Estimation of Spatial Fields of Nlos/Los Conditions for Improved Localization in Indoor Environments

A major challenge in indoor localization is the presence or absence of line-of-sight (LOS). The absence of LOS, denoted as non-line-of-sight (NLOS), directly affects the accuracy of any localization algorithm because of the induced bias in ranging. The estimation of the spatial distribution of NLOS-induced ranging bias in indoor environments remains a major challenge. In this paper, we propose a novel crowd-based Bayesian learning approach to the estimation of bias fields caused by LOS/NLOS conditions. The proposed method is based on the concept of Gaussian processes and exploits numerous measurements. The performance of the method is demonstrated with extensive experiments

A Positioning Scheme Combining Location Tracking with Vision Assisting for Wireless Sensor Networks

This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF)with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedureis employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assistedcalibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracyenhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments.Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KFbasedapproach with the landmark information can calibrate the location estimation and reduce the corner effect of alocation-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimentalresults demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in aZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, theproposed algorithm can achieve reasonably good performance

Application independent in location tracking framework

Due to significant popularity of location-based services and multimedia communication over mobile devices, many researches have been conducted to extend the features of location tracking and make it cost effective to users. This research focuses on the performance of an indoor location tracking system on IPv6 network island with multiple real time applications that has location assisted session transfer feature for mobile users. Received signal strength Indicator mechanism has been used to locate the moving nodes. This research involved the development of location tracking server that monitors the dynamic and centralised MySQL database management system. Session initial protocols user agent has been used to deploy intercommunicating of multimedia data such as video and audio conference, text messaging among the moving nodes and users are able to transfer the multimedia sessions seamlessly to their nearest mobile nodes which will be determined by the location server. This study, thus, presents the variation of location tracking accuracy of triangulation system and fingerprint system on different indoor surroundings to compare the performance of their location tracking accuracy. Two indoor positioning systems, triangulation method (TM) and fingerprint method (FPM) were implemented and experiments were successfully conducted in different large area and small area scenarios of indoor environment. FPM experiments were examined into two sections: FPM database with data redundancy and FPM database without data redundancy. FPM database without data redundancy achieved 94.287% tracking accuracy which is the highest comparing to the FPM database with data redundancy and TM

Multistatic radar optimization for radar sensor network applications

The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities. In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology. The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric

A supporting infrastructure for Wireless Sensor Networks in Critical Industrial Environments

