Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Visible Light Positioning using Received Signal Strength for Industrial Environments

There is a forecast for exceptional digital data traffic growth due to the digitisation of industrial applications using the internet of things. As a result, a great need for high bandwidth and faster transmission data rates for future wireless networks has emerged. One of the considered communication technologies that can assist in satisfying this demand is visible light communications (VLC). VLC is an emerging technology that uses the visible light spectrum by mainly utilising lightemitting diodes (LEDs) for simultaneous indoor lighting and high bandwidth wireless communication. Some of the applications of VLC are to provide high data rate internet in homes, offices, campuses, hospitals, and several other areas. One of these promising areas of application is for industrial wireless communications. The research project will provide a review of VLC applications intended for industrial applications with an emphasis on visible light positioning (VLP). In this research work, a three-dimensional (3D) positioning algorithm for calculating the location of a photodiode (PD) is presented. It solely works on measured powers from different LED sources and does not require any prior knowledge of the receiver’s height unlike other works in the literature. The performance of the proposed VLP algorithm in terms of positioning error is evaluated using two different trilateration algorithms, the Cayley–Menger determinant (CMD) and the Linear Least Squares (LLS) trilateration algorithms. The evaluation considers different scenarios, with and without receiver tilt, and with multipath reflections. Simulation results show that the CMD algorithm is more accurate and outperforms the LLS trilateration positioning algorithm. Furthermore, the proposed method has been experimentally assessed under two different LED configurations, with different degrees of receiver tilt, and in the presence of a fully stocked storage rack to examine the effect of multipath reflections on the performance of VLP systems. It was observed from simulations and experimental investigations that the widely used square LED-configuration results in position ambiguities for 3D systems while a non-lattice layout, such as a star-shaped configuration, is much more accurate. An experimental accuracy with a 3D median error of 10.5 cm was achieved using the CMD algorithm in a 4 m × 4 m × 4.1 m area with a horizontal receiver. Adding receiver tilt of 5◦ and 10◦ increases the median error by an average of 29% and 110%, respectively. The effect of reflections from the i storage rack has also been thoroughly examined using the two mentioned trilateration algorithms and showed to increase the 3D median positioning error by an average of 69% in the experimental testbed for the areas close to the storage rack. These results highlight the degrading effect of multipath reflections on VLP systems and the necessity to consider it when evaluating these systems. As the primary consideration for positioning systems in industrial environments is for mobile robots, the encouraging results in this thesis can be further improved though the use of a sensor fusion method

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Probabilistic Time of Arrival Localization

In this letter, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement

Practical implementation of a hybrid indoor localization system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, coverage range, energy consumption and cost. This master thesis describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). The objective is to track a target node, assuming that the object follows a linear motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed on the two KF outputs in order to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear. Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente, a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%, enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente

Research on port AGV composite positioning based on UWB/RFID

In recent years, ports in various countries have successively carried out research and application of fully automated terminal. The terminal adopts the "Double car shore bridge + AGV + ARMG" automation process, which is the most widely used and relatively mature fully automated solution. At present, the AGV navigation of the terminal is based on RFID magnetic nail positioning and the accuracy is good. However, nowadays UWB technology has become the most popular technology in ranging and positioning. The research in this work is based on UWB/RFID composite positioning, which is mainly used for the specific localization tasks in the port and it can accurately locate the position of the AGV. This MSc work studies the UWB positioning system first and then researches the traditional 3D positioning algorithm. Importance contribution expressed by 3D TOA localization algorithm. For RFID system, this connection between the reader and the carrier is designed, and the reference tag is buried. At last, data-based on RFID localization algorithm in scene analysis method is adopted for positioning. Secondly, the basis of the composite positioning system is data fusion technology. The most widely used and mature fusion algorithm is the Kalman filter algorithm and Particle filter. Finally, the experimental analysis of UWB and RFID composite positioning system is implemented. The results indicate that UWB and RFID composite positioning system can reduce the cost of the positioning system. Higher positioning accuracy and robustness are characterizing the developed system.Nos últimos anos, portos de vários países realizaram sucessivamente pesquisas e aplicações de terminais totalmente automatizados. O terminal adota o processo de automação "Double car shore bridge + AGV + ARMG", que é a solução totalmente automatizada mais amplamente utilizada e relativamente madura. Atualmente, a navegação AGV do terminal é baseada no posicionamento da etiqueta RFID e a precisão é boa. No entanto, hoje em dia, a tecnologia UWB tornou-se na tecnologia mais popular relativamente ao alcance e posicionamento. A pesquisa neste trabalho é baseada no posicionamento composto por UWB / RFID, usado principalmente para tarefas de localização específicas nos portos, podendo desta forma localizar-se com precisão a posição do AGV. Este projeto de mestrado estuda em primeiro lugar o sistema de posicionamento UWB, e depois um algoritmo tradicional de posicionamento 3D. A contribuição da importância expressa pelo algoritmo de posicionamento “time of arrival” (TOA) 3D foi proposta. Para o sistema de posicionamento RFID, a conexão entre o leitor e a transportadora é projetada e a etiqueta de referência é ocultada. Por fim, o algoritmo de “k-nearest neighbor” baseado numa base de dados e no método de análise de cena é adotado para realizar o posicionamento. Em segundo lugar, a base do sistema de posicionamento composto é a tecnologia de fusão de dados. O algoritmo de fusão mais amplamente utilizado e maduro é o algoritmo de filtro Kalman e o filtro de partículas. Finalmente, é realizada a análise experimental do sistema de posicionamento composto UWB e RFID. Os resultados experimentais mostram que o sistema de posicionamento composto UWB e RFID pode reduzir o custo do sistema de posicionamento. O sistema desenvolvido é caracterizado por uma maior precisão de posicionamento e robustez

A comparison analysis of ble-based algorithms for localization in industrial environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018