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Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments

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A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 25997

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Special Issue Editors

Dr. Lei Jing

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Guest Editor

Graduate School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizuwakamatsu 965-8580, Japan
Interests: motion capture; wearable sensors; smart devices
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yoshinori Matsumoto

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Guest Editor

Department of Applied Physics and Physico-Informatics, Keio University, Yokohama 223-8522, Japan
Interests: sensor; IoT; sensor network; microprocessor/circuit; machine learning

Prof. Dr. Zhan Zhang

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Guest Editor

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: wearable computing; edge computing; human–computer interaction; fault tolerant computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

The rapid development of the Internet of Things (IoT) has revolutionized the way in which we interact with our environment, with countless sensors collecting and transmitting data in real time. This has led to a myriad of innovative applications being developed across a wide range of domains, including smart cities, healthcare, agriculture, transportation, and industry. We invite researchers, engineers, and practitioners to submit original research and review articles that explore the state of the art, the challenges and opportunities associated with IoT sensors, and their broader applications.

We welcome high-quality submissions that advance the understanding of IoT sensors and their diverse applications, as well as those that discuss the challenges and opportunities associated with IoT sensor deployment in various domains. The goal of this Special Issue is to foster interdisciplinary research that combines expertise from fields such as engineering, computer science, data science, and domain-specific application areas.

Dr. Lei Jing
Prof. Dr. Yoshinori Matsumoto
Prof. Dr. Zhan Zhang
Guest Editors

Manuscript Submission Information

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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. Micromachines is an international peer-reviewed open access monthly 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

advanced IoT sensor design and fabrication
energy harvesting and power management for IoT sensors
IoT sensor networks: architecture, protocols, and communication
data processing and analytics for IoT sensor data
IoT sensors for smart cities, homes, and infrastructure
IoT sensors in healthcare, agriculture, and manufacturing
security, privacy, and trust in IoT sensor networks
IoT sensor applications in environmental monitoring and disaster management
human-computer interaction (HCI) with IoT sensor systems
emerging trends and future directions in IoT sensor technology

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Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 157 KiB

Open AccessEditorial

Editorial for the Special Issue on Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments

by Lei Jing, Yoshinori Matsumoto and Zhan Zhang

Micromachines 2024, 15(8), 1048; https://doi.org/10.3390/mi15081048 - 18 Aug 2024

Viewed by 3780

Abstract

As the editor of the Special Issue on “Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments”, I am delighted to present this collection of groundbreaking research that addresses the emerging needs and challenges in the field of IoT [...] Read more.

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

Research

Jump to: Editorial

18 pages, 5076 KiB

Open AccessArticle

Gesture-Controlled Robotic Arm for Agricultural Harvesting Using a Data Glove with Bending Sensor and OptiTrack Systems

by Zeping Yu, Chenghong Lu, Yunhao Zhang and Lei Jing

Micromachines 2024, 15(7), 918; https://doi.org/10.3390/mi15070918 - 16 Jul 2024

Cited by 2 | Viewed by 1152

Abstract

This paper presents a gesture-controlled robotic arm system designed for agricultural harvesting, utilizing a data glove equipped with bending sensors and OptiTrack systems. The system aims to address the challenges of labor-intensive fruit harvesting by providing a user-friendly and efficient solution. The data glove captures hand gestures and movements using bending sensors and reflective markers, while the OptiTrack system ensures high-precision spatial tracking. Machine learning algorithms, specifically a CNN+BiLSTM model, are employed to accurately recognize hand gestures and control the robotic arm. Experimental results demonstrate the system’s high precision in replicating hand movements, with a Euclidean Distance of 0.0131 m and a Root Mean Square Error (RMSE) of 0.0095 m, in addition to robust gesture recognition accuracy, with an overall accuracy of 96.43%. This hybrid approach combines the adaptability and speed of semi-automated systems with the precision and usability of fully automated systems, offering a promising solution for sustainable and labor-efficient agricultural practices. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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34 pages, 5929 KiB

Open AccessArticle

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human–Computer Interaction

by Pontakorn Sonchan, Neeranut Ratchatanantakit, Nonnarit O-Larnnithipong, Malek Adjouadi and Armando Barreto

Micromachines 2024, 15(4), 553; https://doi.org/10.3390/mi15040553 - 21 Apr 2024

Cited by 2 | Viewed by 3665

Abstract

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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18 pages, 6164 KiB

