TFG: Diseño, desarrollo e implementación de un sistema de adquisición, almacenamiento y presentación de los datos obtenidos de una red de sensores inalámbricos
Posted on by b105

El objetivo de este Trabajo Fin de Grado es el diseño e implementación un sistema que adquiera, procese y almacene los datos obtenidos de la WSN y los presente a través de un servidor Web que permita consultar datos en tiempo real y en un histórico, así como envío de parámetros de control, con los que configurar la WSN.

El proyecto se basará en una red de sensores inalámbricos desarrollada de forma simultanea en otro Trabajo Fin de Grado, compuesta por dos tipos de nodos, Prometheus y Boucherot. Los nodos Prometheus se encargarán de medir valores como presencia y temperatura, además de estado de sus baterías, mientras que los Boucherot monitorizarán el consumo de todo dispositivo conectado a ellos. Asimismo, los nodos Boucherot también implementan una serie de actuadores que permiten el encendido y apagado de los aparatos conectados a los mismos. Esta red presenta además una serie de comandos que permiten configurar ciertos parámetros de medida de la red y del estado de sus nodos.

Para la implementación del sistema se ha recurrido a distintas herramientas:

  • Desarrollo de script en Python para adquisición, procesado y almacenamiento en base de datos. Así como el envío de comandos de control a la red inalámbrica. Se han empleado los módulos serial, sqlite3 y pynotify.
  • Desarrollo del servidor Web en Node.js, que sirve paginas con información de la red, información de las medidas en tiempo real y en un histórico, con módulos: socket.io, sqlite3, http-auth entre otros.
  • Diseño de las paginas web que se muestran en el cliente basadas en distintos frameworks como: Bootstrap 3, graficas de HighCharts, y tablas con Datatables y jQuery.

A continuación se muestra una breve descripción de la interfaz del sistema con el usuario, que se realiza a través de una serie de paginas web:

DOMOLabo B105_TrealPágina que muestra dinámicamente las medidas en Tiempo Real tomadas por la WSN

DOMOLabo B105_Hist

Página que muestra Histórico de las medidas tomadas por la WSN

Ambas páginas, constan de una serie de gráficas que muestran las medidas tomadas por la WSN. Cada gráfica agrupa a todos los sensores de un tipo y permite seleccionar los nodos que se desean visualizar en la leyenda. Además permite hacer zoom en la gráfica, bien seleccionando sobre ella o bien pulsando alguno de los botones de la esquina superior izquierda de la gráfica. También es posible exportar datos en distintos formatos, .pdf, .png, .svg, etc. gracias al botón situado en la esquina superior derecha.

DOMOLabo B105_Sensores

Página que muestra información y permite el control de la WSN

Esta pagina consta de una tabla principal donde se muestra información de todos los nodos de la red (identificadores, tipos de sensores presentes, localización del sensor y estado de la batería y de sus actuadores). En la parte inferior de la tabla se encuentra un formulario que permite añadir nuevos sensores al sistema.

En la parte superior de la tabla se presenta un conjunto de botones que permiten el envío de una serie de comandos de control a la red (Relé, Configurar el tiempo que un nodo permanece dormido y en estado activo, actuar sobre el relé y/o los leds, etc.). Estos comandos se envían al nodo AP de la red que se encarga de enviarlos al nodo que corresponda.

También se ha implementado una autenticación de usuarios, para el control de acceso a funciones de configuración de la red y del sistema. Para los usuarios no administradores el aspecto es ligeramente diferente al presentado, ya que las funciones de control están desactivadas y no se permite la incorporación de nuevos sensores al sistema. Sin embargo la tabla es visible y se permite como en el caso anterior consultar e imprimir el estado de la red.

Se ha tenido especial interés en implementar un sistema modular, en el cual la caída de un modulo no imposibilite el normal funcionamiento del resto. Escalable, donde se puedan gestionar múltiples peticiones simultaneas de usuarios con distintos dispositivos y necesidades de consulta. Primando también la versatilidad del sistema respecto a la red de la que se adquieran los datos.

El sistema se ha dimensionado ampliamente para soportar una red con mas de 100 sensores y almacenar datos durante varias décadas, con tiempos de medida de 1 minuto para los sensores.

 

Thesis Proposal: Methodology for Implementation of Synchronization Strategies for Wireless Sensor Networks
Posted on by frta

 

Author: Francisco Tirado-Andrés

Advisor: Alvaro Araujo Pinto

Synopsis:
Wireless Sensor Networks (WSN) are networks composed of a large number of small devices that take measurements, process them, and communicate with other devices coordinating their operations. Time synchronization is necessary for that coordination of actions.

