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Applied Sciences
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Current Research in Indoor Positioning and Localization
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Current Research in Indoor Positioning and Localization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 2111

Special Issue Editors

Prof. Dr. Shengcheng Yeh

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Guest Editor
Department of Applied Artificial Intelligence, Ming Chuan University, Taipei, Taiwan
Interests: computer network; wireless communication; deep learning; indoor positioning technology
Dr. Yih-Shyh Chiou

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Guest Editor
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City, Taiwan
Interests: wireless positioning techniques; mobile positioning and tracking; mobile computing
Prof. Dr. Bang Wang

E-Mail Website
Guest Editor
School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan 430074, China
Interests: indoor localization and tracking; Wi-Fi fingerprinting; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor navigation is no longer science fiction. Researchers are making significant strides in developing accurate and versatile indoor positioning systems (IPSs). This Special Issue dives into the cutting edge of IPSs, exploring new techniques and applications.

  • Ultra-Wideband (UWB): a high-bandwidth technology offering centimeter-level accuracy.
  • Sensor Fusion: combining data from multiple sensors (Wi-Fi, Bluetooth, cameras) for more robust positioning.
  • Deep Learning: using artificial intelligence to analyze signal patterns and improve location estimation.
  • Angle of Arrival (AoA): determining the direction of a signal source for more precise localization.
  • Indoor Navigation: providing guidance within buildings using real-time location data.
  • Real-Time Locating Systems (RTLSs): tracking the location of objects or people indoors.
  • Fingerprinting: creating a unique "fingerprint" of signal signatures at different locations.
  • Challenge of Multipath Interference: signal bouncing off objects, affecting accuracy.
  • Indoor LiDAR: Using light detection and ranging for 3D indoor mapping.

Prof. Dr. Shengcheng Yeh
Dr. Yih-Shyh Chiou
Prof. Dr. Bang Wang
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. Applied Sciences 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 2400 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

  • ultra-wideband (UWB)
  • sensor fusion
  • deep learning
  • angle of arrival (AoA)
  • indoor navigation
  • real-time locating system (RTLS)
  • fingerprinting
  • challenge of multipath interference

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Published Papers (2 papers)

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25 pages, 1280 KiB  
Article
Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
by Isil Karabey Aksakalli and Levent Bayındır
Appl. Sci. 2025, 15(4), 1985; https://doi.org/10.3390/app15041985 - 14 Feb 2025
Viewed by 565
Abstract
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among [...] Read more.
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems—such as Bluetooth, ZigBee, or FM radio—Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification. Full article
(This article belongs to the Special Issue Current Research in Indoor Positioning and Localization)
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22 pages, 11693 KiB  
Article
Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
by Jiwei Hou, Patrick Hübner and Dorota Iwaszczuk
Appl. Sci. 2025, 15(3), 1151; https://doi.org/10.3390/app15031151 - 23 Jan 2025
Viewed by 1039
Abstract
Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from [...] Read more.
Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response. Full article
(This article belongs to the Special Issue Current Research in Indoor Positioning and Localization)
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