Open AccessArticle

Geometry Scaling for Externally Balanced Cascade Deterministic Lateral Displacement Microfluidic Separation of Multi-Size Particles

by Heyu Yin, Sylmarie Dávila-Montero and Andrew J. Mason

Micromachines 2024, 15(3), 405; https://doi.org/10.3390/mi15030405 - 17 Mar 2024

Cited by 1 | Viewed by 1709

Abstract

To non-invasively monitor personal biological and environmental samples in Internet of Things (IoT)-based wearable microfluidic sensing applications, the particle size could be key to sensing, which emphasizes the need for particle size fractionation. Deterministic lateral displacement (DLD) is a microfluidic structure that has shown great potential for the size fractionation of micro- and nano-sized particles. This paper introduces a new externally balanced multi-section cascade DLD approach with a section-scaling technique aimed at expanding the dynamic range of particle size separation. To analyze the design tradeoffs of this new approach, a robust model that also accounts for practical fabrication limits is presented, enabling designers to visualize compromises between the overall device size and the achievement of various performance goals. Furthermore, results show that a wide variety of size fractionation ranges and size separation resolutions can be achieved by cascading multiple sections of an increasingly smaller gap size and critical separation dimension. Model results based on DLD theoretical equations are first presented, followed by model results that apply the scaling restrictions associated with the second order of effects, including practical fabrication limits, the gap/pillar size ratio, and pillar shape. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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18 pages, 9153 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Application of Braided Piezoelectric Poly-l-Lactic Acid Cord Sensor to Sleep Bruxism Detection System with Less Physical or Mental Stress

by Yoshiro Tajitsu, Saki Shimda, Takuto Nonomura, Hiroki Yanagimoto, Shun Nakamura, Ryoma Ueshima, Miyu Kawanobe, Takuo Nakiri, Jun Takarada, Osamu Takeuchi, Rei Nisho, Koji Takeshita, Mitsuru Takahashi and Kazuki Sugiyama

Micromachines 2024, 15(1), 86; https://doi.org/10.3390/mi15010086 - 30 Dec 2023

Cited by 1 | Viewed by 2359

Abstract

For many years, we have been developing flexible sensors made of braided piezoelectric poly-l-lactic acid (PLLA) fibers that can be tied and untied for practical applications in society. To ensure good quality of sleep, the occurrence of bruxism has been attracting attention in recent years. Currently, there is a need for a system that can easily and accurately measure the frequency of bruxism at home. Therefore, taking advantage of the braided piezoelectric PLLA cord sensor’s unique characteristic of being sewable, we aimed to provide a system that can measure the frequency of bruxism using the braided piezoelectric PLLA cord sensor simply sewn onto a bed sheet on which the subject lies down. After many tests using trial and error, the sheet sensor was completed with zigzag stitching. Twenty subjects slept overnight in a hospital room on sheets integrated with a braided piezoelectric PLLA cord. Polysomnography (PSG) was simultaneously performed on these subjects. The results showed that their bruxism could be detected with an accuracy of more than 95% compared with PSG measurements, which can only be performed in a hospital by a physician and are more burdensome for the subjects, with the subjects simply lying on the bed sheet with a braided piezoelectric PLLA cord sensor sewn into it. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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12 pages, 4464 KiB

Open AccessArticle

Low-Power Consumption IGZO Memristor-Based Gas Sensor Embedded in an Internet of Things Monitoring System for Isopropanol Alcohol Gas

by Myoungsu Chae, Doowon Lee and Hee-Dong Kim

Micromachines 2024, 15(1), 77; https://doi.org/10.3390/mi15010077 - 29 Dec 2023

Cited by 3 | Viewed by 1838

Abstract

Low-power-consumption gas sensors are crucial for diverse applications, including environmental monitoring and portable Internet of Things (IoT) systems. However, the desorption and adsorption characteristics of conventional metal oxide-based gas sensors require supplementary equipment, such as heaters, which is not optimal for low-power IoT monitoring systems. Memristor-based sensors (gasistors) have been investigated as innovative gas sensors owing to their advantages, including high response, low power consumption, and room-temperature (RT) operation. Based on IGZO, the proposed isopropanol alcohol (IPA) gas sensor demonstrates a detection speed of 105 s and a high response of 55.15 for 50 ppm of IPA gas at RT. Moreover, rapid recovery to the initial state was achievable in 50 μs using pulsed voltage and without gas purging. Finally, a low-power circuit module was integrated for wireless signal transmission and processing to ensure IoT compatibility. The stability of sensing results from gasistors based on IGZO has been demonstrated, even when integrated into IoT systems. This enables energy-efficient gas analysis and real-time monitoring at ~0.34 mW, supporting recovery via pulse bias. This research offers practical insights into IoT gas detection, presenting a wireless sensing system for sensitive, low-powered sensors. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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20 pages, 9986 KiB