Tese de doutoramento no Programa de Doutoramento em Ciências e Tecnologias da Informação apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.As Redes de Sensores Sem Fios (RSSFs) têm uma aplicabilidade muito elevada nas mais diversas áreas, como na indústria, nos sistemas militares, na saúde e nas casas inteligentes. No entanto, continuam a existir várias limitações que impedem que esta tecnologia tenha uma utilização extensiva. A fiabilidade é uma destas principais limitações que tem atrasado a adopção das RSSFs em ambientes industriais, principalmente quando sujeitos a elevadas interferências e ruídos. Por outro lado, a interoperabilidade é também um dos principais requisitos a cumprir nomeadamente com o avanço para o paradigma da Internet of Things. A determinação da localização dos nós, principalmente dos nós móveis, é, também ele, um requisito crítico em muitas aplicações. Esta tese de doutoramento propõe novas soluções para a integração e para a localização de RSSFs que operem em ambientes industriais e críticos. Como os nós sensores são, na maioria das vezes, instalados e deixados sem intervenção humana durante longos períodos de tempo, isto é, meses ou mesmo anos, é muito importante oferecer processos de comunicação fiável. No entanto, muitos problemas ocorrem durante a transmissão dos pacotes, nomeadamente devido a ruídos, interferências e perda de potência do sinal. A razão das interferências deve-se à existência de mais do que uma rede ou ao espalhamento espectral que ocorre em determinadas frequências. Este tipo de problemas é mais severo em ambientes dinâmicos nos quais novas fontes de ruído pode ser introduzidas em qualquer instante de tempo, nomeadamente com a chegadas de novos dispositivos ao meio. Consequentemente, é necessário que as RSSFs tenham a capacidade de lidar com as limitações e as falhas nos processos de comunicação. O protocolo Dynamic MAC (DunMAC) proposto nesta dissertação utiliza técnicas de rádio cognitivo (CR) para que a RSSF se adapte, de forma dinâmica, a ambientes instáveis e ruidosos através da selecção automática do melhor canal durante o período de operação. As RSSFs não podem operar em isolação completa do meio, e necessitam de ser monitoradas e controladas por aplicações externas. Apesar de ser possível adicionar a pilha protocolar IP aos nós sensores, este procedimento não é adequado para muitas aplicações. Para estes casos, os modelos baseados em gateway ou proxies continuam a apresentar-se preferíveis para o processo de integração. Um dos desafios existentes para estes processos de integração é a sua adaptabilidade, isto é, a capacidade da gateway ou do proxy poder ser reutilizado sem alterações por outras aplicações. A razão desta limitação deve-se aos consumidores finais dos dados serem aplicações e não seres humanos. Logo, é difícil ou mesmo impossível criar normas para as estruturas de dados dada a infinidade de diferentes formatos. É então desejável encontrar uma solução que permita uma integração transparente de diferentes RSSFs e aplicações. A linguagem Sensor Traffic Description Language (STDL) proposta nesta dissertação propõe uma solução para esta integração através de gateways e proxies flexíveis e adaptados à diversidade de aplicações, e sem recorrer à reprogramação. O conhecimento da posição dos nós sensores é, também ele, crítico em muitas aplicações industriais como no controlo da deslocação dos objectos ou trabalhadores. Para além do mais, a maioria dos valores recolhidos dos sensores só são úteis quando acompanhados pelo conhecimento do local onde esses valores foram recolhidos. O Global Positioning Systems (GPS) é a mais conhecida solução para a determinação da localização. No entanto, o recurso ao GPS em cada nó sensor continua a ser energeticamente ineficiente e impraticável devido aos custos associados. Para além disso, os sistemas GPS não são apropriados para ambientes in-door. Este trabalho de doutoramento propõe-se actuar nestas áreas. Em particular, é proposto, implementado e avaliado o protocolo DynMAC para oferecer fiabilidade às RSSFs. Para a segunda temática, a linguagem STDL e o seu motor são propostos para suportar a integração de ambientes heterogéneos de RSSFs e aplicações. As soluções propostas não requerem reprogramação e suportam também serviços de localização nas RSSFs. Diferentes métodos de localização foram avaliados para estimar a localização dos nós. Assim, com estes métodos as RSSFs podem ser usadas como componentes para integrar e suportar a Futura Internet. Todas as soluções propostas nesta tese foram implementadas e validadas tanto em simulação com em plataformas práticas, laboratoriais e industriais.The Wireless Sensor Network (WSN) has a countless number of applications in almost all of the fields including military, industrial, healthcare, and smart home environments. However, there are several problems that prevent the widespread of sensor networks in real situations. Among them, the reliability of communication especially in noisy industrial environments is difficult to guarantee. In addition, interoperability between the sensor networks and external applications is also a challenge. Moreover, determining the position of nodes, particularly mobile nodes, is a critical requirement in many types of applications. My original contributions in this thesis include reliable communication, integration, localization solutions for WSNs operating in industrial and critical environments. Because sensor nodes are usually deployed and kept unattended without human intervention for a long duration, e.g. months or even years, it is a crucial requirement to provide the reliable communication for the WSNs. However, many problems arise during packet transmission and are related to the transmission medium (e.g. signal path-loss, noise and interference). Interference happens due to the existence of more than one network or by the spectral spread that happens in some frequencies. This type of problem is more severe in dynamic environments in which noise sources can be introduced at any time or new networks and devices that interfere with the existing one may be added. Consequently, it is necessary for the WSNs to have the ability to deal with the communication failures. The Dynamic MAC (DynMAC) protocol proposed in this thesis employs the Cognitive Radio (CR) techniques to allow the WSNs to adapt to the dynamic noisy environments by automatically selecting the best channel during its operation time. The WSN usually cannot operate in complete isolation, but it needs to be monitored, controlled and visualized by external applications. Although it is possible to add an IP protocol stack to sensor nodes, this approach is not appropriate for many types of WSNs. Consequently, the proxy and gateway approach is still a preferred method for integrating sensor networks with external networks and applications. The problem of the current integration solutions for WSNs is the adaptability, i.e., the ability of the gateway or proxy developed for one sensor network to be reused, unchanged, for others which have different types of applications and data frames. One reason behind this problem is that it is difficult or even impossible to create a standard for the structure of data inside the frame because there are such a huge number of possible formats. Consequently, it is necessary to have an adaptable solution for easily and transparently integrating WSNs and application environments. In this thesis, the Sensor Traffic Description Language (STDL) was proposed for describing the structure of the sensor networks’ data frames, allowing the framework to be adapted to a diversity of protocols and applications without reprogramming. The positions of sensor nodes are critical in many types of industrial applications such as object tracking, location-aware services, worker or patient tracking, etc. In addition, the sensed data is meaningless without the knowledge of where it is obtained. Perhaps the most well-known location-sensing system is the Global Positioning System (GPS). However, equipping GPS sensor for each sensor node is inefficient or unfeasible for most of the cases because of its energy consumption and cost. In addition, GPS is not appropriate in some environments, e.g., indoors. Similar to the original concept of WSNs, the localization solution should also be cheap and with low power consumption. This thesis aims to deal with the above problems. In particular, in order to add the reliability for WSN, DynMAC protocol was proposed, implemented and evaluated. This protocol adds a mechanism to automatically deal with the noisy and changeable environments. For the second problem, the STDL and its engine provide the adaptable capability to the framework for interoperation between sensor networks and external applications. The proposed framework requires no reprogramming when deploying it for new applications and protocols of WSNs. Moreover, the framework also supports localization services for positioning the unknown position sensor nodes in WSNs. The different localization methods are employed to estimate the location of mobile nodes. With the proposed framework, WSNs can be used as plug and play components for integrating with the Future Internet. All the proposed solutions were implemented and validated using simulation and real testbeds in both the laboratory and industrial environments

