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Feature Papers in the Internet of Things Section 2023

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8446

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Dr. Raffaele Bruno

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Institute for Informatics and Telematics (IIT), National Research Council of Italy (CNR), Via G. Moruzzi, 1, I-56124 Pisa, Italy
Interests: MAC protocols for wireless networks; architectures and protocols for the Internet of Things; vehicular networks; 5G networks; smart transportation; smart grids and smart buildings
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Prof. Dr. Leopoldo Angrisani

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Department of Information Technology and Electrical Engineering, CeSMA—Center of Advanced Measurement and Technology Services, University of Napoli Federico II, Naples, Italy
Interests: communication systems and networks test and measurement; measurements for Internet of Things applications; compressive sampling based measurements; measurements for Industry 4.0; measurement uncertainty
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Dr. Nikos Fotiou

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Mobile Multimedia Laboratory, Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business, 104 34 Athens, Greece
Interests: access control; blockchain technologies; cryptography; information-centric networking; IoT; privacy; security; web technologies
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Dr. Ismail Butun

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School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Interests: cyber-security; IoT; IIoT; computer networks
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Special Issue Information

Dear Colleagues,

We are pleased to announce that the section Internet of Things is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from EBMs.

We expect original papers and review articles that show state-of-the-art, theoretical, and applicative advances, new experimental discoveries, and novel technological improvements regarding the Internet of Things. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted.

We would also like to take this opportunity to call on more excellent scholars to join the section Internet of Things so that we can work together to further develop this exciting field of research.

Dr. Raffaele Bruno
Prof. Dr. Leopoldo Angrisani
Dr. Nikos Fotiou
Dr. Ismail Butun
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.

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Feature Review Papers in the Internet of Things Section 2024 in Sensors
Feature Papers in the Internet of Things Section 2024 in Sensors (1 article)

Published Papers (8 papers)

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Research

11 pages, 564 KiB

Open AccessArticle

A New Model of Air–Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors

by Gabryel F. Soares, Gilberto Fernandes, Jr., Otacílio M. Almeida, Gildario D. Lima and Joel J. P. C. Rodrigues

Sensors 2024, 24(5), 1481; https://doi.org/10.3390/s24051481 - 24 Feb 2024

Viewed by 511

Abstract

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air–oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli’s equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH₂O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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18 pages, 2614 KiB

Open AccessArticle

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing

by Fatemeh Asgarinejad, Xiaofan Yu, Danlin Jiang, Justin Morris, Tajana Rosing and Baris Aksanli

Sensors 2024, 24(3), 1014; https://doi.org/10.3390/s24031014 - 04 Feb 2024

Viewed by 817

Abstract

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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20 pages, 7225 KiB

Open AccessArticle

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

by Saul Davila-Gonzalez and Sergio Martin

Sensors 2024, 24(2), 655; https://doi.org/10.3390/s24020655 - 19 Jan 2024

Viewed by 1295

Abstract

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers’ psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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19 pages, 7811 KiB

Open AccessArticle

AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats

by Pedro Blanco-Carmona, Lucía Baeza-Moreno, Eduardo Hidalgo-Fort, Rubén Martín-Clemente, Ramón González-Carvajal and Fernando Muñoz-Chavero

Sensors 2023, 23(24), 9733; https://doi.org/10.3390/s23249733 - 10 Dec 2023

Viewed by 1008

Abstract

A significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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29 pages, 2973 KiB

Open AccessArticle

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

by Bram van Berlo, Camiel Oerlemans, Francesca Luigia Marogna, Tanir Ozcelebi and Nirvana Meratnia

Sensors 2023, 23(23), 9534; https://doi.org/10.3390/s23239534 - 30 Nov 2023

Cited by 1 | Viewed by 716

Abstract

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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15 pages, 11151 KiB

Open AccessArticle

SMDFbs: Specification-Based Misbehavior Detection for False Base Stations

by Hoonyong Park, Philip Virgil Berrer Astillo, Yongho Ko, Yeongshin Park, Taeguen Kim and Ilsun You

Sensors 2023, 23(23), 9504; https://doi.org/10.3390/s23239504 - 29 Nov 2023

Viewed by 669

Abstract

The advancement of cellular communication technology has profoundly transformed human life. People can now watch high-definition videos anytime, anywhere, and aim for the implementation of advanced autonomous driving capabilities. However, the sustainability of such an environment is threatened by false base stations. False base stations execute attacks in the Radio Access Network (RAN) of cellular systems, adversely affecting the network or its users. To address this challenge, we propose a behavior rule specification-based false base station detection system, SMDFbs. We derive behavior rules from the normal operations of base stations and convert these rules into a state machine. Based on this state machine, we detect network anomalies and mitigate threats. We conducted experiments detecting false base stations in a 5G RAN simulator, comparing our system with seven machine learning-based detection techniques. The experimental results showed that our proposed system achieved a detection accuracy of 98% and demonstrated lower overhead compared to other algorithms. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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11 pages, 1407 KiB

