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Special Issue "Sensors and Systems for Indoor Positioning"

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

Deadline for manuscript submissions: 15 July 2021.

Special Issue Editors

Prof. Dr. Riccardo Carotenuto
Website
Guest Editor
Department of Information, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: indoor positioning; smart sensors; ultrasonic sensors; energy harvesting
Dr. Massimo Merenda
Website
Guest Editor
Department of Information, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: internet of things; silicon sensors; integrated sensors; RFID; energy harvesting; embedded systems; edge machine learning
Dr. Demetrio Iero
Website
Guest Editor
Department of Information, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: indoor positioning; smart sensors; energy harvesting; solar systems

Special Issue Information

Dear Colleagues,

There is an increasing interest about indoor positioning, which is an emerging technology with a wide range of applications. Accurate and real-time positioning enables augmented and mixed reality applications, human–machine and home automation gestural interfaces, and navigation in shopping centers. Relevant applications include robotics, acquiring the position of flexible arms, navigation of unmanned automatic vehicles, security, virtual fencing of sensitive locations, safety, and preventing accidents through the recognition of dangerous postures and positions in workers. Further fields of application include medicine, such as monitoring elderly people’s movements or rehabilitative exercises; logistics, such as the positioning of goods in warehouses; sport, such as monitoring body and limb position during training exercises and in game consoles.

At present, research effort needs to be directed to new algorithms, architectures, sensor technologies, coverage, power consumption, size, and increased spatial and temporal resolution of indoor positioning systems, based on the physical and economic constraints of the various applications. In this framework, we are glad to edit this Special Issue on “Sensors and Systems for Indoor Positioning". Original contributions focused on systems and technologies to enable the indoor applications listed above are welcome.

Prof. Dr. Riccardo Carotenuto
Dr. Massimo Merenda
Dr. Demetrio Iero
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 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. 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 2200 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 positioning
  • positioning strategies
  • position sensors
  • acoustic emitters and sensors for positioning
  • magnetic positioning sensors
  • Bluetooth and WiFi positioning sensors
  • positioning systems and infrastructures
  • positioning algorithms
  • active and passive positioning
  • sensorless positioning
  • positioning deep learning

Published Papers (6 papers)

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Open AccessArticle
Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting
Sensors 2021, 21(2), 432; https://doi.org/10.3390/s21020432 - 09 Jan 2021
Abstract
A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi [...] Read more.
A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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Open AccessArticle
Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning
Sensors 2020, 20(22), 6470; https://doi.org/10.3390/s20226470 - 12 Nov 2020
Abstract
To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), [...] Read more.
To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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Open AccessArticle
Indoor Localization Based on Infrared Angle of Arrival Sensor Network
Sensors 2020, 20(21), 6278; https://doi.org/10.3390/s20216278 - 04 Nov 2020
Cited by 1
Abstract
Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of [...] Read more.
Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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Open AccessArticle
Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication
Sensors 2020, 20(16), 4432; https://doi.org/10.3390/s20164432 - 08 Aug 2020
Cited by 1
Abstract
The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest [...] Read more.
The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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Open AccessArticle
Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning
Sensors 2020, 20(12), 3548; https://doi.org/10.3390/s20123548 - 23 Jun 2020
Abstract
Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on [...] Read more.
Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points—namely, the linear chirp—due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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Open AccessLetter
A Recursive Algorithm for Indoor Positioning Using Pulse-Echo Ultrasonic Signals
Sensors 2020, 20(18), 5042; https://doi.org/10.3390/s20185042 - 04 Sep 2020
Abstract
Low frequency ultrasounds in air are widely used for real-time applications in short-range communication systems and environmental monitoring, in both structured and unstructured environments. One of the parameters widely evaluated in pulse-echo ultrasonic measurements is the time of flight (TOF), which can be [...] Read more.
Low frequency ultrasounds in air are widely used for real-time applications in short-range communication systems and environmental monitoring, in both structured and unstructured environments. One of the parameters widely evaluated in pulse-echo ultrasonic measurements is the time of flight (TOF), which can be evaluated with an increased accuracy and complexity by using different techniques. Hereafter, a nonstandard cross-correlation method is investigated for TOF estimations. The procedure, based on the use of template signals, was implemented to improve the accuracy of recursive TOF evaluations. Tests have been carried out through a couple of 60 kHz custom-designed polyvinylidene fluoride (PVDF) hemicylindrical ultrasonic transducers. The experimental results were then compared with the standard threshold and cross-correlation techniques for method validation and characterization. An average improvement of 30% and 19%, in terms of standard error (SE), was observed. Moreover, the experimental results evidenced an enhancement in repeatability of about 10% in the use of a recursive positioning system. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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