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Special Issue "Advanced Wireless Sensing Techniques for Communication"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Yuh-Shyan Chen
E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan
Interests: wireless sensor networks; fog computing for sensors; software-defined sensors; sensors with 5G/6G; internet of things
Special Issues and Collections in MDPI journals
Dr. Ilsun You
E-Mail Website1 Website2
Guest Editor
Department of Information Security Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si 31538, Choongchungnam-do, Korea
Interests: 5G security; IoT security; authentication and access control; formal security analysis; mobile internet security
Special Issues and Collections in MDPI journals
Prof. Dr. Shih-Lin Wu
E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Taiwan
Interests: mobile/wireless networks; internet of things; wireless sensor networks; big data analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The development of new advanced wireless sensing technologies for communication in smart cities with Internet of Things (IoT) has long been a particular concern for researchers. The key applications of smart cities with IoT technologies include smart economy, smart transportation, smart life, smart education, smart management, smart medical care, and smart environments. The recent COVID-19 pandemic has dealt a significant blow to the global economy and has had a major impact on global industries, education, health, and tourism. Therefore, how to more effectively combine new sensors and advanced communication technologies to effectively control COVID-19, and to further enhance the smart economy, smart transportation, smart life, smart education, and smart management in smart cities are very important topics. To support telemedicine and telehealth for smart medical care, wearable or non-invasive sensors combined with advanced wireless communications technology can effectively and continuously monitor patients to facilitate data collection and to immediately contact doctors when a temporary danger occurs in a long distance medicine environment. Furthermore, in the smart environment, environmental monitoring and sensing (pollution, food, and water quality, etc.) can be combined with advanced communication wireless sensing technology, such as effective collection of artificial intelligence data sets to perform artificial intelligence for environmental pollution analysis and monitoring to prevent health hazards to the general public.

This Special Issue aims to address advanced wireless sensing techniques for smart cities. We invite state-of-the-art theoretical, as well as practical works on a broad range of issues important to advanced wireless sensing techniques for researches, developers, and practitioners from both academia and industry.

Topics of primary interest include but are not limited to the following:

  • advanced wireless sensing techniques for Internet of Things in smart cities
  • advanced wireless sensing techniques for Internet of Nano Things with molecular communication (MC)
  • advanced wireless sensing techniques for 5G/beyond 5G/6G
  • advanced wireless sensing techniques for URLLC/eMTC/eMBB applications
  • advanced wireless sensing techniques with artificial intelligence
  • advanced wireless sensing techniques with security
  • advanced wireless sensing techniques with edge/cloud computing

Prof. Dr. Yuh-Shyan Chen
Prof. Dr. Ilsun You
Prof. Dr. Shih-Lin Wu
Guest Editors

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 papers will be 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 2200 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

  • advanced wireless sensing techniques
  • 5G/beyond 5G/6G
  • artificial intelligence
  • security
  • edge/cloud computing

Published Papers (1 paper)

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Research

Article
A Semi-Supervised Transfer Learning with Grid Segmentation for Outdoor Localization over LoRaWans
Sensors 2021, 21(8), 2640; https://doi.org/10.3390/s21082640 - 09 Apr 2021
Viewed by 460
Abstract
During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a small number of labelled data [...] Read more.
During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a small number of labelled data from the source domain to generate a kernel knowledge for the target domain. The kernel knowledge is transferred to a target domain to transfer some unlabelled data into the virtual labelled data. In this paper, we have proposed a new outdoor localization scheme using a semi-supervised transfer learning for LoRaWANs. In the proposed localization algorithm, a grid segmentation concept is proposed so as to generate a number of virtual labelled data through learning by constructing the relationship of labelled and unlabelled data. The labelled-unlabelled data relationship is repeatedly fine-tuned by correctly adding some more virtual labelled data. Basically, the more the virtual labelled data are added, the higher the location accuracy will be obtained. In the real implementation, three types of signal features, RSSI, SNR, and timestamps, are used for training to improve the location accuracy. The experimental results illustrate that the proposed scheme can improve the location accuracy and reduce the localization error as opposed to the existing outdoor localization schemes. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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