DISTRIBUTED CONSENSUS-BASED CALIBRATION OF NETWORKED CONTROL SYSTEMS

In this paper a new algorithm for distributed blind macro-calibration of Networked Control Systems is presented. It is assumed that the measured signal is stochastic and unknown. The algorithm is in the form of recursions of gradient type for estimation of the correction parameters for sensor gains and offsets. The recursion for gain correction is autonomous, derived from the measurement increments. The recursion for offset correction is based on differences between local measurements and utilizes the results of gain correction. It is proved that the algorithm provides asymptotic convergence to consensus in the sense that the corrected gains and offsets are equal for all sensors. It is demonstrated that the adopted structure of the algorithm enables obtaining high convergence rate, superior to the algorithms existing in the literature. Simulation results are provided illustrating the proposed algorithm properties

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Algoritmos para a detecção de drifting em sensores de fundo de poço

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2012Apresentam-se neste trabalho o desenvolvimento e a implementação de sistemas de monitoramento e validação de sensores de poços de petróleo, utilizando técnicas de aprendizado de máquina para a construção de modelos empíricos baseados em histórico de dados. Tais sistemas são fundamentados na redundância analítica dos sensores e o foco principal é a detecção de possíveis desvios nas leituras realizadas por esses dispositivos. Basicamente, o processo de validação é composto por três etapas: a predição das atuais leituras de sensores a partir de seus históricos de dados, a comparação entre as predições geradas e as correspondentes leituras dos sensores e, finalmente, a detecção de mudanças estatísticas nas propriedades dos resultados dessa comparação. Para verificar a aplicabilidade dos sistemas desenvolvidos, são realizados ensaios com diferentes conjuntos de dados: um conjunto de dados gerado a partir da simulação do modelo de um poço de petróleo e um conjunto de dados coletados de sensores de um poço real. De acordo com os resultados obtidos e as métricas de desempenho adotadas, o emprego de modelos empíricos baseados em histórico para a validação de sensores de poços de petróleo é promissor.Abstract : This work demonstrates the development and implementation of monitoring and validation systems for oil well sensors, using machine learning techniques to build data-drivenmodels of the behaviour of correlated sensors. These systems are based on the analytical redundancy of the sensors and the main objective is the calibration drift detection and isolation. Basically, the validation process is composed of three steps: the prediction of the sensors values given by the data-driven models, the comparison between the prediction and the actual sensor value, generating residuals, and, finally, the detection of abnormal changes in the statistical properties of the residuals. To verify the applicability of the developed systems, tests with two different datasets are performed: one dataset generated by the simulation of an oil well model and one dataset collected from sensors of a real oil well. Considering the obtained results and the performance metrics adopted, the use of data-driven models for validation of oil well sensors is promising

Calibration automatique d'un réseau de capteurs sans fil à effet Hall mesurant la consommation énergétique résidentielle

Un réseau de capteurs sans fil mesurant la consommation énergétique résidentielle de chacun des disjoncteurs du panneau d’alimentation principal d’une résidence est un outil très pratique pour un usager, mais un tel système peut s’avérer dispendieux. Afin de réduire le coût de ce produit, un réseau de capteurs à effet Hall est proposé en alternative aux traditionnels transformateurs de courants. Un système utilisant plusieurs capteurs à effet Hall est beaucoup moins coûteux et est plus facile d’installation grâce à son potentiel de miniaturisation. Par contre, la précision des mesures des capteurs à effet Hall n’est pas la même que celle des transformateurs de courants puisque sa sensibilité dépend de la proximité de celui-ci au fil qu’il mesure, ce qui cause alors des incertitudes au niveau du gain du capteur. Ce mémoire contribue à l’amélioration de la précision des mesures prises par les capteurs à effet Hall en proposant deux algorithmes numériques afin de faire la calibration des capteurs. Le premier algorithme est l’algorithme des moindres carrés (LMS) qui est simple et peut calibrer les capteurs sans présence de diaphonie tandis que le second est l’analyse en composantes indépendantes rapide (FastICA) qui est un algorithme plus complexe, mais en mesure d’atténuer le problème de diaphonie lorsqu’il est présent. Suite à la présentation du système et de la théorie liée aux algorithmes, les résultats des simulations et des expérimentations sont présentés pour les deux algorithmes. Pour les résultats de simulation avec LMS, l’erreur moyenne du courant mesuré par capteur pour 3 à 30 capteurs sans diaphonie et avec bruit est de 0,53 ARMS. Pour ce qui est de la combinaison de FastICA et LMS, l’erreur moyenne du courant mesuré avec diaphonie et avec bruit est de 0,55 ARMS pour 3 capteurs allant jusqu’à 0,83 ARMS pour 30 capteurs. Deux types d’installations sont testés pour les résultats expérimentaux. L’algorithme LMS pour les tests sans diaphonie est en mesure de réduire l’erreur moyenne par capteur pour 3 capteurs à 0,47 ARMS pour l’installation 1 (cafetière, fusil à air chaud et purificateur d’air) et à 0,49 ARMS pour l’installation 2 (cafetière, fusil à air chaud et plinthes contrôlées par un thermostat électronique). Puisque les mesures d’un des appareils utilisés pour l’installation 1 sont très bruitées, la combinaison de FastICA et LMS pour les tests avec diaphonie a seulement réduit l’erreur moyenne par capteur pour 3 capteurs à 3,16 ARMS. Par contre, l’erreur est réduite à 0,85 ARMS pour l’installation 2

