LAURA: LocAlization and Ubiquitous monitoRing of pAtients for health care support

This works illustrates the LAURA system, which performs localization, tracking and monitoring of patients hosted at nursing institutes by exploiting a wireless sensor network based on the IEEE 801.15.4 (Zigbee) standard. We focus on the indoor personal localization module, which leverages a method based on received signal strength measurements, together with a particle filter to perform tracking of moving patients. We discuss the implementation and dimensioning of the localization and tracking system using commercial hardware, and we test the LAURA system in real environment, both with static and moving patients, achieving an average localization error lower than 2 m in 80% of the cases. The data sets containing the real measurements of received signal strengths collected during the experiments are made publicly available to enable reproducible research

Indoor Navigation Ontology for Smartphone Semi- Automatic Self-Calibration Scenario

The indoor navigation within public environments and location-based service development are very interesting and promising tasks. This paper describes an ontology-based technique for human movement recognition using the hybrid indoor localization technique based on received signal strength multilateration and pedestrian dead reckoning which relies on internal smartphone sensors. This technique takes into account the anchor node proximity zones and using internal sensors performs the semi-automatic online calibration procedure of log- distance path loss propagation model in accordance with a certain semi-automatic self-calibration scenario. The usage of indoor navigation ontology allows to decrease the influence of radio signal obstructions induced by user's body and moving people

An experimental characterization of reservoir computing in ambient assisted living applications

In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling

Projeto de sistema de posicionamento indoor por análise de cena em rede IEEE 802.15.4.

TCC (graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia Elétrica.Apesar dos avanços na tecnologia GPS de posicionamento global, a localização em ambientes fechados ainda se mostra como um problema relevante. Tecnologias e técnicas distintas possuem vantagens em determinadas situações, porém nenhuma sobressai como solução definitiva. Algoritmos de localização por análise de cena, que mapeiam previamente os níveis de sinais do ambiente, vêm fornecendo ótimos resultados, sendo as principais soluções, atualmente, baseadas em redes IEEE 802.11. Em ambientes onde não há este tipo de infraestrutura de rede disponível, ou onde requisitos de custo, área de alcance e consumo de energia limitam a aplicação dessas redes, outras tecnologias devem ser utilizadas. Como alternativa, o presente trabalho apresenta um projeto de sistema de posicionamento indoor implementado em uma rede IEEE 802.15.4. O sistema proposto obteve bons resultados, apresentando erro médio de 0.6 m nas estimativas de posição em um cenário real. No entanto, apesar da boa precisão, foram identificados diversos desafios de implementação e escalabilidade do sistema.Despite advances in GPS technology for global positioning, indoor positioning still proves to be a significant problem. Different technologies and techniques have advantages in certain situations, but none stand out as a definitive solution. Scene analysis-based positioning algorithms, which previously maps the signal levels of the environment, have provided great results. Currently, the main solutions are based on IEEE 802.11 networks. In environments where this type of network infrastructure is not available, or where requirements for cost, power consumption and range limit the application of such networks, other technologies may be used. As an alternative, this paper presents the project of an indoor positioning system implemented in an IEEE 802.15.4 network. The proposed system obtained good results, presenting an average error of 0.6 m in the position estimates in a real scenario. However, despite the good accuracy, several implementation and scalability challenges were identified

Mise en place d’un service de géolocalisation au sein d’une plateforme d’exploitation d’un réseau de capteurs sans fil.

National audienceDans les réseaux de capteur sans-fil, les nœuds sondent l’environnement et perçoivent des grandeurs physiques telles que la température ou l’humidité. A ce titre, la position de ceux-ci est une information critique souvent utilisée à des fins de corrélation. A travers l’étude de trois algorithmes (moyenne pondérée, trilatération, multilatération), nous avons mis en place un module de géolocalisation basé sur l’indication de force du signal (RSSI). Cette fonctionnalité est intégrée dans une plateforme web JEE et le système embarqué est Contiki. Dans un premier temps, résultant d’une communication pair à pair, les RSSI sont collectés depuis le réseau, et routés jusqu’au sink. Par la suite, une passerelle transfère ces données au serveur qui se charge de leur traitement et enregistrement. Les résultats obtenus à partir des algorithmes de géolocalisation sont envoyés à l’interface utilisateur qui peut ainsi consulter les résultats et voir la topologie du réseau sur une carte. La comparaison des algorithmes nous amène, dans le meilleur des cas et pour différentes topologies, à une précision autour de 3m. Dans un but de réduction de la consommation d’énergie, nous avons mis en place un module de commandes. Celles-ci sont envoyées depuis l’interfaceweb et nous permettent de désactiver certains modules tels que la géolocalisation. S’inscrivant dans la tendance des Open Data, les données collectées sont mises à disposition via une API. Enfin, une interface d’administration permet la gestion de la plateforme web et la supervision du réseau de capteurs par l’intermédiaire d’envoi de commandes au sink relayées dans le reste du réseau

