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Smart Sensor Systems for Positioning and Navigation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 527

Special Issue Editors


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Guest Editor
Department of Computer Science and Centre for Reliable Machine Learning, Royal Holloway, University of London, London, UK
Interests: machine learning; data analysis; networked systems; indoor positioning

E-Mail Website
Guest Editor
Department of Computer Science, Royal Holloway University of London, Surrey TW20 0EX, UK
Interests: indoor positioning; contact tracing; railway navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last ten years have seen enormous technical progress in the field of indoor positioning and indoor navigation. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems.

This Special Issue encourages authors, from academia and industry, to submit new research results about innovations for indoor positioning and navigation, especially the application of smart sensors. The Special Issue topics include but are not limited to the following:

  • Location-based services and applications;
  • Benchmarking, assessment, evaluation and standards;
  • User requirements;
  • UI, indoor maps, and 3D building models;
  • Human motion monitoring and modeling;
  • Robotics and UAV;
  • Indoor navigation and tracking methods;
  • Self-contained sensors;
  • Wearable and multisensor systems.

Prof. Dr. Zhiyuan Luo
Dr. Khuong An Nguyen
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 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

  • indoor positioning
  • indoor mapping
  • indoor navigation
  • smart sensors

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Published Papers (1 paper)

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Research

13 pages, 4239 KiB  
Communication
Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks
by Runjie Liu, Qionggui Zhang, Yuankang Zhang, Rui Zhang and Tao Meng
Sensors 2024, 24(16), 5335; https://doi.org/10.3390/s24165335 - 18 Aug 2024
Viewed by 390
Abstract
In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across [...] Read more.
In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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