Journal of Sensor and Actuator Networks

20 pages, 5332 KiB

Open AccessArticle

An Efficient Communication Protocol for Real-Time Body Sensor Data Acquisition and Feedback in Interactive Wearable Systems

by Armands Ancans, Modris Greitans and Sandis Kagis

J. Sens. Actuator Netw. 2025, 14(1), 4; https://doi.org/10.3390/jsan14010004 - 30 Dec 2024

Abstract

We introduce a novel wired communication approach for interactive wearable systems, employing a single signal wire and innovative group addressing protocol to reduce overhead. While wireless solutions dominate body sensor networks, wired approaches offer advantages for interactive applications that require low latency, high [...] Read more.

We introduce a novel wired communication approach for interactive wearable systems, employing a single signal wire and innovative group addressing protocol to reduce overhead. While wireless solutions dominate body sensor networks, wired approaches offer advantages for interactive applications that require low latency, high reliability, and communication with high-density nodes; yet they have been less explored in the context of wearable systems. Many commercial products use wired connections without disclosing technical details, limiting broader adoption. To address this gap, we present and test a new group addressing protocol implemented using Universal Asynchronous Receiver–Transmitter (UART) hardware, disclosing frame diagrams and node architectures. We developed a prototype interactive jacket with nine sensor/actuator nodes connected via three wires for power supply and data transmission to a wireless gateway. Mathematical analysis showed an overhead reduction of approximately 50% compared to traditional individual addressing. Our solution is the most wire-efficient among wired interactive wearable systems reviewed in the literature, using only one signal wire; other methods require at least two wires and often have overlapping topologies. Performance experimental evaluation revealed a total feedback delay of 2.27 ms and a maximum data frame rate of 435.4 Hz, comparable to the best-performing products and leaving room for twice the performance calculated theoretically. These results indicate that the proposed approach is suitable for interactive wearable systems, both for real-time applications and high-resolution data acquisition. Full article

(This article belongs to the Section Communications and Networking)

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22 pages, 11697 KiB

Open AccessArticle

Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments

by Ray Colucci and Imad Mahgoub

J. Sens. Actuator Netw. 2025, 14(1), 3; https://doi.org/10.3390/jsan14010003 - 27 Dec 2024

Abstract

The reliance on fossil fuels as a primary global energy source has significantly impacted the environment, contributing to pollution and climate change. A shift towards renewable energy sources, particularly solar power, is underway, though these sources face challenges due to their inherent intermittency. [...] Read more.

The reliance on fossil fuels as a primary global energy source has significantly impacted the environment, contributing to pollution and climate change. A shift towards renewable energy sources, particularly solar power, is underway, though these sources face challenges due to their inherent intermittency. Battery energy storage systems (BESS) play a crucial role in mitigating this intermittency, ensuring a reliable power supply when solar generation is insufficient. The objective of this paper is to accurately predict the solar irradiance for battery operation optimization in microgrids. Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. Our model outperforms previous works using the same dataset and has been validated to generalize across diverse geographical locations in Florida. This work demonstrates the potential of AI-assisted data-driven approaches to support sustainable energy management in solar-powered IoT-based microgrids. Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

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26 pages, 6009 KiB

Open AccessArticle

Enhancing Campus Environment: Real-Time Air Quality Monitoring Through IoT and Web Technologies

by Alfiandi Aulia Rahmadani, Yan Watequlis Syaifudin, Budhy Setiawan, Yohanes Yohanie Fridelin Panduman and Nobuo Funabiki

J. Sens. Actuator Netw. 2025, 14(1), 2; https://doi.org/10.3390/jsan14010002 - 25 Dec 2024

Abstract

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air [...] Read more.

