Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Machine Learning in Internet of Things and Indoor Positioning/Localization

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 12680

Share This Special Issue

Special Issue Editor

Dr. Seyed Ali Ghorashi

E-Mail Website
Guest Editor

Department of Computer Science & Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London E16 2RD, UK
Interests: artificial intelligence; machine learning; Internet of Things; positioning; wireless networks

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is used where different smart devices including sensors and/or actuators are connected to the Internet through wireless technology for monitoring, sharing information, intelligent analysis, and decision-making purposes to pave the way towards a better-connected society and to enhance object-related services with more advanced analysis.

Among these services, location-aware or location-based services are becoming essential in different IoT applications such as health, industrial, civil, and safety-critical ones, and now, highly precise positioning technologies have attracted significant attention from researchers, particularly for indoor environments where no global navigation satellite system (GNSS) is dominantly available and positioning/localization is more challenging due to constraints of noise, cost, energy, and nonstationary data.

In recent years, machine learning (ML) has been playing a growing role in IoT and positioning/localization applications, driven by the theoretical and technological advances in data science. Therefore, using ML in IoT and the positioning/localization technologies, especially in indoor environments, is drawing great attention from researchers of academia and industry, in various research areas.

This Special Issue aims to collect original cutting-edge research advances in the area of using ML for future IoT and indoor positioning/localization systems. Potential topics include but are not limited to the following:

ML for physical layers of IoT and positioning/localization systems.
ML for cross-layer solutions of IoT and positioning/localization systems.
ML for 5G and beyond related IoT and positioning/localization systems.
ML for novel sensory data-acquisition techniques.
ML for security, privacy, and trust aspects of IoT and positioning/localization systems.
ML for crowd-sensing-based IoT and positioning/localization systems.
ML for data quality management of IoT and positioning/localization systems.
ML for signal-strength-based positioning/localization, fingerprinting.
ML for inertial-sensor-based positioning/localization, sensor fusion.
ML for ultra-wideband (UWB)-based positioning/localization.
ML for RFID-based positioning/localization.
ML for vision-based positioning/localization.
ML for hybrid or collaborative positioning/localization frameworks.
ML for indoor tracking.
ML for making indoor data structures and models.
ML for navigating between outdoor and indoor models.
ML for human or robot activity detection and monitoring.
ML for blockchain IoT and positioning/localization systems.
ML for big data processing and analysis in IoT and positioning/localization systems.
ML for location-based applications and services.
ML for positioning/localization methods with robust, reliable, energy-efficient performance.
ML for opportunistic positioning/localization (using existing signals in the environment).
ML for indoor navigation or activity recommendation.
The role of ML in making positioning/localization a service in IoT systems.
Deep learning for IoT and positioning/localization systems.
Reinforced learning for IoT and positioning/localization systems.
Decision trees for IoT and positioning/localization systems.
Artificial neural networks for IoT and positioning/localization systems.
Deep reinforcement learning for IoT and positioning/localization systems.
Distributed ML models for IoT and positioning/localization systems.
Fundamental limits of positioning/localization techniques.
ML-based positioning/localization applications for IoT networks.
Deployment, test-bed, experimental experiences, and innovative applications of ML in IoT and positioning/localization systems.

Dr. Seyed Ali Ghorashi
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

machine learning
Internet of Things
positioning
localization
indoor
fingerprinting

Published Papers (5 papers)

Download All Papers

Research

Jump to: Review

21 pages, 5494 KiB

Open AccessArticle

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

by Mahnaz Chahoushi, Mohammad Nabati, Reza Asvadi and Seyed Ali Ghorashi

Sensors 2023, 23(7), 3591; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073591 - 30 Mar 2023

Cited by 3 | Viewed by 1634

Abstract

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pretrained encoder which is trained using only a limited number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier’s layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level. Full article

(This article belongs to the Special Issue Machine Learning in Internet of Things and Indoor Positioning/Localization)

► Show Figures

Figure 1

20 pages, 3016 KiB

Open AccessArticle

Research on Indoor 3D Positioning Algorithm Based on WiFi Fingerprint

by Lixing Wang, Shuang Shang and Zhenning Wu

Sensors 2023, 23(1), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010153 - 23 Dec 2022

Cited by 8 | Viewed by 2476

Abstract

Indoor 3D positioning is useful in multistory buildings, such as shopping malls, libraries, and airports. This study focuses on indoor 3D positioning using wireless access points (AP) in an environment without adding additional hardware facilities in large-scale complex places. The integration of a deep learning algorithm into indoor 3D positioning is studied, and a 3D dynamic positioning model based on temporal fingerprints is proposed. In contrast to the traditional positioning models with a single input, the proposed method uses a sliding time window to build a temporal fingerprint chip as the input of the positioning model to provide abundant information for positioning. Temporal information can be used to distinguish locations with similar fingerprint vectors and to improve the accuracy and robustness of positioning. Moreover, deep learning has been applied for the automatic extraction of spatiotemporal features. A temporal convolutional network (TCN) feature extractor is proposed in this paper, which adopts a causal convolution mechanism, dilated convolution mechanism, and residual connection mechanism and is not limited by the size of the convolution kernel. It is capable of learning hidden information and spatiotemporal relationships from the input features and the extracted spatiotemporal features are connected with a deep neural network (DNN) regressor to fit the complex nonlinear mapping relationship between the features and position coordinates to estimate the 3D position coordinates of the target. Finally, an open-source public dataset was used to verify the performance of the localization algorithm. Experimental results demonstrated the effectiveness of the proposed positioning model and a comparison between the proposed model and existing models proved that the proposed model can provide more accurate three-dimensional position coordinates. Full article

(This article belongs to the Special Issue Machine Learning in Internet of Things and Indoor Positioning/Localization)

► Show Figures

Figure 1

10 pages, 373 KiB

Open AccessArticle

A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

by Tingwei Zhang, Peng Zhang, Paris Kalathas, Guangxin Wang and Huaping Liu

Sensors 2022, 22(17), 6404; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176404 - 25 Aug 2022

Cited by 3 | Viewed by 2286

Abstract

Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal’s angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low. Full article

(This article belongs to the Special Issue Machine Learning in Internet of Things and Indoor Positioning/Localization)

► Show Figures

Figure 1

16 pages, 973 KiB

Open AccessArticle

Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System

by Yuhang Wang, Kun Zhao, Zhengqi Zheng, Wenqing Ji, Shuai Huang and Difeng Ma

Sensors 2022, 22(9), 3179; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093179 - 21 Apr 2022

Cited by 4 | Viewed by 2040

Abstract

Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone. Full article

(This article belongs to the Special Issue Machine Learning in Internet of Things and Indoor Positioning/Localization)

► Show Figures

Figure 1

Review

Jump to: Research

20 pages, 422 KiB

Open AccessReview

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

by Reza Shahbazian, Giusy Macrina, Edoardo Scalzo and Francesca Guerriero

Sensors 2023, 23(7), 3551; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073551 - 28 Mar 2023

Cited by 5 | Viewed by 2439

Abstract

The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends. Full article

(This article belongs to the Special Issue Machine Learning in Internet of Things and Indoor Positioning/Localization)

► Show Figures