Architecture for Multi-Technology Real-Time Location Systems

[Abstract]The rising popularity of location-based services has prompted considerable research in the field of indoor location systems. Since there is no single technology to support these systems, it is necessary to consider the fusion of the information coming from heterogeneous sensors. This paper presents a software architecture designed for a hybrid location system where we can merge information from multiple sensor technologies. The architecture was designed to be used by different kinds of actors independently and with mutual transparency: hardware administrators, algorithm developers and user applications. The paper presents the architecture design, work-flow, case study examples and some results to show how different technologies can be exploited to obtain a good estimation of a target position.[Resumen]El aumento de la popularidad de servicios localización-basados ha llevado a una investigación considerable en el campo de los sistemas de localización en interiores. Ya no hay solo tecnología para soportar estos sistemas, es necesario considerar la fusión de la información proveniente de sensores heterogéneos. Este papel presenta una arquitectura de software diseñada para un sistema de localización de híbridos donde nosotros podemos combinar información de múltiples tecnologías de sensor. La arquitectura fue diseñada para ser utilizada por diferentes tipos de actores independientemente y con transparencia mutua: los administradores de hardware, los desarrolladores de algoritmo y aplicaciones de usuario. El documento presenta el diseño de arquitectura, flujo de trabajo, ejemplos de estudios de caso y algunos resultados para mostrar cómo las diferentes tecnologías pueden explotarse para obtener una buena estimación de la posición de destinoMinisterio de Industria, Turismo y Comercio; IPT-020000-2010-35Ministerio de Educación y Ciencia; TEC2010-19545-C04-01Ministerio de Educación y Ciencia; CSD2008-0001

NLOS Identification and Mitigation Using Low-Cost UWB Devices

[Abstract] Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

[Abstract] Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments

[Abstract] Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Alternatives for Locating People Using Cameras and Embedded AI Accelerators: A Practical Approach

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Indoor positioning systems usually rely on RF-based devices that should be carried by the targets, which is non-viable in certain use cases. Recent advances in AI have increased the reliability of person detection in images, thus, enabling the use of surveillance cameras to perform person localization and tracking. This paper evaluates the performance of indoor person location using cameras and edge devices with AI accelerators. We describe the video processing performed in each edge device, including the selected AI models and the post-processing of their outputs to obtain the positions of the detected persons and allow their tracking. The person location is based on pose estimation models as they provide better results than do object detection networks in occlusion situations. Experimental results are obtained with public datasets to show the feasibility of the solution.This work has been funded by the Navantia-UDC Joint Research Unit under Grant IN853B-2018/02, the Xunta de Galicia (by grant ED431C 2020/15, and grant ED431G 2019/01 to support the Centro de Investigación de Galicia “CITIC”), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42) and ERDF funds of the EU (FEDER Galicia 2014–2020 & AEI/FEDER Programs, UE).Xunta de Galicia; IN853B-2018/02Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

An overview of IoT architectures, technologies, and existing open-source projects

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Today’s needs for monitoring and control of different devices in organizations require an Internet of Things (IoT) platform that can integrate heterogeneous elements provided by multiple vendors and using different protocols, data formats and communication technologies. This article provides a comprehensive review of all the architectures, technologies, protocols and data formats most commonly used by existing IoT platforms. On this basis, a comparative analysis of the most widely used open source IoT platforms is presented. This exhaustive comparison is based on multiple characteristics that will be essential to select the platform that best suits the needs of each organization.This research/work has been supported by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry, Xunta de Galicia under grant COV20/00604 through the ERDF Galicia 2014-2020; and by grant PID2019-104958RB-C42 (ADELE) funded by MCIN/AEI/10.13039/501100011033 . Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; COV20/0060

Fine Time Measurement for the Internet of Things: A Practical Approach Using ESP32

[Abstract]: In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternative real-time implementation for distance estimation inside the ESP32 using an approach based on machine learning. Such an implementation is successfully validated in a scenario totally different than those considered for the training and test sets. Finally, both the measurement sets and the developed software are available to the scientific community

Localización con Ultra Wideband en escenarios sin línea de visión directa: un enfoque práctico

