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Measurements of User and Sensor Data from the Internet of...
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Measurements of User and Sensor Data from the Internet of Things (IoT) Devices

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 40618

Special Issue Editors

Dr. Aleksandr Ometov

E-Mail Website
Guest Editor
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere‎, Finland
Interests: wireless communications; information security; authentication; distributed systems; blockchain; resource-constrained devices; wearable technology
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquín Torres-Sospedra

E-Mail Website
Guest Editor
ALGORITMI Research Centre, Universidade do Minho, 4800-058 Guimarães, Portugal
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localization and positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of modern cyber-physical systems and the tremendous growth in the number of interconnected Internet of Things (IoT) devices is already paving new ways for improved data collection and processing methods development.

Modern devices, including smartphones, wearables, sensors and actuators, generate tremendous amounts of data related to both human factors (biometrics, behavior, contact-tracing, etc.) as well as general environmental monitoring information (humidity, temperature, tracking, etc.) being collected and analyzed by the data scientists all over the world. However, most of the collected measurements are only available for a small range of people deeply involved in its actual collection or processing.

This Special Issue is devoted but not limited to data sets including any raw data collected by different IoT devices, supplemented by methods, algorithms, sensor fusion models and related aspects of such kind of data for wireless communications, tracking, personal data processing, positioning, eHealth monitoring, sport analysis, among others.

Dr. Aleksandr Ometov
Dr. Joaquín Torres-Sospedra
Guest Editors

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Keywords

  • Biometric data
  • Contact-tracing data
  • Crowdsourced data
  • Data analysis
  • Data mining
  • Data prepared for machine learning applications
  • Data processing
  • Data profiling
  • Dataset comparisons
  • Dataset qualities
  • eHealth datasets
  • Healthcare data
  • Indoor Positioning
  • Location tracking Measurements datasets
  • Medical datasets
  • Open datasets
  • Personal Area Networks
  • Personal data
  • Positioning datasets
  • Security datasets
  • Social data

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Published Papers (6 papers)

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Research

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19 pages, 2945 KiB  
Article
TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis
by Justin Trautmann, Lin Zhou, Clemens Markus Brahms, Can Tunca, Cem Ersoy, Urs Granacher and Bert Arnrich
Data 2021, 6(9), 95; https://doi.org/10.3390/data6090095 - 26 Aug 2021
Cited by 10 | Viewed by 6388
Abstract
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order [...] Read more.
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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Review

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18 pages, 2416 KiB  
Review
The Comparison of Cybersecurity Datasets
by Ahmed Alshaibi, Mustafa Al-Ani, Abeer Al-Azzawi, Anton Konev and Alexander Shelupanov
Data 2022, 7(2), 22; https://doi.org/10.3390/data7020022 - 29 Jan 2022
Cited by 22 | Viewed by 8771
Abstract
Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models [...] Read more.
Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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Other

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10 pages, 13286 KiB  
Data Descriptor
Large-Scale Dataset for the Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
by Usman Ali, Giuseppe Caso, Luca De Nardis, Konstantinos Kousias, Mohammad Rajiullah, Özgü Alay, Marco Neri, Anna Brunstrom and Maria-Gabriella Di Benedetto
Data 2022, 7(3), 34; https://doi.org/10.3390/data7030034 - 15 Mar 2022
Cited by 17 | Viewed by 5340
Abstract
Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset [...] Read more.
Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3–3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde & Schwarz (R&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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12 pages, 2596 KiB  
Data Descriptor
Indoor Environment Dataset to Estimate Room Occupancy
by Andreé Vela, Joanna Alvarado-Uribe and Hector G. Ceballos
Data 2021, 6(12), 133; https://doi.org/10.3390/data6120133 - 13 Dec 2021
Cited by 6 | Viewed by 4676
Abstract
The estimation of occupancy is a crucial contribution to achieve improvements in energy efficiency. The drawback of data or incomplete data related to occupancy in enclosed spaces makes it challenging to develop new models focused on estimating occupancy with high accuracy. Furthermore, considerable [...] Read more.
The estimation of occupancy is a crucial contribution to achieve improvements in energy efficiency. The drawback of data or incomplete data related to occupancy in enclosed spaces makes it challenging to develop new models focused on estimating occupancy with high accuracy. Furthermore, considerable variation in the monitored spaces also makes it difficult to compare the results of different approaches. This dataset comprises the indoor environmental information (pressure, altitude, humidity, and temperature) and the corresponding occupancy level for two different rooms: (1) a fitness gym and (2) a living room. The fitness gym data were collected for six days between 18 September and 2 October 2019, obtaining 10,125 objects with a 1 s resolution according to the following occupancy levels: low (2442 objects), medium (5325 objects), and high (2358 objects). The living room data were collected for 11 days between 14 May and 4 June 2020, obtaining 295,823 objects with a 1 s resolution, according to the following occupancy levels: empty (50,978 objects), low (202,613 objects), medium (35,410 objects), and high (6822 objects). Additionally, the number of fans turned on is provided for the living room data. The data are publicly available in the Mendeley Data repository. This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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Graphical abstract

20 pages, 3914 KiB  
Data Descriptor
Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor
by Pavel Masek, Martin Stusek, Ekaterina Svertoka, Jan Pospisil, Radim Burget, Elena Simona Lohan, Ion Marghescu, Jiri Hosek and Aleksandr Ometov
Data 2021, 6(6), 62; https://doi.org/10.3390/data6060062 - 10 Jun 2021
Cited by 13 | Viewed by 4872
Abstract
This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It [...] Read more.
This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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13 pages, 1463 KiB  
Data Descriptor
IntelliRehabDS (IRDS)—A Dataset of Physical Rehabilitation Movements
by Alina Miron, Noureddin Sadawi, Waidah Ismail, Hafez Hussain and Crina Grosan
Data 2021, 6(5), 46; https://doi.org/10.3390/data6050046 - 30 Apr 2021
Cited by 22 | Viewed by 8327
Abstract
In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect [...] Read more.
In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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