Cognitive Test-bed for Wireless Sensor Networks
Posted on by jblesa

Title: Cognitive Test-bed for Wireless Sensor Networks
Authors: Elena Romero, Javier Blesa, Agustín Tena, Guillermo Jara, Juan Domingo and Alvaro Araujo
Published in: IEEE DySPAN 2014
Date of Publication: March 2014
Web: http://dyspan2014.ieee-dyspan.org/

dyspanCognitive Wireless Sensor Networks are an emerging technology with a vast potential to avoid traditional wireless problems such as reliability, interferences and spectrum scarcity in Wireless Sensor Networks.

Cognitive Wireless Sensor Networks test-beds are an important tool for future developments,protocol strategy testing and algorithm optimization in real scenarios. A new cognitive test-bed for Cognitive Wireless Sensor Networks is presented in this paper. This work in progress includes the design of both a cognitive simulator for networks with high number of nodes and the implementation of a new platform with three wireless interfaces and a cognitive software for extracting real data.

Finally, as a future work, a remote programmable system and the planning for the physical deployment of the nodes at the university building is presented.

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.

 

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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

 

RS – Rehabilitación Sostenible de edificios
Posted on by b105
  • 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