Open AccessArticle

Investigating Pathways to Minimize Sensor Power Usage for the Internet of Remote Things

by Tiana Cristina Majcan, Solomon Ould and Nick S. Bennett

Sensors 2023, 23(21), 8871; https://doi.org/10.3390/s23218871 - 31 Oct 2023

Viewed by 678

Abstract

The Internet of Remote Things (IoRT) offers an exciting landscape for the development and deployment of remote wireless sensing nodes (WSNs) which can gather useful environmental data. Low Power Wide Area Networks (LPWANs) provide an ideal network topology for enabling the IoRT, but due to the remote location of these WSNs, the power and energy requirements for such systems must be accurately determined before deployment, as devices will be running on limited energy resources, such as long-life batteries or energy harvesting. Various sensor modules that are available on the consumer market are suitable for these applications; however, the exact power requirements and characteristics of the sensor are often not stated in datasheets, nor verified experimentally. This study details an experimental procedure where the energy requirements are measured for various sensor modules that are available for Arduino and other microcontroller units (MCUs). First, the static power consumption of continually powered sensors was measured. The impact of sensor warm-up time, associated with powering on the sensor and waiting for reliable measurements, is also explored. Finally, the opportunity to reduce power for sensors which have multiple outputs was investigated to see if there is any significant reduction in power consumption when obtaining readings from fewer outputs than all that are available. It was found that, generally, CO₂ and soil moisture sensors have a large power requirement when compared with temperature, humidity and pressure sensors. Limiting multiple sensor outputs was shown not to reduce power consumption. The warm-up time for analog sensors and digital sensors was generally negligible and in the order of 10–50 ms. However, one CO₂ sensor had a large overhead warm-up time of several seconds which added a significant energy burden. It was found that more, or as much, power could be consumed during warm-up as during the actual measurement phase. Finally, this study found disparity between power consumption values in datasheets and experimental measurements, which could have significant consequences in terms of battery life in the field. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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43 pages, 987 KiB

Open AccessArticle

Yet Another Compact Time Series Data Representation Using CBOR Templates (YACTS)

by Sebastian Molina Araque, Ivan Martinez, Georgios Z. Papadopoulos, Nicolas Montavont and Laurent Toutain

Sensors 2023, 23(11), 5124; https://doi.org/10.3390/s23115124 - 27 May 2023

Viewed by 1864

Abstract

The Internet of Things (IoT) technology is growing rapidly, while the IoT devices are being deployed massively. However, interoperability with information systems remains a major challenge for this accelerated device deployment. Furthermore, most of the time, IoT information is presented as Time Series (TS), and while the majority of the studies in the literature focus on the prediction, compression, or processing of TS, no standardized representation format has emerged. Moreover, apart from interoperability, IoT networks contain multiple constrained devices which are designed with limitations, e.g., processing power, memory, or battery life. Therefore, in order to reduce the interoperability challenges and increase the lifetime of IoT devices, this article introduces a new format for TS based on CBOR. The format exploits the compactness of CBOR by leveraging delta values to represent measurements, employing tags to represent variables, and utilizing templates to convert the TS data representation into the appropriate format for the cloud-based application. Moreover, we introduce a new refined and structured metadata to represent additional information for the measurements, then we provide a Concise Data Definition Language (CDDL) code to validate the CBOR structures against our proposal, and finally, we present a detailed performance evaluation to validate the adaptability and the extensibility of our approach. Our performance evaluation results show that the actual data sent by IoT devices can be reduced by between 88% and 94% compared to JavaScript Object Notation (JSON), between 82% and 91% compared to Concise Binary Object Representation (CBOR) and ASN.1, and between 60% and 88% compared to Protocol buffers. At the same time, it can reduce Time-on-Air by between 84% and 94% when a Low Power Wide Area Networks (LPWAN) technology such as LoRaWAN is employed, leading to a 12-fold increase in battery life compared to CBOR format or between a 9-fold and 16-fold increase when compared to Protocol buffers and ASN.1, respectively. In addition, the proposed metadata represent an additional 0.5% of the overall data transmitted in cases where networks such as LPWAN or Wi-Fi are employed. Finally, the proposed template and data format provide a compact representation of TS that can significantly reduce the amount of data transmitted containing the same information, extend the battery life of IoT devices, and improve their lifetime. Moreover, the results show that the proposed approach is effective for different data types and it can be integrated seamlessly into existing IoT systems. Full article

(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2023)

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