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Cognitive Radio Wireless Sensor Networks: From Radio to Applications
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Cognitive Radio Wireless Sensor Networks: From Radio to Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 3828

Special Issue Editor

Prof. Dr. Sangman Moh

E-Mail Website
Guest Editor
Department of Computer Engineering, Chosun University, Gwangju, Korea
Interests: Ad hoc and sensor networks; cognitive radio networks; unmanned aerial vehicle networks; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cognitive radio wireless sensor networks have been envisioned as one of the future wireless networking technologies to support seamless networking service and to improve spectrum utilization. They yield a large number of research and development challenges. For example, spectrum sensing and management are inherent to the properties of cognitive radio, and reliability and energy conservation are due to the characteristics of wireless sensor networks. This Special Issue covers a vast range of topics ranging from low-level physical radio to practical user applications in cognitive radio wireless sensor networks. Not only original research articles but also innovative reviews related to hot issues are welcome.

Prof. Dr. Sangman Moh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Algorithms
  • Network protocols
  • Test platforms
  • Embedded software
  • Modeling and performance study
  • Field applications
  • Interdisciplinary design

Published Papers (2 papers)

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16 pages, 634 KiB  
Communication
Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network
by Md. Tahidul Islam, Sithamparanathan Kandeepan and Robin. J. Evans
Sensors 2021, 21(9), 2997; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092997 - 24 Apr 2021
Cited by 3 | Viewed by 1666
Abstract
In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for [...] Read more.
In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system’s MTTR gains range from 11% to over 95% for various parametric values of the system model. Full article
(This article belongs to the Special Issue Cognitive Radio Wireless Sensor Networks: From Radio to Applications)
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19 pages, 1366 KiB  
Article
Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier
by Myke D. M. Valadão, Diego Amoedo, André Costa, Celso Carvalho and Waldir Sabino
Sensors 2021, 21(21), 7146; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217146 - 28 Oct 2021
Cited by 5 | Viewed by 1507
Abstract
Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in [...] Read more.
Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users. Full article
(This article belongs to the Special Issue Cognitive Radio Wireless Sensor Networks: From Radio to Applications)
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