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Multi-Sensor Positioning for Navigation in Smart Cities
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Multi-Sensor Positioning for Navigation in Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 5659

Special Issue Editors

Dr. Estefania Munoz Diaz

E-Mail Website
Guest Editor
Institute of Communications and Navigation, German Aerospace Center (DLR), 82234 Wessling, Germany
Interests: multimodal transportation; smart cities; modelling of passenger flows; smartphone-based navigation algorithms
Dr. Francisco Zampella

E-Mail Website
Guest Editor
Huawei Technologies Research and Development (UK) Limited, Edinburgh EH3 8BL , UK
Interests: indoor positioning; crowdsourcing; smartphone positioning; pedestrian dead reckoning; Wi-Fi positioning; sensor fusion
Dr. Elizabeth Colin

E-Mail Website
Guest Editor
EFREI Research Lab, 94800 Villejuif, France
Interests: RFID localization systems; indoor sub-metric localization; context-aware localization; localization in sports

Special Issue Information

Dear Colleagues,

The mobility of people and goods plays an important role in the life, work, prosperity, and cohesion of the citizens of smart cities. The biggest part of this transportation is carried out using motorized vehicles that make a large contribution (82 %) to greenhouse gas emissions. Additionally, the continuing growth in the demand for transport and changes in mobility behaviour lead to increasing conflicts over the use of limited space, where pedestrians, cyclists, and motorized vehicles compete for the use of the roads.

New user-centred mobility concepts that complement existing public transport, with the use of e- scooters, bicycles, and walking, are the key to solving these issues in smart cities. These concepts require robust individual positioning to provide advanced, seamless navigation across all transport modes, enabling a frictionless coexistence of active and motorized transport modes, and fostering sustainable mobility options.

Navigation in urban spaces, such as train stations or airports, is of key importance in understanding the needs, preferences, behaviours, and activities of users and crowds in each area. To achieve seamless navigation, the successful detection of different urban scenarios is needed, e.g., indoor/outdoor detection, to adapt the sensors and algorithms depending on the scenario.

Artificial intelligence (AI) can provide a significant boost for understanding mobility and behavioural patterns, as well as for the protection of e-scooters, cyclists, and pedestrians in urban environments. For the application of AI in safety-critical applications, new methods of validation and training are required. Access to city-wide information provides a significant amount of data, but it introduces new challenges for data handling and mining that need to be addressed. The analysis of big data and the methods for data-driven research should be used to gain high-quality data, dedicated to the training of AI for transport applications.

Dr. Estefania Munoz Diaz
Dr. Francisco Zampella
Dr. Elizabeth Colin
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.

Keywords

  • crowdsourcing
  • bicycle
  • pedestrian
  • machine learning
  • wearables
  • awareness and context detection
  • big data
  • data mining
  • mapping

Published Papers (3 papers)

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Research

18 pages, 5780 KiB  
Article
Crowdsourced Indoor Positioning with Scalable WiFi Augmentation
by Yinhuan Dong, Guoxiong He, Tughrul Arslan, Yunjie Yang and Yingda Ma
Sensors 2023, 23(8), 4095; https://doi.org/10.3390/s23084095 - 19 Apr 2023
Cited by 7 | Viewed by 1527
Abstract
In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, [...] Read more.
In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark. Full article
(This article belongs to the Special Issue Multi-Sensor Positioning for Navigation in Smart Cities)
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32 pages, 23389 KiB  
Article
Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis
by Andrea Brunello, Andrea Dalla Torre, Paolo Gallo, Donatella Gubiani, Angelo Montanari and Nicola Saccomanno
Sensors 2023, 23(1), 352; https://doi.org/10.3390/s23010352 - 29 Dec 2022
Viewed by 2273
Abstract
Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at [...] Read more.
Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only. Full article
(This article belongs to the Special Issue Multi-Sensor Positioning for Navigation in Smart Cities)
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16 pages, 5530 KiB  
Article
Smartphone-Based Localization for Passengers Commuting in Traffic Hubs
by Francisco Jurado Romero, Estefania Munoz Diaz and Dina Bousdar Ahmed
Sensors 2022, 22(19), 7199; https://doi.org/10.3390/s22197199 - 22 Sep 2022
Viewed by 1255
Abstract
Passengers commute between different modes of transportation in traffic hubs, and passenger localization is a key component for the effective functioning of these spaces. The smartphone-based localization system presented in this work is based on the 3D step and heading approach, which is [...] Read more.
Passengers commute between different modes of transportation in traffic hubs, and passenger localization is a key component for the effective functioning of these spaces. The smartphone-based localization system presented in this work is based on the 3D step and heading approach, which is adapted depending on the position of the smartphone, i.e., held in the hand or in the front pocket of the trousers. We use the accelerometer, gyroscope and barometer embedded in the smartphone to detect the steps and the direction of movement of the passenger. To correct the accumulated error, we detect landmarks, particularly staircases and elevators. To test our localization algorithm, we have recorded real-world mobility data in a test station in Munich city center where we have ground truth points. We achieve a 3D position accuracy of 12 m for a smartphone held in the hand and 10 m when the phone is placed in the front pocket of the trousers. Full article
(This article belongs to the Special Issue Multi-Sensor Positioning for Navigation in Smart Cities)
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