PROYECTO: Quitanieves

 

  • Title: Desarrollo de una herramienta TIC para la mejora de la gestión de los vehículos de vialidad invernal.
  • Funding Organisation: Ministry of Industry, Energy and Tourism.
  • Participants: Valoriza Infraestructuras, LSI-UPM.
  • Description: Improve the snowplow operation enhancing safety and efficiency.
  • Tasks:
    Design and implementation of a fully functional ICT tool for snowplows based on real-time data acquisition.
    Development of a Wireless Sensor Network capable of providing real-time road conditions (humidity, salinity, temperature…) and also context information (altitude, wind speed and air temperature…) to increase the safety of snowplows.
    Implementation of a intelligent system able to offer the driver the best approach in every moment and situation to aid decision making.

Snow Plow Truck Retro

PUE Attack Detection in CWSN Using Collaboration and Learning Behavior

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

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

 

RS – Rehabilitación Sostenible de edificios

  • Title: Rehabilitación Sostenible de edificios (RS)rs
  • Funding Organization: FCC via Centro para el Desarrollo Tecnológico Industrial (CDTI) – Ministerio de Ciencia e Innovación
  • Participants:
    • Companies: FCC, URSA, METALES EXTRUIDOS, ENERGESIS and OPLAN.
    • Research Centers: Universidad de Málaga (UM), Universidad de Sevilla (US), Universidad Politécnica de Madrid (UPM) and Instituto Eduardo Torroja.
  • Description: The main objective of the RS project is the development of an integrated system to achieve an improvement in energy efficiency in the sustainable rehabilitation of existing buildings. Related topics are smart power management, thermal control and renewable energy systems. LSI-B105 at UPM is in charge of monitoring power consumption and temperature at home, gathering user data and preferences and designing energy-efficient strategies for warming the different rooms.

RS_process

PROMETEO: Tecnologías para el combate integral contra incendios forestales y para la conservación de nuestros bosques

prometeo

  • Title: Tecnologías para el combate integral contra incendios forestales y para la conservación de nuestros bosques.
  • Funding Organization: ISDEFE through the Centro para el Desarrollo Tecnológico Industrial (CDTI). Ministerio de Ciencia e Innovación.
  • Participants:
    • Companies: Inaer Helicópteros, Hispasat, Indra Espacio, Indra Software Labs, Deimos Space Elecnor, Telvent Sneider, Expace Maxam, Geacam, Vaersa, Isdefe, Inaer Maintenance, Aries Ingeniería, Tecnosylva, Brainstorm, Innovatec.
    • Research Centers: Fundación Robotiker-Tecnalia, Universidad de Córdoba (UCO), Universidad de Salamanca (USAL), Universidad Politécnica de Madrid (UPM), Universidad Politécnica de Cataluña (UPC), Universidad Politécnica de Valencia (UPV), Universidad de Castilla La Mancha (UCLM), Universidad de Santiago de Compostela, Instituto Tecnológico de Informática (ITI), Instituto Nacional de Técnica Aeroespacial (INTA), Laboratorio de Teledetección de la Universidad de Valladolid (LATUV), Fundación Centro de Supercomputación de Castilla y León (FCSCL), Fundación General del Medio Ambiente de Castilla La Mancha (FCGM), Organismo de Investigación Fundación Andaluza para el Desarrollo Aeroespacial (FADA-CATEC).
  • Description: PROMETEO is the biggest applied research project awarded to a corporate consortium in Spain, concerning the fight against forest fires. This project aims at safeguarding the conservation of our forests, optimizing the public administrations’ resources allocated for this purpose, by means of the development of new technologies that would allow us to minimize the forest fire hazard and mitigate the environmental damage in case of fire. Our group is responsible for the development of a wireless sensor network, to be deployed in high risk environments, with the objective of preventing and eventually helping in the extinction of forest fires.

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