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Smartphone Sensors for Indoor Positioning

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 33771

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Special Issue Editors

Dr. Imran Ashraf

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
Interests: indoor positioning and localization; indoor user navigation; location based services; data mining; sentiment analysis; sensors for autonomous vehicles (LIDAR); accident analysis and prevention; wireless positioning; magnetic field based positioning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yousaf Bin Zikria

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Korea
Interests: IoT, 5G, wireless communications and networks, opportunistic communications, wireless sensor networks, routing protocols, cognitive radio ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sadia Din

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Guest Editor

Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: Data Science; demosaicking and denoising; Artificial Intelligence; Deep Learning; Wireless Sensor Networks; Big Data; IoT

Special Issue Information

Dear Colleagues,

The wide proliferation of mobile devices, especially smartphones, has initiated several intuitive and diverse services including on-the-go services, online customer services, and location-based services (LBS), among others. Accurate location information serves as the backbone of LBS, which is offered both outdoors and indoors. Despite its importance, positioning faces several challenges, both indoor and outdoor. Sensors are being increasingly embedded in modern smartphones, which can be leveraged to estimate the user’s current location. In addition, 5G communication has opened new dimensions for cellular-based positioning. Novel algorithms, advanced models, sensor fusion solutions, and frameworks should be devised which can serve the requirements of LBS accuracy and provide the desired accuracy. Machine learning techniques and deep learning models can play a potential role in refining location information.

This Special Issue will focus on the publication of high-quality research articles and review papers that present advanced solutions using smartphone sensors for positioning and localization. Novel architectures, intuitive algorithms, and machine learning-based models are deemed valuable, though articles with experimental results will be highly regarded and appreciated.

Topics of interest include, but are not limited to, the following:

Indoor positioning using 4G and 5G cellular networks
Positioning using wireless local area network
Pedestrian dead reckoning systems
Machine and deep learning solutions using smartphone sensors
Bluetooth-based positioning
Outdoor positioning in challenging GPS and GPS denied environments
Ultra-wideband, infrared, and other custom positioning solutions
Sensor fusion frameworks and approaches involving smartphone sensors

Prof. Dr. Imran Ashraf
Prof. Dr. Yousaf Bin Zikria
Prof. Dr. Sadia Din
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.

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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

smartphone sensor-based positioning
machine and deep learning solutions
positioning in GPS-denied environments
positioning using 4G and 5G cellular networks
sensor fusion
simultaneous localization and mapping using smartphones
smartphone-based pedestrian dead reckoning

Published Papers (10 papers)

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Editorial

Jump to: Research

6 pages, 179 KiB

Open AccessEditorial

Smartphone Sensors for Indoor Positioning

by Imran Ashraf, Yongwan Park, Yousaf Bin Zikria and Sadia Din

Sensors 2023, 23(8), 3811; https://doi.org/10.3390/s23083811 - 7 Apr 2023

Viewed by 1631

Abstract

The explosive growth and wide proliferation of mobile devices, the majority of which are smartphones, led to the inception of several novel and intuitive services, including on-the-go services, online customer services, and location-based services (LBS) [...] Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

Research

Jump to: Editorial

51 pages, 25874 KiB

Open AccessArticle

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

by Abdullah M. AlSahly, Mohammad Mehedi Hassan, Kashif Saleem, Amerah Alabrah and Joel J. P. C. Rodrigues

Sensors 2022, 22(17), 6513; https://doi.org/10.3390/s22176513 - 29 Aug 2022

Cited by 2 | Viewed by 3140

Abstract

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation—in terms of either positioning or tracking—for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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25 pages, 9678 KiB

Open AccessArticle

Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities

by Muhammad Shoaib Farooq, Rana Muhammad Nadir, Furqan Rustam, Soojung Hur, Yongwan Park and Imran Ashraf

