Synopsis: Due to the spectrum scarcity problem, mostly in license-free ISM bands, and the forecasts regarding the increasing adoption of wireless communications, especially in scenarios like cities, it is essential to optimize the use of the spectrum to ensure the proper functioning of services and devices in the near future.
As the characteristics of the spectrum, by their own physical nature and its use, are very dynamic and vary constantly, devices must be able to intelligently adapt to these changes, as the Cognitive Radio paradigm proposes. Moreover, this adaptation should be done quickly in order to be effective and it should minimize the impact on the use of the spectrum.
Because of that, this work is going to be mainly focused on the development and evaluation of cognitive strategies with zero or minimum communication overhead. In other words, the aim of the research is to evaluate the degree of optimization of resources that can be achieved in a Cognitive Wireless Sensor Network (CWSN) by doing the cognitive cycle (spectrum sensing, learning and adaptation) mostly at node-level. To better exploit the cognitive radio capabilities of these networks, and thanks to the current development of wireless and processing technology, Software-Defined Radio (SDR) techniques are going to be used in sensor nodes for that purpose. This approach supposes a new paradigm in CWSNs which implies new challenges to be faced.
At this point, it appears to be necessary to evaluate some issues about the future of wireless communications. Will someday the need for cognition to use the spectrum outweigh the current energy constraints? In other words, will it be possible to achieve efficient and reliable wireless communication without cognitive capabilities in the near future? Answering this question will reveal whether it still make sense to compare the power consumption of SDR solutions with other platforms based on COTS radio transceivers or, conversely, the addition of cognitive capabilities will cease to pose a challenge to maximize systems’ efficiency and become a key point for their proper operation.
El objetivo de este Proyecto Fin de Carrera es el despliegue de un banco de pruebas para una red de sensores cognitiva (CWSN). Esta red contará con varios nodos cognitivos que permitirán la prueba de estrategias de optimización en este tipo de redes. Este banco de pruebas se realizará contando con una serie de nodos cognitivos previamente desarrollados en el laboratorio (cNGD) sobre el que se han hecho varios desarrollos software para adaptar tanto el protocolo de comunicación radio como la arquitectura cognitiva.
El despliegue del banco de pruebas cubrirá todas las salas permitidas del laboratorio B105 y el Departamento de Ingeniería Electrónica. Este proyecto abarca tanto la planificación del montaje físico de los nodos como el desarrollo de una interfaz para la gestión y recolección de información del banco de pruebas. Algunos parámetros a tener en cuenta serán el alcance de los nodos, su accesibilidad o la fuente de alimentación.
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
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.
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.
The objective of this project is to design and develop a software architecture that will be able to implement cognitive strategies in nodes to conform a CWSN.
The main model this architecture follows is that one proposed in the Connectivity Brokerage (Jan Rabaey, Adam Wolisz, Ali Ozer Ercan, Alvaro Araujo, Fred Burghardt, Samah Mustafa, Arash Parsa, Sofie Pollin, I-Hsiang Wang, Pedro Malagon 2010) and is represented as follows:
In the figure above six modules are shown inside the CAgent (Cognitive Agent). Each of this modules play an specific role inside the Cognitive Module and the work of all of them makes possible the execution of the Cognitive Cycle as defined in Cognitive Networks (Ryan W. Thomas, Luiz A. DaSilva, Allen B. MacKenzie 2005) which exposes that: “A cognitive network has a cognitive process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals.”.
Synopsis: Global data traffic in telecommunication annually grows with a rate higher than 50%. While the growth in traffic is stunning, the rapid adoption of wireless technology over the complete globe and the penetration through all layers of society is even more amazing. Over the span of 20 years, wireless subscription has risen to 40% of the world population, and is expected to grow to 70% by 2015. Overall mobile data traffic is expected to grow to 6.3 exabytes per month by 2015, a 26-fold increase over 2010. This expansion leads to an increase of the energy consumption by approximately 10% per year. A major portion of this expanding traffic has been migrating to mobile networks and systems. Due to this growth in wireless data traffic, the associated consumption to it becomes very important. Up to now, wireless network power consumption has not been an important issue because it was insignificant in comparison with wired network consumption. Nevertheless, over the recent years, wireless and mobile communications are increasingly becoming popular with consumer. Take into account the wireless traffic prediction the current rate of power consumption per unit of data cannot be sustained.
One of the most important trends related with the expansion of wireless networks is the significant increase of ubiquitous computing. WSNs give technological solution to this challenge, so its growth is closely linked to these data. Typical ubiquitous applications include security and surveillance (sensor nodes and video streams transmitted by Wi-Fi), health care (medical information transmitted by sensor nodes) or vehicular networks. Due to the number of nodes, its wireless nature, and its deployment in difficult access areas, WSN nodes should not require any maintenance. In terms of consumption this means that the sensors must be energetically autonomous and therefore the batteries cannot be changed or recharged.
The increasing demand for wireless communication presents an efficient spectrum utilization challenge. To address this challenge, Cognitive Radio (CR) has emerged as the key technology, which enables opportunistic access to the spectrum. In this way, the cooperation between devices introducing by CR regarding information sharing and taking decisions allows better spectrum use, lower energy consumption and better data reliability. The introduction of Cognitive Radio capabilities in WSN provides a new paradigm for power consumption reduction offering new opportunities to improve it, but also implies some challenges to face. Talking in detail about power consumption, sensing state, collaboration among devices (that requires communication) and changes in transmission parameters are not free in terms of consumption. In this way, all steps must be taken into account for a holistic optimization. Reducing power consumption requires optimization across all the layers of the communication systems.
The final goal is to reduce energy consumption in WSN exploiting the new capabilities introduced by the cognitive radio concept.
The objective of this project is the development of a cognitive module in smartphones. This module will implement cognitive features that becomes this terminals in nodes of a Cognitive Wireles Sensor Network. The smartphones are one of the best terminals in order to implement cognitive tasks such as spectrum sensing, collaboration and learning.
The objective of this project is the development of a simulator of Cognitive Wireless Sensor Networks. This simulator must support Cognitive Radio techniques adapted to wireless sensor networks. These techniques are: spectrum sensing, collaboration and learning, among others.