Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both over space and time the variations of physical quantities such as temperature, humidity, light, or sound. <p><p>In environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.<p><p>An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.<p><p>This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data. <p><p>The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks. <p><p>/<p><p>Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.<p><p>Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie. <p><p>Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées. <p><p>Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.<p><p>Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements. <p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

The ever increasing diversity in communication systems has created a demand for constant improvements in receiver components. This thesis describes the design and characterisation of front-end receiver components for various challenging applications, including characterisation of low noise foundry processes, LNA design and multi-band antenna design. It also includes a new theoretical analysis of noise coupling in low noise phased array receivers.In LNA design much depends on the choice of the optimum active devices. A comprehensive survey of the performance of low noise transistors is therefore extremely beneficial. To this end a comparison of the DC, small-signal and noise behaviours of 10 state-of-the-art GaAs and InP based pHEMT and mHEMT low noise processes has been carried out. Their suitability in LNA designs has been determined, with emphasis on the SKA project. This work is part of the first known detailed investigation of this kind. Results indicate the superiority of mature GaAs-based pHEMT processes, and highlight problems associated with the studied mHEMT processes. Two of the more promising processes have then been used to design C-band and UHF-band MMIC LNAs. A new theoretical analysis of coupled noise between antenna elements of a low noise phased array receiver has been carried out. Results of the noise wave analysis, based on fundamental principles of noisy networks, suggest that the coupled noise contribution to system noise temperatures should be smaller than had previously been suggested for systems like the SKA. The principles are applicable to any phased array receiver. Finally, a multi-band antenna has been designed and fabricated for a severe operating environment, covering the three extremely crowded frequency bands, the 2.1 GHz UMTS, the 2.4 GHz ISM and the 5.8 GHz ISM bands. Measurements have demonstrated excellent performance, exceeding that of equivalent commercial antennas aimed at similar applications.

La tesis se enmarca en el estudio de las arquitecturas inteligentes de control distribuido y de los sistemas de comunicaciones empleados, más concretamente el trabajo se centra en la optimización del sistema de control por medio de la evaluación del rendimiento en el middleware a través de los parámetros de calidad de servicio y de la optimización del control empleando políticas de calidad de servicio. El principal objetivo de este trabajo ha sido estudiar, diseñar, desarrollar y evaluar una arquitectura de control distribuido, basándose en el estándar de comunicaciones Data-Distribution Service for Real-Time Systems (DDS) propuesto por la organización Object Management Group (OMG). Como aportación principal de la tesis se propone el diseño de una arquitectura distribuida de control inteligente que de soporte a la QoS, tanto en la medición por medio de los parámetros, como en la gestión por medio de las políticas de QoS. Las políticas deben permitir la variación de las características de la comunicación en función de los requisitos de control, expresados estos últimos por medio de los parámetros de QoC. A la arquitectura desarrollada se le ha llamado FSACtrl. Para determinar los requisitos de la arquitectura FSACtrl, se han estudiado las revisiones de los autores más relevantes acerca de las características más destacadas de las arquitecturas distribuidas de sistemas de control. A partir de estas características se han diseñado los elementos de la arquitectura FSACtrl. Los elementos que dan soporte a las comunicaciones se han basado en los del estándar DDS de la OMG, mientras que los elementos de control se han basado en el estándar Sensor Web Enablement (SWE) del Open Geospatial Consortium (OGC). Para la validación de la arquitectura se ha implementado un entorno de diseño y simulación del control. / Poza Luján, JL. (2012). Propuesta de arquitectura distribuida de control inteligente basada en políticas de calidad de servicio [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14674 / Palancia

Indiana University-Purdue University Indianapolis (IUPUI) / One of the most common goals of a wireless sensor network is to collect sensor data. The goal of this thesis is to provide an easy to use and energy-e fficient system for deploying data collection sensor networks. There are numerous challenges associated with deploying a wireless sensor network for collection of sensor data; among these challenges are reducing energy consumption and the fact that users interested in collecting data may not be familiar with software design. This thesis presents a complete system, comprised of the Compression Data-stream Protocol and a general gateway for data collection in wireless sensor networks, which attempts to provide an easy to use, energy efficient and complete system for data collection in sensor networks. The Compressed Data-stream Protocol is a transport layer compression protocol with a primary goal, in this work, to reduce energy consumption. Energy consumption of the radio in wireless sensor network nodes is expensive and the Com-pressed Data-stream Protocol has been shown in simulations to reduce energy used on transmission and reception by around 26%. The general gateway has been designed in such a way as to make customization simple without requiring vast knowledge of sensor networks and software development. This, along with the modular nature of the Compressed Data-stream Protocol, enables the creation of an easy to deploy and easy to configure sensor network for data collection. Findings show that individual components work well and that the system as a whole performs without errors. This system, the components of which will eventually be released as open source, provides a platform for researchers purely interested in the data gathered to deploy a sensor network without being restricted to specific vendors of hardware.

The use of Internet of Things systems has increased to meet the need for smart systems in various fields, such as smart homes, intelligent industries, medical systems, agriculture, and the military. IoT networks are expanding daily to include hundreds and thousands of IoT devices, which transmit information through other linked devices to reach the network sink or gateway. The information follows different routes to the network sink. Finding an ideal routing solution is a big challenge due to several factors, such as power, computation, storage, and memory limitation for IoT devices. In 2011, A new standardized routing protocol for low-power and lossy networks was released by the Internet Engineering task force (IETF). The IETF adopted a distance vector routing algorithm for the RPL protocol. RPL protocol utilizes the objective functions (OFs) to select the path depending on diffident metrics.These OFs with different metrics must be evaluated and tested to develop the best routing solution.This project aims to test the performance of standardized RPL objective functions in a simulation environment. Afterwards, a new objective function with a new metric will be implemented and tested in the same environmental conditions. The performance results of the standard objective functions and the newly implemented objective function will be analyzed and compared to evaluate whether the standard objective functions or the new objective function is better as a routing solution for the IoT devices network.

<p><strong>The Energy Monitoring Framework</strong>, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology.</p> <p>The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely.</p> <p>The main objectives of the project include the following:</p> <ul> <li>Set up a wireless network using sensors and smart implants with a base station/ controller.</li> <li>Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency.</li> <li>Set up a generalized interface to collect and process the sensor data values and store the data in a database.</li> <li>Design and develop a generic database compatible with various companies irrespective of the type and size.</li> <li> Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment.</li> </ul> <p>The General Structure of the project constitutes the following components:</p> <ul> <li>A wireless sensor network with a base station.</li> <li>An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators.</li> <li>A cloud that hosts a database and an API to collect and store information.</li> <li>A web application hosted in the cloud to provide an interactive platform for users to analyze the data.</li> </ul> <p>The project was demonstrated in:</p> <ul> <li>Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/).</li> <li>Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/).</li> <li>A company in Indiana.</li> </ul> <p>The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.</p> <p><br></p>