Multiple features characterized a WSN. Some of them are Power Consumption, Cost, Network type, Security, Data throughput, Scalability, etc.

WSNs bring us many benefits over traditional wired networks, but they also add difficulties to counteract its limitations.

The functionality of a WSN is very specific to the problem it solves. It is therefore that no single synchronization method is optimal along all axes. Unnecessary synchronization wastes resources; insufficient synchronization leads to poor application performance.

The requirements that are entailed to the various parameters that define the synchronization protocol will come imposed by the specific application to which it is oriented.

Because, it is not the same an application for a distributed humidity control in a natural park where each sample is collected every half hour and synchronization may deviate seconds without affecting the results, that the conditions required for an application of Wireless Surround Sound System where real-time operations and very small deviations are needed for a proper operation of the system.

But today there is no methodology that helps to design or configuration. Neither with the synchronization protocols nor the general system parameters.

There are many difficulties to be resolved because the synchronization protocol must meet not only the requirements of the application for which is designed, but also the intrinsic demands of WSNs.

One application where the results are very dependent on the accuracy of timing synchronization is Structural Health Monitoring (SHM). A configurable protocol which is able to adapt itself to the requirements of the application and the requirements of the system will be more useful and it will be ready for future applications and requirements.

My intention is to contribute, both during, and at the end of this thesis, with a methodology to guide and help implement synchronization protocols in Wireless Sensor Networks. Always keeping in mind that the synchronization protocol must meet requirements of accuracy and precision at the same time should not interfere with the performance of other tasks in the system. In that way the user will be able to adapt the configuration of the system and the parameters to get a productive WSN.

BackToTheFuture-Synchronization
Movie: Back To the Future. 23’17”

Cognitive Wireless Sensor Network Platform for Cooperative Communications
Posted on by b105

Title: Cognitive Wireless Sensor Network Platform for Cooperative Communications
Authors: Agustín Tena, Guillermo Jara, Juan Domingo, Elena Romero, Alvaro Araujo
Published in: International Journal of Distributed Sensor Networks
Date of Publication: January 2014
Digital Object Identifier : 10.1155/2014/473905
Web: http://www.hindawi.com/journals/ijdsn/2014/473905/

Nowadays, Wireless Ad-Hoc Sensor Networks (WAHSNs), specially limited in energy and resources, are subject to development constraints and difficulties such as the increasing Radio Frequency (RF) spectrum saturation at the unlicensed bands. Cognitive Wireless Sensor Networks (CWSNs), leaning on a cooperative communication model, develop new strategies to mitigate the inefficient use of the spectrum that WAHSNs face. However, few and poorly featured platforms allow their study due to their early research stage.

This paper presents a versatile platform that brings together cognitive properties into WAHSNs. It combines hardware and software modules as an entire instrument to investigate CWSNs. The hardware fits WAHSN requirements in terms of size, cost, features, and energy. It allows communication over three different RF bands, becoming the first cognitive platform for WAHSNs with this capability. In addition, its modular and scalable design is widely adaptable to almost any WAHSN application.

Significant features such as Radio Interface (RI) agility or energy consumption have been proved throughout different performance tests.

 

nodo

PUE Attack Detection in CWSN Using Collaboration and Learning Behavior
Posted on by jblesa

Title: PUE Attack Detection in CWSN Using Collaboration and Learning Behavior
Authors: Javier Blesa, Elena Romero, Alba Rozas, Alvaro Araujo and Octavio Nieto-Taladriz
Published in: International Journal of Distributed Sensor Networks
Date of Publication: June 2013
Digital Object Identifier : 10.1155/2013/815959
Web: http://www.hindawi.com/journals/ijdsn/2013/815959/

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives.

cognitive radio module

 

PUE attack detection in CWSNs using anomaly detection techniques
Posted on by jblesa

Title: PUE attack detection in CWSNs using anomaly detection techniques
Authors: Javier Blesa, Elena Romero, Alba Rozas and Alvaro Araujo
Published in: EURASIP Journal on Wireless Communications and Networking 
Date of Publication: September 2013
Digital Object Identifier : 10.1186/1687-1499-2013-215
Web: http://jwcn.eurasipjournals.com/content/2013/1/215

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.

clusters