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Future Internet
Special Issues
Wireless Technology for Indoor Localization System
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Wireless Technology for Indoor Localization System

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 10238

Special Issue Editor

Dr. Walter Balzano

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80126 Napoli, Italy
Interests: indoor and outdoor localization; locating vehicles circulating inside large buildings; various wireless technologies

Special Issue Information

Dear Colleagues,

Wireless technology for indoor localization systems includes a set of techniques, methods, and technologies mainly aimed at determining the localization of devices in environments where classic localization resources such as traditional GPS are not usable. In fact, the need to be able to locate a generic entity within, for example, large buildings such as universities, shopping centres, administrative offices, hospitals, car parks, is important for many functions. These are just some examples in which GPS cannot be used because sufficient visibility of the various constellations of existing satellites is not guaranteed inside structures. These considerations are the main prerequisites for the study and design of alternative localization systems. In order to minimize the costs of creating completely new platforms from scratch, the properties of existing infrastructure such as networks, wireless technologies, and IoT are exploited.

The purpose of this Special Issue is to highlight the most recent innovations in the area of wireless technologies, such as Wi-Fi, Bluetooth, Zigbee, and RFID, in order to obtain both innovative and more precise and secure location services capable of guaranteeing a high level of localization quality.

The topics of this Special Issue include, but are not limited to, the following:

  • State-of-the-art and overview of wireless technology for indoor localization systems
  • Fingerprinting
  • Triangulation
  • Wireless Network
  • Indoor localization
  • WiFi
  • ZigBee
  • Bluetooth
  • RFID
  • Proximity

Dr. Walter Balzano
Guest Editor

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • wireless network
  • indoor localization
  • Wi-Fi
  • Bluetooth
  • RFID

Published Papers (5 papers)

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Research

18 pages, 4899 KiB  
Article
A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
by Yaning Li, Hongsheng Li, Baoguo Yu and Jun Li
Future Internet 2022, 14(8), 235; https://doi.org/10.3390/fi14080235 - 29 Jul 2022
Cited by 1 | Viewed by 1402
Abstract
At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, [...] Read more.
At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection. Full article
(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)
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13 pages, 684 KiB  
Article
An Indoor Smart Parking Algorithm Based on Fingerprinting
by Silvia Stranieri
Future Internet 2022, 14(6), 185; https://doi.org/10.3390/fi14060185 - 14 Jun 2022
Cited by 6 | Viewed by 1727
Abstract
In the last few years, researchers from many research fields are investigating the problem affecting all the drivers in big and populated cities: the parking problem. In outdoor environments, the problem can be solved by relying on vehicular ad hoc networks, which guarantee [...] Read more.
In the last few years, researchers from many research fields are investigating the problem affecting all the drivers in big and populated cities: the parking problem. In outdoor environments, the problem can be solved by relying on vehicular ad hoc networks, which guarantee communication among vehicles populating the network. When it comes to indoor settings, the problem gets harder, since drivers cannot count on classic GPS localization. In this work, a smart parking solution for a specific indoor setting is provided, exploiting the fingerprint approach for indoor localization. The considered scenario is a multi-level car park inside an airport building. The algorithm provides a vehicle allocation inside the car park in quadratic time over the number of parking slots, by also considering the driver’s preferences on the terminal to be reached. Full article
(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)
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16 pages, 5432 KiB  
Article
An Indoor and Outdoor Multi-Source Elastic Fusion Navigation and Positioning Algorithm Based on Particle Filters
by Guangwei Fan, Chuanzhen Sheng, Baoguo Yu, Lu Huang and Qiang Rong
Future Internet 2022, 14(6), 169; https://doi.org/10.3390/fi14060169 - 31 May 2022
Cited by 5 | Viewed by 1755
Abstract
In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of [...] Read more.
In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of indoor and outdoor complex scenes, these methods will cause the results of position with a marked jump and affect the accuracy of navigation and positioning. Aiming at the navigation and positioning problem in the case of indoor and outdoor complex scene switching, this paper proposes a multisource elastic navigation and positioning method based on particle filters, which makes full use of the redundant information of multisource sensors, constructs an elastic multisource fusion navigation and positioning model after eliminating abnormal data, elastically gives different particle weights to multisource sensors according to environmental changes and realizes the elastic fusion and positioning of multisource sensors through filtering. The test results show that this method has high navigation and positioning accuracy, the dynamic positioning accuracy is better than 0.7 m and there will be no jumping of positioning results in the process of scene switching, which improves the navigation and positioning accuracy and stability in complex and changeable indoor and outdoor environments. Full article
(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)
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17 pages, 2435 KiB  
Article
Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
by Helmer Augusto de Souza Mourão and Horácio Antonio Braga Fernandes de Oliveira
Future Internet 2022, 14(2), 51; https://doi.org/10.3390/fi14020051 - 7 Feb 2022
Cited by 3 | Viewed by 2252
Abstract
Indoor localization systems are used to locate mobile devices inside buildings where traditional solutions, such as the Global Navigation Satellite Systems (GNSS), do not work well due to the lack of direct visibility to the satellites. Fingerprinting is one of the most known [...] Read more.
Indoor localization systems are used to locate mobile devices inside buildings where traditional solutions, such as the Global Navigation Satellite Systems (GNSS), do not work well due to the lack of direct visibility to the satellites. Fingerprinting is one of the most known solutions for indoor localization. It is based on the Received Signal Strength (RSS) of packets transmitted among mobile devices and anchor nodes. However, RSS values are known to be unstable and noisy due to obstacles and the dynamicity of the scenarios, causing inaccuracies in the position estimations. This instability and noise often cause the system to indicate a location that it is not quite sure is correct, although it is the most likely based on the calculations. This property of RSS can cause algorithms to return a localization with a low confidence level. If we could choose more reliable results, we would have an overall result with better quality. Thus, in our solution, we created a checking phase of the confidence level of the localization result. For this, we use the prediction probability provided by KNN and the novelty detection to discard classifications that are not very reliable and often wrong. In this work, we propose LocFiND (Localization using Fingerprinting and Novelty Detection), a fingerprint-based solution that uses prediction probability and novelty detection to evaluate the confidence of the estimated positions and mitigate inaccuracies caused by RSS in the localization phase. We implemented our solution in a real-world, large-scale school area using Bluetooth-based devices. Our performance evaluation shows considerable improvement in the localization accuracy and stability while discarding only a few, low confidence estimations. Full article
(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)
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19 pages, 4494 KiB  
Article
No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts
by Jin Wang and Jun Luo
Future Internet 2022, 14(1), 7; https://doi.org/10.3390/fi14010007 - 23 Dec 2021
Cited by 1 | Viewed by 2285
Abstract
While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such [...] Read more.
While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations. Full article
(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)
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