significativamente a Taxa de Erro de Bits (BER) do sistema. O sistema de navegação por Li-Fi foi analisado através da simulação de demonstrações de localização indoor de elevada resolução.The integration of Internet of Things (IoT) and Light Fidelity (Li-Fi) devices is a promising field in technological evolution, associated with the current trend of incorporating location services in smart devices. Although the Global Positioning System (GPS) works well outdoors, the same does not happen indoors, such as in buildings or underground locations, where the signal is attenuated or reflected by the materials that constitute them, leading to service failure or inaccuracy in positioning. Today there are different navigation technologies for indoor use. Most are based on Radio Frequency (RF) signals, such as Wireless Fidelity (Wi-Fi), which has a low accuracy for positioning when it is more accessible. However, the RF spectrum is increasingly limited due to increased traffic, and even with spectral efficiency and frequency reuse, this cannot be solved. On the other hand, the visible spectrum, on which Li-Fi is based, is completely free. The big advantage of using Li-Fi, instead of Wi-Fi, for indoor location is that you get better performance at a lower price. That´s because in a building there are ten times more Light Emitting Diodes (LEDs), compared to Wi-Fi Access Points (APs). So, LEDs enable faster and more accurate triangulation of the mobile device and reusing them for communication makes them the most sustainable option. For these reasons, Visible Light Communication (VLC) technology, on which Li-Fi is based, will be at the forefront of future indoor location-based services and is one of the technologies that the Sixth Generation (6G) of telecommunications will include. This thesis describes the characterization of a communication system using visible light, in which data is transmitted through a tetrachromatic LED lamp (RGBV-LED). LEDs allow switching different levels of light intensity at a high rate, that is imperceptible to the human eye. This functionality can be used for communication where data is encoded in the emitted light, allowing the LEDs to be used to provide illumination as well as for communication. Thus, the illumination lamp, sends data (embedded in the emitted optical signal), which is transmitted and received by a photodetector based on Hydrogenated Amorphous Silicon (a-SiC:H) alloys with filtering and demultiplexing properties. The characterization of the visible light communication channel, established between the lighting and communication infrastructure and the mobile user, under line-of-sight conditions, is discussed in this work. The Lambertian model was used to characterize the optical signal distribution of the LED. The channel gain was calculated assuming a quadrangular configuration of the tetrachromatic LEDs, where the data to be transmitted along with identifiers, assigned to the physical location of the transmitters, were sent using an On-Off Keying (OOK) modulated scheme. The signal coverage and the respective footprint regions within each unit cell were determined, which allowed estimation of the multiplexed signal, received by the photodetector. This model was calibrated with experimental data, obtained from the laboratory prototype. The decoding of the multiplexed signal first occurred to a calibration curve and was subsequently optimized using the parity control bit methodology, which allowed the Bit Error Rate (BER) of the system to be significantly improved. The Li-Fi navigation system was analyzed by simulating high-resolution indoor localization demonstrations.info:eu-repo/semantics/publishedVersio

Kernel and Multi-Class Classifiers for Multi-Floor WLAN Localisation

Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and time-efficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multi-floor environment. This thesis proposes an improved multi-floor localisation technique by integrating three important elements of the system; radio map fingerprint database optimisation, floor or vertical localisation, and horizontal localisation. The main focus of this work is to extend the kernel density approach and implement multi- class machine learning classifiers to improve the localisation accuracy and processing time of the each and overall elements of the proposed technique. For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. The variant AP selection is further improved by grouping AP based on signal distribution. In this work, two AP selection algorithms are proposed which are Max Kernel and Kernel Logistic Discriminant that implement the knowledge of kernel density estimate and logistic regression machine learning classification. For floor localisation, the strategy is based on developing the algorithm to determine the floor by utilising fingerprint clustering technique. The clustering method is based on simple signal strength clustering which sorts the signals of APs in each fingerprint according to the strongest value. Two new floor localisation algorithms namely Averaged Kernel Floor (AKF) and Kernel Logistic Floor (KLF) are studied. The former is based on modification of univariate kernel algorithm which is proposed for single-floor localisation, while the latter applies the theory kernel logistic regression which is similar to AP selection approach but for classification purpose. For horizontal localisation, different algorithm based on multi-class k-nearest neighbour classifiers with optimisation parameter is presented. Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. All of the algorithms are later combined to provide device location estimation for multi-floor environment. Improvement of 43.5% of within 2 meters location accuracy and reduction of 15.2 times computational time are seen as compared to existing multi-floor localisation techniques by Gansemer and Marques. The improved accuracy is due to better performance of proposed floor and horizontal localisation algorithm while the computational time is reduced due to introduction of AP selection algorithm