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)

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20 pages, 5477 KiB

Open AccessArticle

Development of Virtual Water Flow Sensor Using Valve Performance Curve

by Taeyang Kim, Hyojun Kim, Jinhyun Lee and Younghum Cho

J. Sens. Actuator Netw. 2025, 14(1), 1; https://doi.org/10.3390/jsan14010001 - 24 Dec 2024

Abstract

This research focuses on addressing the limitations of conventional physical sensors and developing a virtual water flow rate prediction technology. With HVAC systems being increasingly adopted, research on optimizing control settings based on load variations is critical. Existing systems often operate based on [...] Read more.

This research focuses on addressing the limitations of conventional physical sensors and developing a virtual water flow rate prediction technology. With HVAC systems being increasingly adopted, research on optimizing control settings based on load variations is critical. Existing systems often operate based on peak load conditions, leading to energy overconsumption in partial load scenarios. Physical sensors used for water flow measurement face challenges such as installation difficulties in constrained spaces and increased costs in large buildings. Virtual water flow rate prediction technology offers a cost-effective solution by leveraging in situ measurement data instead of extensive physical sensors. To achieve this, a test bed with a pump, valve, and heat pump was used, controlled via a BAS. Water flow rate was measured using an ultrasonic flow meter, and differential pressure was recorded using pressure gauges. Equations were developed to replace differential pressure values with valve opening rates and pump speeds by deriving performance curves and differential pressure ratio equations. Measurement uncertainty was calculated to assess the reliability of the experimental setup. Various test numbers were created to evaluate the virtual water flow rate model under controlled conditions. The results showed that relative errors ranged from 0.32% to 10.54%, with RMSE, MBE, and CvRMSE meeting all threshold criteria. The virtual water flow rate model demonstrated strong predictive accuracy and reliability, supported by an R² value close to 1. This research confirms the effectiveness of the proposed model for reducing the dependence on physical sensors while enabling accurate water flow rate predictions in HVAC systems. Full article

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22 pages, 5963 KiB

Open AccessArticle

Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks

by Batyrbek Zholamanov, Askhat Bolatbek, Ahmet Saymbetov, Madiyar Nurgaliyev, Evan Yershov, Kymbat Kopbay, Sayat Orynbassar, Gulbakhar Dosymbetova, Ainur Kapparova, Nurzhigit Kuttybay and Nursultan Koshkarbay

J. Sens. Actuator Netw. 2024, 13(6), 89; https://doi.org/10.3390/jsan13060089 - 20 Dec 2024

Abstract

Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and [...] Read more.

Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works. Full article

(This article belongs to the Section Wireless Control Networks)

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17 pages, 2807 KiB

Open AccessArticle

Electrodermal Activity for Quantitative Assessment of Dental Anxiety

by Dindar S. Bari, Mohammed Noor S. Rammoo, Ardawan A. Youssif, Hoger M. Najman, Haval Y. Yacoob Aldosky, Christian Tronstad, Jie Hou and Ørjan G. Martinsen

J. Sens. Actuator Netw. 2024, 13(6), 88; https://doi.org/10.3390/jsan13060088 - 18 Dec 2024

Abstract

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether [...] Read more.

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether dental anxiety can be quantified objectively using the EDA parameters. EDA (skin conductance (SC), skin susceptance (SS), and skin potential (SP)) parameters and heart rate (HR) were recorded from 40 participants during two different sessions (baseline and anxiety). In addition, the Modified Dental Anxiety Scale (MDAS) scale was also used to record the level of anxiety. The physiological data from EDA and HR were compared with the subjective self-reports of anxiety provided on the MDAS to show whether higher EDA and HR readings correspond to higher scores on the MDAS. To elicit dental anxiety, participants were exposed to several film clips associated with dental treatment. EDA signals were compared between the two sessions for all clips and all EDA scores. SC and HR significantly (p < 0.05) increased during the anxiety session compared to the baseline session. The number of fluctuations per minute in the SC, SS, and SP notably increased during the anxiety session. The MDAS results revealed that the participants had dental anxiety when they were exposed to video clips of dental procedures. The study results imply that EDA parameters could be used as a useful tool to monitor dental anxiety, in particular in young children and non-verbal patients or those with intellectual disabilities, which may aid the dentist in the successful management of dental anxiety during treatment. The moment-to-moment EDA data on a patient’s anxiety levels throughout particular dental operations provides a clearer picture of anxiety variations than pre- or post-appointment surveys alone, in addition to offering unbiased tracking of dental anxiety levels over self-reports. This study seeks to encourage further research into the most effective EDA parameters for improving the management of dental anxiety. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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21 pages, 3863 KiB

