Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices

The article “Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices” by our lab member Ramiro Utrilla has just been published in the IEEE Access, a high-impact open-access journal.

This work has been carried out in collaboration with researchers from the CONNECT – Centre for Future Networks and Communications in Dublin (Ireland), where Ramiro carried out a research stay of 3 months.

In this article, they focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained end-devices.

In this work, they propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices.

The proposed GRU network model for AMC.

They trained and tested their solution with over-the-air measurements of real radio signals, which were acquired with the MIGOU platform.

Dataset generation scenario set up.

Comparison of signals recorded at (a) 1 and (b) 6 meters. The signals in the bottom row are the normalized version of those in the top row.

Their results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.

Increasing the training set can lead to improvements in the performance of a model without increasing its complexity. These improvements allow developers to reduce the complexity of the model and, therefore, the device resources it requires. However, longer training processes can lead to fitting and gradient problems. These tradeoffs should be explored when developing neural network-based solutions for resource-constrained end-devices.

Applied Science: Special Issue “Wireless Sensor Networks: Technologies, Applications, Prospects”

The main objective of this Special Issue is to provide a common space for WSNs researchers to share their high quality research and outcomes, and disseminate them to the rest of the world. The topics include novel designs,
developments, and management of smart systems with a focus on new applications. In addition to these, notable advancements in the performance of WSN are welcome.

A Methodology for Choosing Time Synchronization Strategies for Wireless IoT Networks

This summer we have published a new article about time synchronization for wireless sensor networks, applied to the field of IoT, in Sensors Open Access Journal. This journal has these statistics:

  • 2018 Impact Factor: 3.031
  • 5-year Impact Factor: 3.302
  • JCR category rank: 15/61 (Q1) in ‘Instruments & Instrumentation’

This article belongs to the Special Issue Topology Control and Protocols in Sensor Network and IoT Applications.

This article has a direct relationship with the thesis of our colleague Francisco Tirado-Andrés. This thesis investigates a methodology, and associated tools, to make it easier for all researchers to choose time synchronization protocols for specific WSNs.

For more information about this article please visit MDPI webpage.

“A security scheme for wireless sensor networks” aceptado en el Globecom 16

Como ya os comentamos hace unos días la visita de nuestro compañero Hacene Fouchal fue muy productiva. Una de las actividades que realizamos fue un artículo para el congreso Globecom’16 y por el gran trabajo realizado, el artículo ha sido aceptado.

El artículo propone un nuevo sistema de seguridad para redes de sensores inalámbricas (WSNs) que asegura la autenticación de los nodos aunque no tengan acceso a una autoridad de certificados. EL congreso se celebrará del 4 al 8 de Diciembre en Washington. Esperemos que sea una gran experiencia para Hacene.


A WSN-Based Intrusion Alarm System to Improve Safety in Road Work Zones

Title: A WSN-Based Intrusion Alarm System to Improve Safety in Road Work Zones
Authors: Jose Martin, Alba Rozas, and Alvaro Araujo
Published in: Journal of Sensors
Date of Publication: Jun 2016
Digital Object Identifier : 10.1155/2016/7048141

Road traffic accidents are one of the main causes of death and disability worldwide. Workers responsible for maintaining and repairing roadways are especially prone to suffer these events, given their exceptional exposure to traffic. Since these actuations usually coexist with regular traffic, an errant driver can easily intrude the work area and provoke a collision. Some authors have proposed mechanisms aimed at detecting breaches in the work zone perimeter and alerting workers, which are collectively called intrusion alarm systems. However, they have several limitations and have not yet fulfilled the necessities of these scenarios. In this paper, we propose a new intrusion alarm system based on a Wireless Sensor Network (WSN). Our system is comprised of two main elements: vehicle detectors that form a virtual barrier and detect perimeter breaches by means of an ultrasonic beam and individual warning devices that transmit alerts to the workers. All these elements have a wireless communication interface and form a network that covers the whole work area. This network is in charge of transmitting and routing the alarms and coordinates the behavior of the system. We have tested our solution under real conditions with satisfactory results.

Artículo aceptado sobre el proyecto PROMETEO

Aunque el proyecto PROMETEO finalizó hace ya dos años, hoy hemos recibido una gran noticia referente a su difusión. Ha sido aceptado un artículo sobre el sistema desarrollado, titulado Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network, en la revista Journal of Sensors, con índice de impacto 1,182. Este artículo describe de forma detallada la red terrena de sensores desarrollada, así como las pruebas que se realizaron durante el transcurso del proyecto.

Esta red era el objetivo principal de la tarea en la que participaba el grupo B105 junto al grupo GIICA del Instituto de Tecnología Informática de la Universidad Politécnica de Valencia y la empresa ISDEFE. El grupo B105 se encargó del desarrollo de los nodos finales, encargados de monitorizar la temperatura, humedad y condiciones de viento en el bosque. Estos datos se enviaban inalámbricamente a los nodos centrales, desarrollados por el grupo de la UPV, que los agrupaban y reenviaban al centro de control. La tarea de ISDEFE consistió en la supervisión y seguimiento del proyecto.

En lo que respecta al artículo, cada participante del proyecto ha escrito las secciones correspondientes a su desarrollo, mientras que el planteamiento general y la escritura del conjunto del artículo han sido realizadas por David Cuesta de la UPV. Esperemos que su publicación contribuya a una mayor difusión del sistema desarrollado. Además, de cara a nuestro grupo, puede servir de ejemplo de que el trabajo de desarrollo e innovación puede y debe publicarse tanto como el de investigación.


Cognitive Wireless Sensor Network Platform for Cooperative Communications

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.



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

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

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.



Improving Security in WMNs with Reputation Systems and Self-Organizing Maps

Title: Improving Security in WMNs with Reputation Systems and Self-Organizing Maps
Authors: Z. Bankovic, D. Fraga, José M. Moya, J.C. Vallejo, P. Malagón, A. Araujo, J.M. de Goyeneche, E. Romero, J. Blesa, D. Villanueva, O. Nieto-Taladriz
Published in: Journal of Network and Computer Applications, Special Issue “Wireless Mesh Networks”ISSN : 1084–8045
Date of Publication: April 2010
Digital Object Identifier : 10.1016/j.jnca.2010.03.023

One of the most important problems of WMNs, that is even preventing them from being used in many sensitive applications, is the lack of security. To ensure security of WMNs, two strategies need to be adopted: embedding security mechanisms into the network protocols, and developing efficient intrusion detection and reaction systems. To date, many secure protocols have been proposed, but their role of defending attacks is very limited.

We present a framework for intrusion detection in WMNs that is orthogonal to the network protocols. It is based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (self-organizing maps), that are able to detect deviations from the normal behavior. An additional advantage of this approach is that it is quite independent of the attacks, and therefore it can detect and confine new, previously unknown, attacks. Unlike previous approaches, and due to the inherent insecurity of WMN nodes, we assume that confidentiality and integrity cannot be preserved for any single node.