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Electronics
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Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based...
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Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based Services

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 11406

Special Issue Editors

Dr. Rashmi Sharan Sinha

E-Mail Website1 Website2
Guest Editor
Electronics and Electrical Engineering, Dongguk University, Seoul, Korea
Interests: communication and signal processing; machine learning; convolutional neural netwrok; indoor positioning system; localization; LTE; cellular networks; Internet of Things; optimization algorithm
Dr. Sourabh Solanki

E-Mail Website
Guest Editor
SnT – Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Interests: cognitive radio; mmwave communications; UAV Networks
Dr. Tuyen Danh Pham

E-Mail Website
Guest Editor
Electronics and Electrical Engineering, Dongguk University, Seoul, Korea
Interests: deep learning; digital image processing; biometrics
Dr. Vishal Sharma

E-Mail Website
Guest Editor
College of Applied Sciences & Technologies, Aston University, Birmingham B4 7ET, UK
Interests: intelligent transportation systems; microwave-photonics systems/networks; adaptive and energy efficient wireless sensor networks; photonics-based radar systems; fiber-based lasers

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute a paper to the Special Issue “Machine-Learning-Based Ubiquitous Localization, Indoor Positioning, and Location-Based Services”.

Reliable navigation and positioning are becoming imperative in more and more applications for safety-critical purposes, public services, and consumer products. A robust localization solution which will be available continuously is needed regardless of the specific environment, i.e., outdoors and indoors, and on different platforms, such as standalone navigators and mobile devices. A seamless localization solution is becoming imperative in more and more applications for safety-critical purposes, public services, and consumer products toward smart city development. Accuracy, reliability, scalability, and adaptability to the environment are prerequisites for widespread deployment. Machine learning (ML) and artificial intelligence (AI) approaches have been widely used to serve these prerequisites. On the other hand, maturing ML techniques, such as reinforcement learning and transfer learning, can potentially serve as the basis for incorporating learning into localization networks.

This Special Issue addresses innovative developments, technologies, and challenges related to machine-learning-based ubiquitous localization, indoor positioning, location-based service (LBS) design, and implementation for ubiquitous and pervasive application scenarios. The Special Issue is seeking the latest findings from research and ongoing projects. Additionally, review articles that provide readers with current research trends and solutions are also welcome. The potential topics include but are not limited to:

- Machine learning LBS and the Internet of Things

- Seamless interaction between people and things in a pervasive environment

- Edge/fog computing architectures to support LBS applications

- 5G architectures and applications for the next generation of LBS services

- Indoor and outdoor localization technologies

- LBS pervasive and ubiquitous applications

- Machine learning and artificial intelligence for localization and LBS

- Real-world experiences, e.g., in smart city and industrial LBS

- Machine learning algorithms for fingerprinting network devices/service

- GNSS-based positioning for indoors and outdoors

- RAN (radio access network)-based positioning in smart phones

- AI enabled vision-aided navigation

- Smart phone navigation and LBS technologies

- Location-based mobility models, services, and applications

- Machine learning, deep learning, reinforcement learning and other learning algorithms for IoT and 5G

- Wi-Fi-based indoor positioning and target detection/recognition

- Positioning for autonomous systems (robots, planes, land, and marine vehicles)

- mm-Wave and THz antennas for Machine-to-Machine (M2M) communications and positioning

- Sensor Networks, Lasers, Lidar and Radar positioning

Dr. Vishal Sharma
Dr. Rashmi Sharan Sinha
Dr. Sourabh Solanki
Dr. Tuyen Danh Pham
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. Electronics 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.

Published Papers (4 papers)

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Research

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23 pages, 15716 KiB  
Article
Machine Learning Approach towards LoRaWAN Indoor Localization
by Toni Perković, Lea Dujić Rodić, Josip Šabić and Petar Šolić
Electronics 2023, 12(2), 457; https://doi.org/10.3390/electronics12020457 - 16 Jan 2023
Cited by 8 | Viewed by 2849
Abstract
The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, [...] Read more.
The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, processing power, and energy consumption. This paper introduces a system for device indoor localization that uses variations in the strength of the wireless signal. The proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceutic delivery, delivery of confidential documents and court exhibits, and even food, since the same is introduced into human organism and presents a potential risk of terrorist or other attack. This work proposes a concept based on low-power and low-cost LoRaWAN based system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. Furthermore, using signal strength measurements, that is, RSSI and SNR captured by LoRaWAN gateways, it is possible to estimate the location of the device point with an accuracy of up to 98.8%. Full article
(This article belongs to the Special Issue Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based Services)
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22 pages, 2837 KiB  
Article
MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
by Valmik Tilwari, MHD Nour Hindia, Kaharudin Dimyati, Dushantha Nalin K. Jayakody, Sourabh Solanki, Rashmi Sharan Sinha and Effariza Hanafi
Electronics 2021, 10(23), 2937; https://doi.org/10.3390/electronics10232937 - 26 Nov 2021
Cited by 6 | Viewed by 1863
Abstract
With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to [...] Read more.
With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes. Full article
(This article belongs to the Special Issue Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based Services)
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13 pages, 5881 KiB  
Article
Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar
by Kyungeun Park, Jeongpyo Lee and Youngok Kim
Electronics 2021, 10(17), 2166; https://doi.org/10.3390/electronics10172166 - 5 Sep 2021
Cited by 9 | Viewed by 2118
Abstract
In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were [...] Read more.
In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m. Full article
(This article belongs to the Special Issue Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based Services)
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Other

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32 pages, 2817 KiB  
Systematic Review
Smartphone-Based Indoor Localization Systems: A Systematic Literature Review
by Rana Sabah Naser, Meng Chun Lam, Faizan Qamar and B. B. Zaidan
Electronics 2023, 12(8), 1814; https://doi.org/10.3390/electronics12081814 - 11 Apr 2023
Cited by 6 | Viewed by 3557
Abstract
These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are [...] Read more.
These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps. Full article
(This article belongs to the Special Issue Machine Learning Based Ubiquitous Localization, Indoor Positioning and Location Based Services)
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