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Smart Sensor Systems for Positioning and Navigation
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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: 30 June 2025 | Viewed by 5834

Special Issue Editors

Prof. Dr. Zhiyuan Luo

E-Mail Website
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
Dr. Khuong An Nguyen

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 (5 papers)

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Research

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19 pages, 3009 KiB  
Article
Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System
by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek and Patricia Nieva
Sensors 2025, 25(9), 2638; https://doi.org/10.3390/s25092638 - 22 Apr 2025
Viewed by 359
Abstract
Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that [...] Read more.
Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual’s location is classified, introducing a highly reliable indoor/outdoor tracking system. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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20 pages, 4186 KiB  
Article
Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics
by Arturs Simkuns, Rodions Saltanovs, Maksims Ivanovs and Roberts Kadikis
Sensors 2025, 25(5), 1576; https://doi.org/10.3390/s25051576 - 4 Mar 2025
Viewed by 953
Abstract
Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable [...] Read more.
Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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22 pages, 1654 KiB  
Article
A New Scene Sensing Model Based on Multi-Source Data from Smartphones
by Zhenke Ding, Zhongliang Deng, Enwen Hu, Bingxun Liu, Zhichao Zhang and Mingyang Ma
Sensors 2024, 24(20), 6669; https://doi.org/10.3390/s24206669 - 16 Oct 2024
Viewed by 1089
Abstract
Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect [...] Read more.
Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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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 1493
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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Review

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21 pages, 1744 KiB  
Review
A Review of Miniature Radio Transmitters for Wildlife Tracking
by Sivan Toledo
Sensors 2025, 25(2), 517; https://doi.org/10.3390/s25020517 - 17 Jan 2025
Viewed by 1427
Abstract
This article surveys the literature on miniature radio transmitters designed to track free-ranging wild animals using emitter-localization techniques. The articles covers the topics of power sources used in such transmitters, including miniature batteries and energy harvesting, techniques for generating the transmitted radio-frequency carrier, [...] Read more.
This article surveys the literature on miniature radio transmitters designed to track free-ranging wild animals using emitter-localization techniques. The articles covers the topics of power sources used in such transmitters, including miniature batteries and energy harvesting, techniques for generating the transmitted radio-frequency carrier, techniques for creating short radio pulses and more general on–off schedules, modulation in modern wildlife-tracking transmitters, construction, manufacturing, and tuning techniques, and recent trends in this area. The article also describes the recreation of the first successful wildlife-tracking transmitter, a nontrivial invention that had a profound impact on wildlife ecology, and explores its behavior. Full article
(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)
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