Sistemas de localização com base em tecnologias sem fios

Mestrado em Engenharia Electrónica e TelecomunicaçõesNo presente trabalho apresenta-se um sistema de localização que pretende em simultâneo trabalhar dentro de edifícios e fora deles (indoor e outdoor). Esta área aplicacional tem suscitado grande interesse actualmente. O caso de estudo é o de uma oficina automóvel. O documento descreve as soluções propostas, com base em rádio frequência, tendo-se optado por um sub-sistema baseado no padrão IEEE 802.15.4 para a localização indoor e por GPS e GPRS para o sistema de localização outdoor. Faz-se também referência a uma possível integração entre ambos. Na parte final do documento são apresentados alguns ensaios e resultados relativamente à parte outdoor.This document describes a location / tracking system that aims to simultaneously work within buildings and outside them (indoor and outdoor). This field of application has attracted a lot of interest nowadays. The case study is an automotive workshop. The document describes the solutions proposed, based on radio frequency which include a sub-system based on the IEEE 802.15.4 standard for indoor location, and GPS and GPRS for outdoor location / tracking. The document also discusses a possible integration between the two. At the end of the document some tests and results related to the outdoor part are also presented and briefly discussed.Micro I/O, Sistemas Electrónicos Lda.DRIVE - I

Ad-Hoc Personenlokalisierung in Drahtlosen Sensornetzwerken

In der Arbeit wird ein neues Konzept zur ad-hoc Personenlokalisierung entwickelt und untersucht. Ansätze aus dem Bereich der Lokalisierung in selbstkonfigurierenden, drahtlosen Sensornetzwerken sowie aus dem Bereich der inertialsensorbasierten Personennavigation werden verwendet und zu einem hybriden Lokalisierungsansatz kombiniert. Eine umfangreiche, experimentelle Studie wird durchgeführt. Im Ergebnis wird ein Ansatz aufgezeigt, wie sich Personen in ad-hoc Szenarien lokalisieren lassen

Reservoir Computing for Learning in Structured Domains

The study of learning models for direct processing complex data structures has gained an increasing interest within the Machine Learning (ML) community during the last decades. In this concern, efficiency, effectiveness and adaptivity of the ML models on large classes of data structures represent challenging and open research issues. The paradigm under consideration is Reservoir Computing (RC), a novel and extremely efficient methodology for modeling Recurrent Neural Networks (RNN) for adaptive sequence processing. RC comprises a number of different neural models, among which the Echo State Network (ESN) probably represents the most popular, used and studied one. Another research area of interest is represented by Recursive Neural Networks (RecNNs), constituting a class of neural network models recently proposed for dealing with hierarchical data structures directly. In this thesis the RC paradigm is investigated and suitably generalized in order to approach the problems arising from learning in structured domains. The research studies described in this thesis cover classes of data structures characterized by increasing complexity, from sequences, to trees and graphs structures. Accordingly, the research focus goes progressively from the analysis of standard ESNs for sequence processing, to the development of new models for trees and graphs structured domains. The analysis of ESNs for sequence processing addresses the interesting problem of identifying and characterizing the relevant factors which influence the reservoir dynamics and the ESN performance. Promising applications of ESNs in the emerging field of Ambient Assisted Living are also presented and discussed. Moving towards highly structured data representations, the ESN model is extended to deal with complex structures directly, resulting in the proposed TreeESN, which is suitable for domains comprising hierarchical structures, and Graph-ESN, which generalizes the approach to a large class of cyclic/acyclic directed/undirected labeled graphs. TreeESNs and GraphESNs represent both novel RC models for structured data and extremely efficient approaches for modeling RecNNs, eventually contributing to the definition of an RC framework for learning in structured domains. The problem of adaptively exploiting the state space in GraphESNs is also investigated, with specific regard to tasks in which input graphs are required to be mapped into flat vectorial outputs, resulting in the GraphESN-wnn and GraphESN-NG models. As a further point, the generalization performance of the proposed models is evaluated considering both artificial and complex real-world tasks from different application domains, including Chemistry, Toxicology and Document Processing