Sensors 2022, 22(16), 5950; https://doi.org/10.3390/s22165950 - 9 Aug 2022

Cited by 8 | Viewed by 2179

Abstract

Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffic is expected in the future where communication networks are expected to provide ubiquity, high quality of service, and on-demand content for a large number of interconnected devices. The sixth-generation (6G) network is considered the problem-solving network of futuristic cities, with huge bandwidth and low latency. The expected 6G of the radio access network is based on terahertz (THz) waves with the capability of carrying up to one terabit per second (Tbps). THz waves have the capability of carrying a large amount of data but these waves have several drawbacks, such as short-range and atmospheric attenuation. Hence, these problems can introduce complications and hamper the performance of the 6G network. This study envisions futuristic smart cities using 6G and proposes a conceptual terrestrial network (TN) architecture for 6G. The nested Bee Hive is a scalable multilayer architecture designed to meet the needs of futuristic smart cities. Moreover, we designed the multilayer network infrastructure while considering the expectations from a network of futuristic smart cities and the complications of THz waves. Extensive simulations are performed using different pathfinding algorithms in the 3D multilayer domain to evaluate the performance of the proposed architecture and set the dynamics of futuristic communication of 6G. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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21 pages, 1352 KiB

Open AccessArticle

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

by Muhammad Shadab Alam Hashmi, Muhammad Ibrahim, Imran Sarwar Bajwa, Hafeez-Ur-Rehman Siddiqui, Furqan Rustam, Ernesto Lee and Imran Ashraf

Sensors 2022, 22(5), 1983; https://doi.org/10.3390/s22051983 - 3 Mar 2022

Cited by 14 | Viewed by 4243

Abstract

The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engineer as a domain expert to differentiate between different rail tracks’ faults, which is cumbersome, laborious, and error-prone. This study proposes the use of traditional acoustic-based systems with deep learning models to increase performance and reduce train accidents. Two convolutional neural networks (CNN) models, convolutional 1D and convolutional 2D, and one recurrent neural network (RNN) model, a long short-term memory (LSTM) model, are used in this regard. Initially, three types of faults are considered, including superelevation, wheel burnt, and normal tracks. Contrary to traditional acoustic-based systems where the spectrogram dataset is generated before the model training, the proposed approach uses on-the-fly feature extraction by generating spectrograms as a deep learning model’s layer. Different lengths of audio samples are used to analyze their performance with each model. Each audio sample of 17 s is split into 3 variations of 1.7, 3.4, and 8.5 s, and all 3 deep learning models are trained and tested against each split time. Various combinations of audio data augmentation are analyzed extensively to investigate models’ performance. The results suggest that the LSTM with 8.5 split time gives the best results with the accuracy of 99.7%, the precision of 99.5%, recall of 99.5%, and F1 score of 99.5%. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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25 pages, 5187 KiB

Open AccessArticle

Analysis and Experiment of Wireless Optical Communications in Applications Dedicated to Mobile Devices with Applicability in the Field of Road and Pedestrian Safety

by Eduard Zadobrischi

Sensors 2022, 22(3), 1023; https://doi.org/10.3390/s22031023 - 28 Jan 2022

Cited by 9 | Viewed by 3360

Abstract

Current developments and the need for high-performance devices that provide safe and reliable communications present a future perspective by using visible light as an alternative solution that can substantially improve road and pedestrian safety. The daily use of smartphones is imperative; thus one can build on this premise a system dedicated to the aforementioned problem. However, the problem of the visible light communication channel (VLC) is highly dynamic and becomes extremely unpredictable in terms of exposure to noise sources. Developing applications dedicated to direct communications with infrastructure and vehicles using portable devices is becoming a challenge and at the same time a necessary solution. The article proposes the shaping of an emission–reception architecture dedicated to adaptive fuse light communications using OCC (optical camera communication) but also standard VLC communications using ambient light sensors via an Android application. This approach aims to provide a first step in shaping information-sharing applications using VLC communications. As far as we know, this approach has not been implemented in external VLC systems. The performance of the architecture and the application was demonstrated by practical tests that confirmed the capacity of the technology even if we are in the first stage. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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16 pages, 6873 KiB