Human-in-the-Loop Cyber-Physical-Systems based on Smartphones

Tese de doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de CoimbraTechnological devices increasingly become smaller, more mobile, powerful and efficient. However, each time we have to hurdle through unintuitive menus, errors and incompatibilities we become stressed by our technology. As first put forward by the renowned computer scientist Mark Weiser, the ultimate form of computers may be an extension of our subconscious. The ideal computer would be capable of truly understanding people's unconscious actions and desires. Instead of humans adapting to technology and learning how to use it, it would be technology that would adapt to the disposition and uniqueness of each human being. This thesis focuses on the realm of Human-in-the-loop Cyber-Physical Systems (HiTLCPSs). HiTLCPSs infer the users’ intents, psychological states, emotions and actions, using this information to determine the system's behavior. This involves using a large variety of sensors and mobile devices to monitor and evaluate human nature. Therefore, this technology has strong ties with wireless sensor networks, robotics, machine-learning and the Internet of Things. In particular, our work focuses on the usage of smartphones within these systems. It begins by describing a framework to understand the principles and theory of HiTLCPSs. It provides some insights into current research being done on this topic, its challenges, and requirements. Another of the thesis' objectives is to present our innovative taxonomy of human roles, where we attempt to understand how a human may interact with HiTLCPSs and how to best explore this resource. This thesis also describes concrete examples of the practical usage of HiTL paradigms. As such, we included a comprehensive description of our research work and associated prototypes, where the major theoretical concepts behind HiTLCPS were applied and evaluated to specific scenarios. Finally, we discuss our personal view on the future and evolution of these systems.A tecnologia tem vindo a tornar-se cada vez mais pequena, móvel, poderosa e eficiente. No entanto, lidar com menus pouco intuitivos, erros, e incompatibilidades, causa frustração aos seus utilizadores. Segundo o reconhecido cientista Mark Weiser, os computadores do futuro poderão vir a existir como se fossem uma extensão do nosso subconsciente. O computador ideal seria capaz de entender, em toda a sua plenitude, as ações e os desejos inconscientes dos seres humanos. Em vez de serem os humanos a adaptarem-se à tecnologia e a aprender a usá-la, seria a tecnologia a aprender a adaptar-se à disposição e individualidade de cada ser humano. Esta tese foca-se na área dos Human-in-the-loop Cyber-Physical Systems (HiTLCPSs). Os HiTLCPSs inferem as intenções, estados psicológicos, emoções e ações dos seus utilizadores, usando esta informação para determinar o comportamento do sistema ciber-físico. Isto envolve a utilização de uma grande variedade de sensores e dispositivos móveis que monitorizam e avaliam a natureza humana. Assim sendo, esta tecnologia tem fortes ligações com redes de sensores sem fios, robótica, algoritmos de aprendizagem de máquina e a Internet das Coisas. Em particular, o nosso trabalho focou-se na utilização de smartphones dentro destes sistemas. Começamos por descrever uma estrutura para compreender os princípios e teoria associados aos HiTLCPSs. Esta análise permitiu-nos adquirir alguma clareza sobre a investigação a ser feita sobre este tópico, e sobre os seus desafios e requisitos. Outro dos objetivos desta tese é o de apresentar a nossa inovadora taxonomia sobre os papeis do ser humano nos HiTLCPSs, onde tentamos perceber as possíveis interações do ser humano com estes sistemas e as melhores formas de explorar este recurso. Esta tese também descreve exemplos concretos da utilização prática dos paradigmas HiTL. Desta forma, incluímos uma descrição do nosso trabalho experimental e dos protótipos que lhe estão associados, onde os conceitos teóricos dos HiTLCPSs foram aplicados e avaliados em diversos casos de estudo. Por fim, apresentamos a nossa perspetiva pessoal sobre o futuro e evolução destes sistemas.Fundação Luso-Americana para o DesenvolvimentoFP7-ICT-2007-2 GINSENG projectiCIS project (CENTRO-07-ST24-FEDER-002003)SOCIALITE project (PTDC/EEI-SCR/2072/2014

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method