Open AccessArticle

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

by Emil Cuadros Zegarra, Dennis Barrios Aranibar and Yudith Cardinale

J. Sens. Actuator Netw. 2024, 13(6), 87; https://doi.org/10.3390/jsan13060087 - 16 Dec 2024

Abstract

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The [...] Read more.

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains. Full article

(This article belongs to the Section Communications and Networking)

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17 pages, 1793 KiB

Open AccessArticle

An Automated Semantic Segmentation Methodology for Infrared Thermography Analysis of the Human Hand

by Melchior Arnal, Cyprien Bourrilhon, Vincent Beauchamps, Fabien Sauvet, Hassan Zahouani and Coralie Thieulin

J. Sens. Actuator Netw. 2024, 13(6), 86; https://doi.org/10.3390/jsan13060086 - 16 Dec 2024

Abstract

Infrared thermography is a non-invasive measurement method that can accurately describe immediate temperature changes of an object. In the case of continuous in vivo hand measurements, extracting correct thermal data requires a first step of image segmentation to identify regions of interest. This [...] Read more.

Infrared thermography is a non-invasive measurement method that can accurately describe immediate temperature changes of an object. In the case of continuous in vivo hand measurements, extracting correct thermal data requires a first step of image segmentation to identify regions of interest. This step can be difficult due to parasitic hand movements. It is therefore necessary to regularly readjust the segmented areas throughout the recording. This process is time-consuming and presents a particular obstacle to studying a large number of areas of the hand and long duration sequences. In this work, we propose an automated segmentation methodology that can automatically detect these regions on the hand. This method differs from previous literature because it uses a secondary visual camera and a combination of computer vision and machine learning feature identification. The obtained segmentation models were compared to models segmented by two human operators via Dice and Intersection-over-Union coefficients. The results obtained are very positive: we were able to decompose the images acquired via IRT with our developed algorithms, regardless of the temperature variation, and this with processing times of less than a second. Thus, this technology can be used to study the long-term thermal kinetics of the human hand by automatic feature detection, even in situations where the hand temperature experiences a significant variation. Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

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21 pages, 1939 KiB

Open AccessArticle

Multi-Class Classification of Human Activity and Gait Events Using Heterogeneous Sensors

by Tasmiyah Javed, Ali Raza, Hafiz Farhan Maqbool, Saqib Zafar, Juri Taborri and Stefano Rossi

J. Sens. Actuator Netw. 2024, 13(6), 85; https://doi.org/10.3390/jsan13060085 - 10 Dec 2024

Abstract

The control of active prostheses and orthoses requires the precise classification of instantaneous human activity and the detection of specific events within each activity. Furthermore, such classification helps physiotherapists, orthopedists, and neurologists in kinetic/kinematic analyses of patients’ gaits. To address this need, we [...] Read more.

