Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de Economía y CompetitividadXunta de Galici

Hierarchical approach to enhancing topology-based WiFi indoor localization in large environments

Traditionally, WiFi has been used for indoors localization purposes due to its important advantages. There are WiFi access points in most buildings and measuring WiFi signal is free of charge even for private WiFi networks. Unfortunately, it also has some disadvantages: when extending WiFi-based localization systems to large environments their accuracy decreases. This has been previously solved by manually dividing the environment into zones. In this paper, an automatic partition of the environment is proposed to increase the localization accuracy in large environments. To do so, a hierarchical partition of the environment is performed using K-Means and the Calinski-Harabasz Index. Then, different classification techniques have been compared to achieve high localization rates. The new approach is tested in a real environment with more than 200 access points and 133 topological positions, obtaining an overall increase in the accuracy of approximately 10% and reducing the error to the real position to 2.45 metres.Ministerio de Ciencia e InnovaciónUniversidad de AlcaláPrincipado de Asturia

Wi-Fi Location Determination for Semantic Locations

In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations

Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly reduces the efforts for collecting WiFi fingerprints. Furthermore, since these NAVs explore a 3D space, the WiFi fingerprints of a 3D space can be obtained increasing the localization accuracy. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi - fingerprint - based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with the manual WiFi fingerprint survey methods

Managing trust and reliability for indoor tracking systems

Indiana University-Purdue University Indianapolis (IUPUI)Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Development of Machine Learning Techniques for Diabetic Retinopathy Risk Estimation

La retinopatia diabètica (DR) és una malaltia crònica. És una de les principals complicacions de diabetis i una causa essencial de pèrdua de visió entre les persones que pateixen diabetis. Els pacients diabètics han de ser analitzats periòdicament per tal de detectar signes de desenvolupament de la retinopatia en una fase inicial. El cribratge precoç i freqüent disminueix el risc de pèrdua de visió i minimitza la càrrega als centres assistencials. El nombre dels pacients diabètics està en augment i creixements ràpids, de manera que el fa difícil que consumeix recursos per realitzar un cribatge anual a tots ells. L’objectiu principal d’aquest doctorat. la tesi consisteix en construir un sistema de suport de decisions clíniques (CDSS) basat en dades de registre de salut electrònic (EHR). S'utilitzarà aquest CDSS per estimar el risc de desenvolupar RD. En aquesta tesi doctoral s'estudien mètodes d'aprenentatge automàtic per constuir un CDSS basat en regles lingüístiques difuses. El coneixement expressat en aquest tipus de regles facilita que el metge sàpiga quines combindacions de les condicions són les poden provocar el risc de desenvolupar RD. En aquest treball, proposo un mètode per reduir la incertesa en la classificació dels pacients que utilitzen arbres de decisió difusos (FDT). A continuació es combinen diferents arbres, usant la tècnica de Fuzzy Random Forest per millorar la qualitat de la predicció. A continuació es proposen diverses tècniques d'agregació que millorin la fusió dels resultats que ens dóna cadascun dels arbres FDT. Per millorar la decisió final dels nostres models, proposo tres mesures difuses que s'utilitzen amb integrals de Choquet i Sugeno. La definició d’aquestes mesures difuses es basa en els valors de confiança de les regles. En particular, una d'elles és una mesura difusa que es troba en la qual l'estructura jeràrquica de la FDT és explotada per trobar els valors de la mesura difusa. El resultat final de la recerca feta ha donat lloc a un programari que es pot instal·lar en centres d’assistència primària i hospitals, i pot ser usat pels metges de capçalera per fer l'avaluació preventiva i el cribatge de la Retinopatia Diabètica.La retinopatía diabética (RD) es una enfermedad crónica. Es una de las principales complicaciones de diabetes y una causa esencial de pérdida de visión entre las personas que padecen diabetes. Los pacientes diabéticos deben ser examinados periódicamente para detectar signos de diabetes. desarrollo de retinopatía en una etapa temprana. La detección temprana y frecuente disminuye el riesgo de pérdida de visión y minimiza la carga en los centros de salud. El número de pacientes diabéticos es enorme y está aumentando rápidamente, lo que lo hace difícil y Consume recursos para realizar una evaluación anual para todos ellos. El objetivo principal de esta tesis es construir un sistema de apoyo a la decisión clínica (CDSS) basado en datos de registros de salud electrónicos (EHR). Este CDSS será utilizado para estimar el riesgo de desarrollar RD. En este tesis doctoral se estudian métodos de aprendizaje automático para construir un CDSS basado en reglas lingüísticas difusas. El conocimiento expresado en este tipo de reglas facilita que el médico pueda saber que combinaciones de las condiciones son las que pueden provocar el riesgo de desarrollar RD. En este trabajo propongo un método para reducir la incertidumbre en la clasificación de los pacientes que usan árboles de decisión difusos (FDT). A continuación se combinan diferentes árboles usando la técnica de Fuzzy Random Forest para mejorar la calidad de la predicción. Se proponen también varias políticas para fusionar los resultados de que nos da cada uno de los árboles (FDT). Para mejorar la decisión final propongo tres medidas difusas que se usan con las integrales Choquet y Sugeno. La definición de estas medidas difusas se basa en los valores de confianza de las reglas. En particular, uno de ellos es una medida difusa descomponible en la que se usa la estructura jerárquica del FDT para encontrar los valores de la medida difusa. Como resultado final de la investigación se ha construido un software que puede instalarse en centros de atención médica y hospitales, i que puede ser usado por los médicos de cabecera para hacer la evaluación preventiva y el cribado de la Retinopatía Diabética.Diabetic retinopathy (DR) is a chronic illness. It is one of the main complications of diabetes, and an essential cause of vision loss among people suffering from diabetes. Diabetic patients must be periodically screened in order to detect signs of diabetic retinopathy development in an early stage. Early and frequent screening decreases the risk of vision loss and minimizes the load on the health care centres. The number of the diabetic patients is huge and rapidly increasing so that makes it hard and resource-consuming to perform a yearly screening to all of them. The main goal of this Ph.D. thesis is to build a clinical decision support system (CDSS) based on electronic health record (EHR) data. This CDSS will be utilised to estimate the risk of developing RD. In this Ph.D. thesis, I focus on developing novel interpretable machine learning systems. Fuzzy based systems with linguistic terms are going to be proposed. The output of such systems makes the physician know what combinations of the features that can cause the risk of developing DR. In this work, I propose a method to reduce the uncertainty in classifying diabetic patients using fuzzy decision trees. A Fuzzy Random forest (FRF) approach is proposed as well to estimate the risk for developing DR. Several policies are going to be proposed to merge the classification results achieved by different Fuzzy Decision Trees (FDT) models to improve the quality of the final decision of our models, I propose three fuzzy measures that are used with Choquet and Sugeno integrals. The definition of these fuzzy measures is based on the confidence values of the rules. In particular, one of them is a decomposable fuzzy measure in which the hierarchical structure of the FDT is exploited to find the values of the fuzzy measure. Out of this Ph.D. work, we have built a CDSS software that may be installed in the health care centres and hospitals in order to evaluate and detect Diabetic Retinopathy at early stages