Uma arquitetura para fusão de dados de sensores de baixo custo em redes de sensores sem fio

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2014.O sensoriamento de condições ambientais é frequentemente empregado nas áreas de Agricultura de Precisão e Engenharia Ambiental, como para o monitoramento climático, na detecção de pragas, na irrigação e análise do solo, nos mapas de produtividade e no monitoramento de áreas florestais e de rios urbanos. As Redes de Sensores sem Fio (RSSF) vêm sendo propostas como infraestruturas para essas aplicações. As referidas redes produzem um grande volume de dados. Contudo, como são comumente compostas por sensores de baixo custo e com baixa confiabilidade, eventualmente dados anômalos (outliers) são gerados, afetando a qualidade final do monitoramento. Essas condições implicam na necessidade da utilização de métodos de fusão de informações que viabilizem o funcionamento da rede e aumentem a confiança nos dados monitorados. Tendo em vista tais características, o presente trabalho propõe uma arquitetura para a fusão de informação voltada a sensores de baixo custo. A arquitetura foi validada através de um estudo de caso envolvendo sensores de pressão atmosférica, compatíveis com a plataforma Arduino, cujos dados produzidos foram tratados por técnicas de fusão de informação. Na avaliação dos resultados obtidos, dois sensores de alto custo foram utilizados para fornecerem valores de referência. Os resultados obtidos mostram que alguns dos métodos de fusão de baixo nível e técnicas para a detecção de outliers estudados neste trabalho, quando combinados e organizados segundo a arquitetura proposta, conseguem substituir um único sensor centralizado e de alto custo, mantendo a confiabilidade obtida nos dados monitorados.Abstract : Detection of environmental conditions is frequently employed in the fields of precision agriculture and environmental engineering, being also used in irrigation and soil testing, productivity maps, and the moni-toring of forest areas and urban rivers. Wireless Sensor Networks (WSN) have been proposed as an infrastructure for these applications. These networks produce a large volume of data. However, as they are often comprised of sensors of low cost and reliability, anomalous data (outliers) is occasionally produced, affecting the final quality of the monitoring. These conditions require using information fusion methods that make it possible for the network to function while increasing the reliability of monitored data. Considering these characteristics, this work proposes an architecture for information fusion aimed at low-cost sensors. The architecture was validated through a case study involving atmospheric pressure sensors compatible with the Arduino platform. The data they produced was processed with information fusion techniques. In the evaluation of the obtained results, two high-cost sensors were used to provide reference values. The obtained results show that, when combined and organized according to the proposed architecture, some of the low-level fusion methods and the outlier detection techniques studied in this work are able to replace a single centralized, high-cost sensor while maintaining reliability of the monitored data

A service orientated architecture and wireless sensor network approach applied to the measurement and visualisation of a micro injection moulding process. Design, development and testing of an ESB based micro injection moulding platform using Google Gadgets and business processes for the integration of disparate hardware systems on the factory shop floor

Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process