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.

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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
Web: https://www.hindawi.com/journals/js/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.