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Advances and Trends in Sensors and Sensing Technologies for Indoor...
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Advances and Trends in Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

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

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 6503

Special Issue Editors

Dr. Filippo Palumbo

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient assisted living; indoor localization; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Antonino Crivello

E-Mail Website
Guest Editor
ISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy
Interests: cyber-physical systems; ambient intelligence; ambient assisted living; indoor localization and positioning; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Barsocchi

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient-assisted living; indoor localization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Welcome to the second edition of the Special Issue. The last ten years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with well-established outdoor GNSS solutions, no technology exists that is cheap and accurate enough for the general market. 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. The Special Issue topics include but are not limited to:

- Location-based services and applications;
- Benchmarking, assessment, evaluation, 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;
- Privacy and security for ILS;
- Standards and protocols for interoperability;
- Indoor maps

Dr. Filippo Palumbo
Dr. Antonino Crivello
Dr. Paolo Barsocchi
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.

Published Papers (2 papers)

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Research

19 pages, 3102 KiB  
Article
A Small World Graph Approach for an Efficient Indoor Positioning System
by Max Lima, Leonardo Guimarães, Eulanda Santos, Edleno Moura, Rafael Costa, Marco Levorato and Horácio Oliveira
Sensors 2021, 21(15), 5013; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155013 - 23 Jul 2021
Cited by 2 | Viewed by 2088
Abstract
The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique [...] Read more.
The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample that needs to be classified must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large-scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, large-scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 98% when compared to the classic kNN and at least 80% when compared to tree-based approaches. Full article
(This article belongs to the Special Issue Advances and Trends in Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation)
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16 pages, 4168 KiB  
Article
High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN
by Ju-Hyeon Seong, Soo-Hwan Lee, Won-Yeol Kim and Dong-Hoan Seo
Sensors 2021, 21(11), 3701; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113701 - 26 May 2021
Cited by 12 | Viewed by 3494
Abstract
Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath [...] Read more.
Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period. Full article
(This article belongs to the Special Issue Advances and Trends in Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation)
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