Open AccessArticle

Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT

by Mohamed S. El-Gendy, Imran Ashraf and Samy El-Hennawey

Sensors 2022, 22(3), 797; https://doi.org/10.3390/s22030797 - 21 Jan 2022

Cited by 2 | Viewed by 2320

Abstract

Indoor positioning systems (IPS) have been regarded as essential for many applications, particularly for smartphones, during the past decade. With the internet of things (IoT), and especially device-to-device (D2D) cases, the client is supposed to have a very simple structure and low cost. It is also desirable that the client contains minimal software modules specifically for IPS purposes. This study proposes a new IPS technique that satisfies these conditions. The evaluation of the technique was previously executed based on a manual procedure. This technique utilizes Wi-Fi technology in addition to a new design of two orthogonal phased antenna arrays. This paper provides a complete design of a Wi-Fi access point (AP), considered as the proof of concept of a commercial AP. For the system to be fully automatic, the proposed architecture is based on a Raspberry Pi, external Wi-Fi modules, a powered universal serial bus (USB) hub, and two orthogonal phased antenna arrays. The phases of each antenna array are governed by extra-phase circuits as well as a radio frequency (RF) switch. Extensive design parameters have been chosen through parametric sweeps that satisfy the design conditions. Software testing results for the antenna arrays are included in this paper to show the feasibility and suitability of the proposed antenna array for IPS. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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21 pages, 2859 KiB

Open AccessArticle

Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors

by Hayat Khaloufi, Karim Abouelmehdi, Abderrahim Beni-Hssane, Furqan Rustam, Anca Delia Jurcut, Ernesto Lee and Imran Ashraf

Sensors 2021, 21(20), 6853; https://doi.org/10.3390/s21206853 - 15 Oct 2021

Cited by 12 | Viewed by 3922

Abstract

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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20 pages, 2121 KiB

Open AccessArticle

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis

by Rahman Shafique, Hafeez-Ur-Rehman Siddiqui, Furqan Rustam, Saleem Ullah, Muhammad Abubakar Siddique, Ernesto Lee, Imran Ashraf and Sandra Dudley

Sensors 2021, 21(18), 6221; https://doi.org/10.3390/s21186221 - 16 Sep 2021

Cited by 24 | Viewed by 5977

Abstract

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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14 pages, 3506 KiB

Open AccessArticle

Li-Pos: A Light Positioning Framework Leveraging OFDM for Visible Light Communication

by Jianbin Wu, Sami Ahmed Haider, Muhammad Irshad, Jehangir Arshad, Sohail M. Noman and Aparna Murthy

Sensors 2021, 21(13), 4310; https://doi.org/10.3390/s21134310 - 24 Jun 2021

Cited by 6 | Viewed by 2525

Abstract

The design of solid-state lighting is vital, as numerous metrics are involved in their exact positioning, and as it is utilized in various processes, ranging from intelligent buildings to the internet of things (IoT). This work aims to determine the power and delay spread from the light source to the receiver plane. The positions of the light source and receiver were used for power estimation. We focus on analog orthogonal frequency-division multiplexing (OFDM) in visible light communication (VLC) and assess the area under the curve (AUC). The proposed system was designed using modulation techniques (i.e., quadrature amplitude modulation; QAM) for visible light communication (VLC) and pulse-width modulation (PWM) for dimming sources. For the positioning and spreading of brightness, the proof-of-concept was weighted equally over the entire area. Therefore, the receiver plane was analyzed, in order to measure the power of each light-emitting diode (LED) in a given area, using the delayed mean square error (MSE). A framework was applied for the placement of LEDs, using full-width at half-maximum (FWHM) parameters with varying distances. Then, the received power was confirmed. The results show that the AUC using DRMS values for LEDs significantly increased (by

30 %

) when the number of source LEDs was changed from four to three. These results confirm that our system, associated with the simple linear lateration estimator, can achieve better energy consumption. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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23 pages, 9351 KiB

Open AccessArticle

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning

by Imran Ashraf, Sadia Din, Soojung Hur, Gunzung Kim and Yongwan Park

Sensors 2021, 21(10), 3533; https://doi.org/10.3390/s21103533 - 19 May 2021

Cited by 3 | Viewed by 2671

Abstract

Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure. Magnetic field positioning is an attractive solution for indoors; yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. The concept of DUST (device, user, space, time) and DOWTS (dynamicity, orientation, walk, trajectory, and sensor fusion) is introduced which divides the characteristics of the magnetic field dataset into basic and advanced groups and discusses the publicly available datasets accordingly. Full article

(This article belongs to the Special Issue Smartphone Sensors for Indoor Positioning)

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