The control of active prostheses and orthoses requires the precise classification of instantaneous human activity and the detection of specific events within each activity. Furthermore, such classification helps physiotherapists, orthopedists, and neurologists in kinetic/kinematic analyses of patients’ gaits. To address this need, we propose an innovative deep neural network (DNN)-based approach with a two-step hyperparameter optimization scheme for classifying human activity and gait events, specific for different motor activities, by using the ENABL3S dataset. The proposed architecture sets the baseline accuracy to 93% with a single hidden layer and offers further improvement by adding more layers; however, the corresponding number of input neurons remains a crucial hyperparameter. Our two-step hyperparameter-tuning strategy is employed which first searches for an appropriate number of hidden layers and then carefully modulates the number of neurons within these layers using 10-fold cross-validation. This multi-class classifier significantly outperforms prior machine learning algorithms for both activity and gait event recognition. Notably, our proposed scheme achieves impressive accuracy rates of 98.1% and 99.96% for human activity and gait events per activity, respectively, potentially leading to significant advancements in prosthetic/orthotic controls, patient care, and rehabilitation programs’ definition. Full article

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12 pages, 3565 KiB

Open AccessArticle

Interdigitated Gear-Shaped Screen-Printed Electrode Using G-PANI Ink for Sensitive Electrochemical Detection of Dopamine

by Pritu Parna Sarkar, Ridma Tabassum, Ahmed Hasnain Jalal, Ali Ashraf and Nazmul Islam

J. Sens. Actuator Netw. 2024, 13(6), 84; https://doi.org/10.3390/jsan13060084 - 6 Dec 2024

Abstract

In this research, a novel interdigitated gear-shaped, graphene-based electrochemical biosensor was developed for the detection of dopamine (DA). The sensor’s innovative design improves the active surface area by 94.52% and 57% compared to commercially available Metrohm DropSens 110 screen-printed sensors and printed circular [...] Read more.

In this research, a novel interdigitated gear-shaped, graphene-based electrochemical biosensor was developed for the detection of dopamine (DA). The sensor’s innovative design improves the active surface area by 94.52% and 57% compared to commercially available Metrohm DropSens 110 screen-printed sensors and printed circular sensors, respectively. The screen-printed electrode was fabricated using laser processing and modified with graphene polyaniline conductive ink (G-PANI) to enhance its electrochemical properties. Fourier Transform Infrared (FTIR) Spectroscopy and X-ray diffraction (XRD) were employed to characterize the physiochemical properties of the sensor. Dopamine, a neurotransmitter crucial for several body functions, was detected within a linear range of 0.1–100 µM, with a Limit of Detection (LOD) of 0.043 µM (coefficient of determination, R² = 0.98) in phosphate-buffer saline (PBS) with ferri/ferrocyanide as the redox probe. The performance of the sensor was evaluated using cyclic voltammetry (CV) and Chronoamperometry, demonstrating high sensitivity and selectivity. The interdigitated gear-shaped design exhibited excellent repeatability, with a relative standard deviation (RSD) of 1.2% (n = 4) and reproducibility, with an RSD of 2.3% (n = 4). In addition to detecting dopamine in human serum, the sensor effectively distinguished dopamine in a ternary mixture containing uric acid (UA) and ascorbic acid (AA). Overall, this novel sensor design offers a reliable, disposable, and cost-effective solution for dopamine detection, with potential applications in medical diagnostics and neurological research. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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Graphical abstract

20 pages, 6859 KiB

Open AccessArticle

Intelligent IoT Platform for Agroecology: Testbed

by Naila Bouchemal, Nicola Chollet and Amar Ramdane-Cherif

J. Sens. Actuator Netw. 2024, 13(6), 83; https://doi.org/10.3390/jsan13060083 - 2 Dec 2024

Abstract

Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devices on farms essential. The development of smart [...] Read more.

Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devices on farms essential. The development of smart sensors that utilize artificial intelligence (AI) algorithms for advanced edge computations only intensifies this need. Moreover, once data are collected, farmers frequently find it challenging to apply them effectively, especially in alignment with agroecological principles. In this context, this paper introduces an energy-efficient platform for embedded AI sensors that leverages the LoRaWAN network, along with a knowledge-based system to aid farmers in decision-making rooted in sensor data and agroecological practices. This paper focuses on the deployment of an end-to-end IoT platform that integrates a wireless sensor network (WSN), embedded AI, and a knowledge base. Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

