Special Issue "Indoor Navigation in Smart Cities"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editor

Prof. Dr. Gianmario Motta
E-Mail Website
Guest Editor
Università di Pavia, Pavia, Italy
Interests: information systems; smart city, positioning systems and in overall cloud management; urban mobility; service systems

Special Issue Information

Dear Colleagues,

A very evident application area in smart city research is indoor positioning and navigation. Indoor navigation requires specific technologies, since GPS becomes undependable and vague, and public information, like Google maps, is not available. Hence, potential applications should efficiently support a complete service life-cycle, which includes map creation, user positioning, path planning, fixed and moving obstacle avoidance, en-route assistance, etc. Further, potential apps should deal with a variety of indoor spaces, from home to public areas. In turn, potential technologies should encompass fixed and wearable sensors and classic and IOT networks. Finally, apps should serve both normal or disabled persons and interact appropriately.

This Special Issue encourages authors from academia and industry to submit new research results about innovations in indoor navigation. Both research and review papers are welcome. The Special Issue topics include but are not limited to:

  • Location-based services and applications;
  • Indoor maps and 3D building models;
  • Human motion monitoring and modeling;
  • Apps and technologies for disabled people;
  • Indoor navigation and tracking methods;
  • Self-contained sensors;
  • Wearable and multisensor systems;
  • Intelligent sensors and wireless sensors for smart cities;
  • Moving obstacle avoidance;
  • Building inspection and maintenance.

Prof. Dr. Gianmario Motta
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 papers will be 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. Information 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 1400 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 (6 papers)

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Editorial

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Editorial
Editorial for Special Issue Indoor Navigation in Smart Cities
Information 2021, 12(4), 152; https://doi.org/10.3390/info12040152 - 03 Apr 2021
Viewed by 548
Abstract
The lifecycle of indoor navigation includes various phases [...] Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)

Research

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Article
Towards a Predictive Bio-Inspired Navigation Model
Information 2021, 12(3), 100; https://doi.org/10.3390/info12030100 - 26 Feb 2021
Cited by 2 | Viewed by 730
Abstract
This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells [...] Read more.
This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells are structures that store and connect local representations of the explored environment, grid and head direction cells make predictions based on these representations to define the position of the agent in a place cell’s reference frame. This specific use of navigation cells has three advantages: First, the environment representations are stored by place cells and require only a few spatialized descriptors or elements, making this model suitable for the integration of large-scale environments (indoor and outdoor). Second, the grid cell modules act as an efficient visual and absolute odometry system. Finally, the model provides sequential spatial tracking that can integrate and track an agent in redundant environments or environments with very few or no distinctive cues, while being very robust to environmental changes. This paper focuses on the architecture formalization and the main elements and properties of this model. The model has been successfully validated on basic functions: mapping, guidance, homing, and finding shortcuts. The precision of the estimated position of the agent and the robustness to environmental changes during navigation were shown to be satisfactory. The proposed predictive model is intended to be used on autonomous platforms, but also to assist visually impaired people in their mobility. Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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Article
Deep Learning-Based Indoor Distance Estimation Scheme Using FMCW Radar
Information 2021, 12(2), 80; https://doi.org/10.3390/info12020080 - 13 Feb 2021
Cited by 3 | Viewed by 768
Abstract
In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a [...] Read more.
In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m. Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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Article
Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation
Information 2020, 11(10), 464; https://doi.org/10.3390/info11100464 - 30 Sep 2020
Cited by 1 | Viewed by 893
Abstract
In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations [...] Read more.
In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM. Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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Article
Discovering Influential Positions in RFID-Based Indoor Tracking Data
Information 2020, 11(6), 330; https://doi.org/10.3390/info11060330 - 20 Jun 2020
Cited by 1 | Viewed by 1177
Abstract
The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, [...] Read more.
The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average. Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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Article
Classroom Attendance Systems Based on Bluetooth Low Energy Indoor Positioning Technology for Smart Campus
Information 2020, 11(6), 329; https://doi.org/10.3390/info11060329 - 19 Jun 2020
Cited by 7 | Viewed by 1824
Abstract
Student attendance during classroom hours is important, because it impacts the academic performance of students. Consequently, several universities impose a minimum attendance percentage criterion for students to be allowed to attend examinations; therefore, recording student attendance is a vital task. Conventional methods for [...] Read more.
Student attendance during classroom hours is important, because it impacts the academic performance of students. Consequently, several universities impose a minimum attendance percentage criterion for students to be allowed to attend examinations; therefore, recording student attendance is a vital task. Conventional methods for recording student attendance in the classroom, such as roll-call and sign-in, are an inefficient use of instruction time and only increase teachers’ workloads. In this study, we propose a Bluetooth Low Energy-based student positioning framework for automatically recording student attendance in classrooms. The proposed architecture consists of two components, an indoor positioning framework within the classroom and student attendance registration. Experimental studies using our method show that the Received Signal Strength Indicator fingerprinting technique that is used in indoor scenarios can achieve satisfactory positioning accuracy, even in a classroom environment with typically high signal interference. We intentionally focused on designing a basic system with simple indoor devices based on ubiquitous Bluetooth technology and integrating an attendance system with computational techniques in order to minimize operational costs and complications. The proposed system is tested and demonstrated to be usable in a real classroom environment at Rangsit University, Thailand. Full article
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
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