A high mobility Command and Control Center a the Brigadier General (OF-6) level (PCBRI) contains from twenty to thirty operator workstations. Each workstation consists on a computer connected to SIMACET (Command and Control Spanish Army digital network) and a telephony terminal. According to the PCBRI layout it supposes between three and five kilometers of signal wire.
Each time the PCBRI moves from one location to another one means removing and recabling several kilometers of wire and about three hundred wire connections. On the other hand beyond the work, personnel and material the main problem is the unavailability of the Command and Control Center between jumps and the connections reliability.
The development of a new wireless Command and Control Center was considered as a good challenge for applying “Human-Centerd Design” methodologies and the “Brigada Guadarrama XII” created a working group acting as final user, the Research Group “B105 Electronic Systems Lab” (Universidad Politécnica de Madrid) as technological and methodological partner, the Colegio Universitario de la Defensa (Zaragoza) both as technological and educational partner, the company Teldat and the Escuela Politécnica Superior del Ejército de Tierra as observer for, applying this methodology, develop a prototype to work inside of the PCBRI without wires.
The results of this project, apart of the experience of applying Human-Centered Design and SCRUM methodologies within the Spanish Army, is the development of a prototype of a high mobility Command and Control Center that copes with the demands of the Brigade where time and effort have been fully controlled. Main features are:
Phantom Digital system over the SIMACET network (The developed system is transparent to the legacy network)
VoIP phantom system supported by an smart switchboard allowing intelligent routing and voice recording running over a legacy NAVARRA station
Electromagnetic shielding with several choices in the binomial cost-attenuation according the mission requirements
El pasado 23 de julio se llevaron a cabo las prelecturas de tesis de Elena Romero y la mía. La tesis de Elena titulada Cognitive strategies for reducing energy consumption in Wireless Sensor Networks se centra en el ahorro energético de las WSNs utilizando características cognitivas como el sensado del espectro o la adaptación al medio. Mi tesis titulada Cognitive based strategies for security in Wireless Sensor Networks busca mejorar la seguridad de las WSNs por medio de estrategias cognitivas. Las dos tesis están englobadas en la línea de investigación de radio cognitiva del grupo B105-Electronic System Lab.
La prelectura de tesis es un requisito del departamento de ingeniería electrónica de cara a asegurar una calidad de las tesis leídas. En nuestro caso el tribunal de la prelectura estuvo formado por Octavio Nieto-Taladriz, Lourdes Peñalver y Gonzalo Vázquez. Los tres han trabajado durante bastantes años en el área de WSNs y radio cognitiva. Sus aportes fueron interesantes de cara a mejorar las dos tesis y poder leer en breve. Sin duda es un gran paso para la consolidación de la línea de investigación y para el grupo.
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
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.
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
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.