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17 pages, 6775 KiB

Open AccessArticle

Optimized Data Transmission and Signal Processing for Telepresence Suits in Multiverse Interactions

by Artem Volkov, Ammar Muthanna, Alexander Paramonov, Andrey Koucheryavy and Ibrahim A. Elgendy

J. Sens. Actuator Netw. 2024, 13(6), 82; https://doi.org/10.3390/jsan13060082 - 29 Nov 2024

Abstract

With the rapid development of the metaverse, designing effective interfaces in virtual and augmented environments presents significant challenges. Additionally, keeping real-time sensory data flowing from users to their virtual avatars in a seamless and accurate manner is one of the biggest challenges in [...] Read more.

With the rapid development of the metaverse, designing effective interfaces in virtual and augmented environments presents significant challenges. Additionally, keeping real-time sensory data flowing from users to their virtual avatars in a seamless and accurate manner is one of the biggest challenges in this domain. To this end, this article investigates a telepresence suit as an interface for interaction within the metaverse and its virtual avatars, aiming to address the complexities of signal generation, conversion, and transmission in real-time telepresence systems. We model a telepresence suit framework that systematically generates state data and transmits it to end-points, which can be either robotic avatars or virtual representations within a metaverse environment. Through a hand movement study, we successfully minimized the volume of transmitted information, reducing traffic by over 50%, which directly decreased channel load and packet delivery delay. For instance, as channel load decreases from 0.8 to 0.4, packet delivery delay is reduced by approximately half. This optimization not only enhances system responsiveness but also improves accuracy, particularly by reducing delays and errors in high-priority signal paths, enabling more precise and reliable telepresence interactions in metaverse settings. Full article

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35 pages, 2580 KiB

Open AccessReview

A Comprehensive Review of UAV-UGV Collaboration: Advancements and Challenges

by Isuru Munasinghe, Asanka Perera and Ravinesh C. Deo

J. Sens. Actuator Netw. 2024, 13(6), 81; https://doi.org/10.3390/jsan13060081 - 28 Nov 2024

Abstract

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its [...] Read more.

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration. Full article

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18 pages, 16693 KiB

Open AccessArticle

Region Segmentation of Images Based on a Raster-Scan Paradigm

by Luka Lukač, Andrej Nerat, Damjan Strnad, Štefan Horvat and Borut Žalik

J. Sens. Actuator Netw. 2024, 13(6), 80; https://doi.org/10.3390/jsan13060080 - 28 Nov 2024

Abstract

This paper introduces a new method for the region segmentation of images. The approach is based on the raster-scan paradigm and builds the segments incrementally. The pixels are processed in the raster-scan order, while the construction of the segments is based on a [...] Read more.

This paper introduces a new method for the region segmentation of images. The approach is based on the raster-scan paradigm and builds the segments incrementally. The pixels are processed in the raster-scan order, while the construction of the segments is based on a distance metric in regard to the already segmented pixels in the neighbourhood. The segmentation procedure operates in linear time according to the total number of pixels. The proposed method, named the RSM (raster-scan segmentation method), was tested on selected images from the popular benchmark datasets MS COCO and DIV2K. The experimental results indicate that our method successfully extracts regions with similar pixel values. Furthermore, a comparison with two of the well-known segmentation methods—Watershed and DBSCAN—demonstrates that the proposed approach is superior in regard to efficiency while yielding visually similar results. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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16 pages, 5102 KiB

Open AccessArticle

Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network

by Tahereh Shah Mansouri, Gennady Lubarsky, Dewar Finlay and James McLaughlin

J. Sens. Actuator Netw. 2024, 13(6), 79; https://doi.org/10.3390/jsan13060079 - 23 Nov 2024

Abstract

Within the domain of Structural Health Monitoring (SHM), conventional approaches generally are complicated, destructive, and time-consuming. It also necessitates an extensive array of sensors to effectively evaluate and monitor the structural integrity. In this research work, we present a novel, non-destructive SHM framework [...] Read more.