Open AccessArticle

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

by Madiha Javeed, Maha Abdelhaq, Asaad Algarni and Ahmad Jalal

Micromachines 2023, 14(12), 2204; https://doi.org/10.3390/mi14122204 - 3 Dec 2023

Cited by 2 | Viewed by 2254

Abstract

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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20 pages, 9709 KiB

Open AccessFeature PaperArticle

HRBUST-LLPED: A Benchmark Dataset for Wearable Low-Light Pedestrian Detection

by Tianlin Li, Guanglu Sun, Linsen Yu and Kai Zhou

Micromachines 2023, 14(12), 2164; https://doi.org/10.3390/mi14122164 - 28 Nov 2023

Cited by 1 | Viewed by 1396

Abstract

Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, we introduce a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras. Most of the data were gathered under starlight-level illumination. Our dataset annotates 32,148 pedestrian instances in 4269 keyframes. The pedestrian density reaches high values with more than seven people per image. We provide four lightweight, low-light pedestrian detection models based on advanced YOLOv5 and YOLOv8. By training the models on public datasets and fine-tuning them on the HRBUST-LLPED dataset, our model obtained 69.90% in terms of [email protected]:0.95 and 1.6 ms for the inference time. The experiments demonstrate that our research can assist in advancing pedestrian detection research by using low-light cameras in wearable devices. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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23 pages, 5984 KiB

Open AccessArticle

Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication

by Liufeng Fan, Zhan Zhang, Biao Zhu, Decheng Zuo, Xintong Yu and Yiwei Wang

Micromachines 2023, 14(11), 2050; https://doi.org/10.3390/mi14112050 - 31 Oct 2023

Cited by 5 | Viewed by 2544

Abstract

This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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11 pages, 3112 KiB

Open AccessArticle

Health Monitoring System from Pyralux Copper-Clad Laminate Film and Random Forest Algorithm

by Chi Cuong Vu, Jooyong Kim and Thanh-Hai Nguyen

Micromachines 2023, 14(9), 1726; https://doi.org/10.3390/mi14091726 - 1 Sep 2023

Cited by 2 | Viewed by 1440

Abstract

Sensor technologies have been core features for various wearable electronic products for decades. Their functions are expected to continue to play an essential role in future generations of wearable products. For example, trends in industrial, military, and security applications include smartwatches used for monitoring medical indicators, hearing devices with integrated sensor options, and electronic skins. However, many studies have focused on a specific area of the system, such as manufacturing processes, data analysis, or actual testing. This has led to challenges regarding the reliability, accuracy, or connectivity of components in the same wearable system. There is an urgent need for studies that consider the whole system to maximize the efficiency of soft sensors. This study proposes a method to fabricate a resistive pressure sensor with high sensitivity, resilience, and good strain tolerance for recognizing human motion or body signals. Herein, the sensor electrodes are shaped on a thin Pyralux film. A layer of microfiber polyesters, coated with carbon nanotubes, is used as the bearing and pressure sensing layer. Our sensor shows superior capabilities in respiratory monitoring. More specifically, the sensor can work in high-humidity environments, even when immersed in water—this is always a big challenge for conventional sensors. In addition, the embedded random forest model, built for the application to recognize restoration signals with high accuracy (up to 92%), helps to provide a better overview when placing flexible sensors in a practical system. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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11 pages, 3493 KiB

Open AccessArticle

Flexible Pressure Sensors and Machine Learning Algorithms for Human Walking Phase Monitoring

by Thanh-Hai Nguyen, Ba-Viet Ngo, Thanh-Nghia Nguyen and Chi Cuong Vu

Micromachines 2023, 14(7), 1411; https://doi.org/10.3390/mi14071411 - 13 Jul 2023

Cited by 5 | Viewed by 1830

Abstract

Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and healthcare. However, most of these studies have focused on a specific area, such as fabrication, data analysis, or experimentation. This approach can lead to challenges regarding the reliability, accuracy, or connectivity of the components. Therefore, there is a pressing need to consider the sensor’s placement in an overall system and find ways to maximize the efficiency of such flexible sensors. This paper proposes a fabrication method for soft capacitive pressure sensors with spacer fabric, conductive inks, and encapsulation glue. The sensor exhibits a good sensitivity of 0.04 kPa⁻¹, a fast recovery time of 7 milliseconds, and stability of 10,000 cycles. We also evaluate how to connect the sensor to other traditional sensors or hardware components. Some machine learning models are applied to these built-in soft sensors. As expected, the embedded wearables achieve a high accuracy of 96% when recognizing human walking phases. Full article

(This article belongs to the Special Issue Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments)

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