Programa Oficial de Doctorado en Tecnologías de la Información y de las Comunicaciones en Redes Móviles. 5029V01[Abstract] Indoor location has experienced a major boost in recent years. location based services (LBS), which until recently were restricted to outdoor scenarios and the use of GPS, have also been extended into buildings. From large public structures such as airports or hospitals to a multitude of industrial scenarios, LBS has become increasingly present in indoor scenarios. Of the various technologies that can be used to achieve this indoor location, the ones based on ultrawideband (UWB) signals have become ones of the most demanded due primarily to their accuracy in position estimation. Additionally, the appearance in the market of more and more manufacturers and products has lowered the prices of these devices to levels that allow to think about their use for large deployments with a contained budget. By their nature, UWB signals are very resistant to the multi-path phenomenon, so in a situation of good direct vision between two devices (line-of-sight (LOS)) the technology is able to provide very accurate distance estimates. This is not the case when this line of sight is totally or partially blocked (non-line-of-sight (NLOS)). In this case the possible rebounds of the signal can be wrongly confused with the original emitted signal and result in an estimate of time, and therefore distance, very far from the actual value. The aim of this thesis is to offer a complete solution to the problem of indoor location with UWB from a practical approach. This means that the problem is approached from all the angles that should be taken into account in a real use case: basic research in search of an improvement at technical level of the UWB values, prior simulation to ensure a good deployment and performance and the final integration of the solution within an external operational system that allows LBS or clients to make use of location data. The first part of this proposal is presented in this thesis in the first chapters, and consists of using machine learning (ML) techniques to detect and mitigate the possible NLOS measurements generated by low-cost UWB devices. For this task the memory describes the different measurement campaigns with real hardware that were carried out to obtain the training data of the algorithms, and how these were used in different experiments to measure to what extent the proposed solution was valid in a real environment. The second part of the thesis presents data from a location-oriented UWB devices simulator created to perform tests and detect possible problems before deploying real hardware. Among the data presented during this second part, different comparisons between real and simulated scenarios are shown in order to check how reliable the simulation is. In addition, the simulator is presented as part of a case commonly used in indoor location systems: the location of vehicles in an industrial environment using multiple sensors. Finally, the third and final part of this thesis describes a multi-technology real-time location system (RTLS) capable of serving the different actors in a real location system. The platform serves as a link between the location sensors themselves, the LBS and end clients, the researchers in charge of the location algorithms and the managers of derived services such as alerts. By means of the proposed RTLS platform it is possible to decouple the functionality of these entities so that all of them can work without dependencies with the others. In addition to the description of the platform, this memory also includes a summary of the real localization projects in which it has been used, as well as a list of the various modifications and improvements that the platform has undergone in recent times thanks exactly to the experiences obtained after those works on site with real projects.[Resumen] La localización en interiores ha sufrido un importante empuje en los últimos años. Los servicios basados en localización (location based services (LBS)) que hasta hace poco estaban restringidos a escenarios exteriores y al uso del GPS, han ido extendiéndose también hacia el interior de los edificios. Desde grandes estructuras públicas como aeropuertos o hospitales hasta multitud de escenarios industriales, los LBS han pasado a estar cada vez mas presentes en escenarios bajo techo. De entre las diversas tecnologías que pueden utilizarse para conseguir esta localización en interiores, las basadas en señales ultra-wideband (UWB) se han convertido en una de las mas demandadas debido fundamentalmente a su precisión en la estimación de posición. Adicionalmente, la aparición en el mercado de cada vez más fabricantes y más productos ha hecho disminuir los precios de estos dispositivos hasta niveles que permiten pensar en su uso para grandes despliegues con un presupuesto contenido. Por su naturaleza, las señales UWB son muy resistentes al fenómeno de multi trayecto, por lo que en una situación de buena visión directa entre dos dispositivos (line-of-sight (LOS)) la tecnología es capaz de proporcionar estimaciones de distancia muy precisas. Esto no ocurre así cuando esta linea de visión esta total o parcialmente bloqueada (non-line-of-sight (NLOS)). En este caso los posibles rebotes de la señal pueden ser erróneamente confundidos con la señal original y derivar en una estimación de tiempo, y por tanto distancia, muy lejana al valor real. El objetivo de esta tesis es ofrecer una solución completa al problema de la localización en interiores con UWB desde un enfoque práctico. Esto quiere decir que se aborda el problema desde todos los ángulos que se deberían tener en cuenta en un caso de uso real: la investigación básica en busca de una mejora a nivel técnico de los valores UWB, la simulación previa para garantizar un buen despliegue y rendimiento y la integración final de la solución dentro de una operativa externa que permita a LBS o clientes poder hacer uso de los datos de localización. La primera parte de esta propuesta se presenta en esta tesis en los primeros capítulos, y consiste en utilizar algoritmos de aprendizaje automático (machine learning (ML)) para detectar y mitigar las posibles medidas NLOS generadas por dispositivos UWB de bajo coste. Para esta tarea se muestran las diferentes campañas de medida con hardware real que se realizaron para obtener los datos de entrenamiento de los algoritmos, y cómo estos se utilizaron en diferentes experimentos para medir hasta que punto la solución planteada era válida en un entorno real. La segunda parte de la tesis presenta por su parte los datos de un simulador de dispositivos UWB orientado a la localización creado para poder realizar pruebas y detectar posibles problemas antes de efectuar un despliegue con hardware real. Entre los datos presentados durante esta segunda parte se muestran diferentes comparativas entre escenarios reales y simulados para comprobar como de fidedigna es la simulación. Además, se presenta el simulador integrado dentro de un caso de uso habitual en los sistemas de localización en interiores: la localización de vehículos en un entorno industrial utilizando múltiples sensores. Por último, la tercera y última parte de esta tesis describe una plataforma de localización en tiempo real (real-time location system (RTLS)) multi-tecnología capaz de dar servicio a los diferentes actores habituales en un sistema de localización real. La plataforma sirve de nexo de unión entre los propios sensores de localización, los LBS y clientes finales, los investigadores encargados de la algorítmica de localización y los gestores de servicios derivados como alertas. Mediante la plataforma RTLS propuesta en esta tercera parte de la memoria se logra desacoplar la funcionalidad de estas entidades de forma que todas ellas puedan trabajar sin dependencias con las demás. Además de la descripción de la plataforma, se incluye también un resumen de los proyectos reales de localización en los que esta ha sido utilizada, así como un listado de las diversas modificaciones y mejoras que la plataforma ha sufrido en los últimos tiempos gracias precisamente a las experiencias obtenidas tras esos trabajos sobre el terreno con proyectos reales.[Resumo] A localización en interiores sufriu dun importante empuxe nos últimos anos. Os servizos baseados en localización (location based services (LBS)) que ata fai pouco estaban restrinxidos a escenarios exteriores e a o uso do GPS, foron estendéndose tamén cara o interior dos edificios. Dende grandes estruturas públicas como aeroportos ou hospitais ata multitude de escenarios industriais, os LBS están cada vez mais presentes baixo teito. De entre as diversas tecnoloxías que poden ser utilizadas para acadar esta localización en interiores, as baseadas en sinais ultra-wideband (UWB) convertéronse nunha das mais demandadas debido fundamentalmente á súa precisión na estimación de posición. Adicionalmente, a aparición no mercado de cada vez mais fabricantes e mais produtos fixo diminuír os prezos destes dispositivos ata niveis que permiten pensar no seu uso para grandes despregues con un presuposto contido. Pola súa natureza, os sinais UWB son moi resistentes ao fenómeno de multi-traxecto, polo que nunha situación de boa visión directa entre os dous dispositivos (LOS) a tecnoloxía é capaz de proporcionar estimación de distancia moi precisas. Isto non ocorre así cando esta liña de de visión está total ou parcialmente bloqueada (NLOS). Neste caso os posibles rebotes do sinal poden ser erroneamente confundidos co sinal orixinal e derivar nunha estimación de tempo, e por tanto distancia, moi alonxada do seu valor real. O obxectivo desta tese é o de ofrecer una solución completa ao problema da localización en interiores con UWB dende un enfoque práctico. Isto quere dicir que se aborda o problema dende todos os ángulos que se deberían ter en conta nun caso de uso real: a investigación básica en busca dunha mellora a nivel técnico dos valores de UWB, a simulación previa para garantir un bo despregue e rendemento e a integración final da solución dentro dunha operativa externa que permita a LBS ou clientes poder facer uso dos datos de localización. A primeira parte desta proposta presentase nos primeiros capítulos da tese, e consiste en utilizar algoritmos de aprendizaxe automático (machine learning (ML)) para detectar e mitigar as posibles medidas NLOS xeradas por dispositivos UWB de baixo custo. Para esta tarefa móstranse as diferentes campañas de medida con dispositivos reais que se realizaron para obter os datos de adestramento dos algoritmos, e como estes se empregaron en diferentes experimentos para medir ata que punto a solución proposta era válida nun entorno real. A segunda parte da tese presenta pola súa parte os datos dun simulador de dispositivos UWB orientado á localización creado para poder realizar probas e detectar posibles problemas antes de efectuar un despregue con dispositivos reais. Entre os datos presentados durante esta segunda parte móstranse diferentes comparativas entre escenarios reais e simulados para comprobar como de fidedigna é a simulación. Ademais, preséntase o simulador integrado dentro dun caso de uso habitual nos sistema de localización en interiores: a localización de vehículos nun entorno industrial utilizando múltiples sensores. Por último, a terceira e derradeira parte desta tese describe unha plataforma de localización en tempo real (real-time location system (RTLS)) multi-tecnoloxía capaz de dar servizo aos diferentes actores habituais nun sistema de localización real. A plataforma serve de punto de unión entre os propios sensores de localización, os LBS e clientes finais, os investigadores encargados da algorítmica de localización e os xestores de servizos derivados como alertas. Mediante a plataforma RTLS proposta nesta terceira parte da memoria lograse desacoplar a funcionalidade destas entidades de forma que todas elas poden traballar sen depender das demais. Ademais da descrición da plataforma, inclúese tamén un resumo dos proxectos reais de localización nos que esta foi utilizada, así como un listado das diversas modificación e melloras que a plataforma sufriu nos últimos tempos grazas precisamente as experiencias obtidas tras estes traballos sobre o terreo con proxectos reais