Within the domain of Structural Health Monitoring (SHM), conventional approaches generally are complicated, destructive, and time-consuming. It also necessitates an extensive array of sensors to effectively evaluate and monitor the structural integrity. In this research work, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localisation of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth Low Energy (BLE) communication. The framework is validated through empirical data collected from 3D carbon fibre-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. The methodology integrates an analytical examination of the Shewhart chart, Grubbs’ test (GT), and hierarchical clustering (HC) algorithm, tailored towards the metrics of fracture measurement and classification. Our novel ML framework allows one to replace exhausting laboratory procedures with a modern and quick mechanism for the material, with unprecedented properties that could provide potential applications in the composites industry. Full article

(This article belongs to the Topic Machine Learning in Communication Systems and Networks, 2nd Edition)

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29 pages, 2096 KiB

Open AccessArticle

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

by Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano and Kateryna Kuznetsova

J. Sens. Actuator Netw. 2024, 13(6), 78; https://doi.org/10.3390/jsan13060078 - 14 Nov 2024

Abstract

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element [...] Read more.

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems. Full article

(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)

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23 pages, 8520 KiB

Open AccessArticle

Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions

by Zeyad T. Aklah, Hussein T. Hassan, Amean Al-Safi and Khalid Aljabery

J. Sens. Actuator Netw. 2024, 13(6), 77; https://doi.org/10.3390/jsan13060077 - 13 Nov 2024

Abstract

The field of physically interactive electronic games is rapidly evolving, driven by the fact that it combines the benefits of physical activities and the attractiveness of electronic games, as well as advancements in sensor technologies. In this paper, a new game was introduced, [...] Read more.

The field of physically interactive electronic games is rapidly evolving, driven by the fact that it combines the benefits of physical activities and the attractiveness of electronic games, as well as advancements in sensor technologies. In this paper, a new game was introduced, which is a special version of Bubble Soccer, which we named Q-eBall. It creates a dynamic and engaging experience by combining simulation and physical interactions. Q-eBall is equipped with a fall detection system, which uses an embedded electronic circuit integrated with an accelerometer, a gyroscopic, and a pressure sensor. An evaluation of the performance of the fall detection system in Q-eBall is presented, exploring its technical details and showing its performance. The system captures the data of players’ movement in real-time and transmits it to the game controller, which can accurately identify when a player falls. The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. The results showed that the inclusion of pressure sensor data significantly improved the performance of all models, with the SVM and LSTM models reaching 100% on all metrics (accuracy, precision, recall, and F1-score). To validate the offline results, a real-time experiment was performed using the pre-trained SVM model, which successfully recorded all 150 falls without any false positives or false negatives. These findings prove the reliability and effectiveness of the Q-eBall fall detection system in real time. Full article

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28 pages, 57781 KiB

Open AccessArticle

Edge Computing for Smart-City Human Habitat: A Pandemic-Resilient, AI-Powered Framework

by Atlanta Choudhury, Kandarpa Kumar Sarma, Debashis Dev Misra, Koushik Guha and Jacopo Iannacci

J. Sens. Actuator Netw. 2024, 13(6), 76; https://doi.org/10.3390/jsan13060076 - 6 Nov 2024

Abstract

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication [...] Read more.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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14 pages, 2976 KiB

Open AccessArticle

Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN

by Minji Kang, Sung Kyu Jang, Jihun Kim, Seongho Kim, Changmin Kim, Hyo-Chang Lee, Wooseok Kang, Min Sup Choi, Hyeongkeun Kim and Hyeong-U Kim

J. Sens. Actuator Netw. 2024, 13(6), 75; https://doi.org/10.3390/jsan13060075 - 4 Nov 2024

Abstract

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF₄ [...] Read more.

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF₄-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments. Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

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Journal of Sensor and Actuator Networks

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