This work is part of the ROBIM project in which the working group B105 Electronic Systems Lab of the University UniversidadPolitécnica de Madrid collaborates. The ROBIM project takes part in the program Programa Estratégico CIEN with the support of the CDTI (Centro para el desarrollo tecnológico Industrial) and the RDF (Regional Development Forum) for Europe.
The ROBIM project seeks to automate technical inspections of buildings, reducing costs and execution times associated with these processes. The system makes use of a drone for inspection work, thus avoiding the installation of scaffolding and all the security measures that the process requires, which is costly in time and money. Currently, the drone has a communication channel that allows users to obtain information on the process, as well as direct the drone whenever necessary. The main objective of this work is to create a secondary, safe and effective communication channel, for situations where communication with the main system is not possible. To achieve this, the project stablish the following requierements:
– The device must allow radiocommunication in ISM bands. – The device has an USB interface to connect with the computer/drone. – The communication must be reliable by allowing communication throwgh various channels and implementing software-defined radio and cognitive radio.
Therefore, to achieve these objectives, this work proposes the design of a 2-channel device for radiocommunication in the 433 MHz and 868 MHz bands, using two SPIRIT1 transceivers and an ARM Cortex-M4 microcontroller.
The Hardware design has been made usign the Altium Designer PCB design layout tool . The designed PCB is divided into three parts: the power/communication stage, the control stage with the microcontroller and the radiofrecuency stage with both SPIRIT1 trasnceivers.
The software design has been developed in 2 stages: software design of an application for evaluation boards during the PCB manufacturating process and software design of a final application for the designed PCB. For the software design of evaluation board, the NUCLEO – L053R8 with the X-NUCLEO-IDS01A4 radio frequency module has been chosen, which allows radio communication in the 868 MHz band. The final design of the software is based on the software of the evaluation board but improving its functionality by adding communication through two channels with a cognitive procedure based on the CSMA / CA protocol and implementing serial communication with the user.
The application designed for the device allows, then, a cognitive communication based on CSMA/CA protocol in bands 433 MHz and 868 MHz in addition to communication with the user and the drone enabling the possibility of the implementation of the second channel for the communication with the drone.
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.
They trained and tested their solution with over-the-air measurements of real radio signals, which were acquired with the MIGOU platform.
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%.
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.