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Learning Technology Based on Navigation Sensors
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Learning Technology Based on Navigation Sensors

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

Deadline for manuscript submissions: closed (10 October 2022) | Viewed by 8628
Please contact the Guest Editor or the Section Managing Editor at (ava.jiang@mdpi.com) for any queries.

Special Issue Editor

Prof. Dr. Taikjin Lee

E-Mail Website
Guest Editor
Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul, Korea
Interests: navigation; localization; pattern recognition; sensor and its applications; machine learning and LBS

Special Issue Information

Dear Colleagues,

Location is one of the most important aspects of modern life, specifically recognizing one’s location and trajectory. Map applications such as Google Maps are used on a daily basis. However, since such programs are based on GNSS, limitations are inevitable in GNSS-denied environments such as indoor or urban spaces. Recently, research that easily combines various navigation sensors using technologies such as machine learning and deep learning and overcoming the limitations of sensors through pattern recognition is evolving. In particular, these technologies are serving as solutions to the “seamless” problem, which is the weakest aspect of existing location services.

This Special Issue includes maximizing the performance of navigation sensors through learning technology such as pattern recognition technology. Thus, we look forward to your proposals on new localization and pattern recognition technologies that are more accurate, highly available, and seamless. We encourage authors to submit new research results about technological innovations and novel applications for pattern recognition and localization.


Prof. Dr. Taikjin Lee
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. 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 or urban localization
  • Indoor positioning
  • Indoor positioning system
  • Position tracking
  • Seamless localization
  • INS on mobile
  • Mobile application in LBS
  • Localization or navigation using machine learning or deep learning
  • Pattern recognition using machine learning or deep learning based on navigation sensors
  • Navigation sensor analysis using pattern recognition
  • Novel technologies
  • New applications
  • State-of-the-art devices
  • Portable device-based
  • Challenges in design and deployment
  • Evaluation

Published Papers (3 papers)

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Research

17 pages, 2779 KiB  
Article
ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
by Fatma H. El-Fouly, Ahmed Y. Khedr, Md. Haidar Sharif, Eissa Jaber Alreshidi, Kusum Yadav, Huseyin Kusetogullari and Rabie A. Ramadan
Sensors 2022, 22(22), 8950; https://doi.org/10.3390/s22228950 - 18 Nov 2022
Cited by 5 | Viewed by 1389
Abstract
Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because [...] Read more.
Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes’ energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks. Full article
(This article belongs to the Special Issue Learning Technology Based on Navigation Sensors)
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19 pages, 8435 KiB  
Article
Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments
by Jinhwan Jeon, Yoonjin Hwang, Yongseop Jeong, Sangdon Park, In So Kweon and Seibum B. Choi
Sensors 2021, 21(20), 6805; https://doi.org/10.3390/s21206805 - 13 Oct 2021
Cited by 5 | Viewed by 2856
Abstract
With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. [...] Read more.
With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented. Full article
(This article belongs to the Special Issue Learning Technology Based on Navigation Sensors)
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15 pages, 6774 KiB  
Article
Underground Parking Lot Navigation System Using Long-Term Evolution Signal
by Beomju Shin, Jung Ho Lee, Changsu Yu, Chulki Kim and Taikjin Lee
Sensors 2021, 21(5), 1725; https://doi.org/10.3390/s21051725 - 2 Mar 2021
Cited by 6 | Viewed by 3440
Abstract
Some of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to determine their current location and need to keep moving the vehicle to find an empty parking space. [...] Read more.
Some of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to determine their current location and need to keep moving the vehicle to find an empty parking space. Moreover, they need to remember the parked location, and find a nearby staircase or elevator to move toward the destination. In such a situation, if the user location can be estimated, a new navigation system can be offered, which can assist users. This study presents an underground parking lot navigation system using long-term evolution (LTE) signals. As the proposed system utilizes LTE network signals for which the infrastructure is already installed, no additional infrastructure is required. To estimate the location of the vehicle, the signal strength of the LTE signal is accumulated, and the location of the vehicle is estimated by comparing it with the previously stored database of the LTE received signal strength (RSS). In addition, the acceleration and gyroscope sensors of a smartphone are used to improve the vehicle position estimation performance. The effectiveness of the proposed system is verified by conducting an experiment in a large shopping-mall underground parking lot where approximately 500 vehicles can be parked. From the results of the experiment, an error of less than an average of 10 m was obtained, which shows that seamless navigation is possible using the proposed system even in an environment where GNSS does not function. Full article
(This article belongs to the Special Issue Learning Technology Based on Navigation Sensors)
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