Journal of Sensor and Actuator Networks

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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29 pages, 17952 KiB

Open AccessArticle

Housekeeping System for Suborbital Vehicles: VIRIATO Mock-Up Vehicle Integration and Testing

by Geraldo Rodrigues, Beltran Arribas, Rui Melicio, Paulo Gordo, Duarte Valério, João Casaleiro and André Silva

J. Sens. Actuator Netw. 2024, 13(6), 74; https://doi.org/10.3390/jsan13060074 - 4 Nov 2024

Abstract

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal [...] Read more.

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal simulations are performed regarding the designed system and a physical prototype is manufactured, assembled and programmed. Functional and field test results resorting to unmanned aerial vehicles, as well as the system’s integration within VIRIATO project’s mock-up vehicle, are presented. These tests demonstrate the viability of this system as an independent data acquisition system, and simulation results show that commercial off-the-shelf components have the capability of surviving expected launch environments. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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29 pages, 4937 KiB

Open AccessArticle

Whale Optimization Algorithm-Enhanced Long Short-Term Memory Classifier with Novel Wrapped Feature Selection for Intrusion Detection

by Haider AL-Husseini, Mohammad Mehdi Hosseini, Ahmad Yousofi and Murtadha A. Alazzawi

J. Sens. Actuator Netw. 2024, 13(6), 73; https://doi.org/10.3390/jsan13060073 - 2 Nov 2024

Abstract

Intrusion detection in network systems is a critical challenge due to the ever-increasing volume and complexity of cyber-attacks. Traditional methods often struggle with high-dimensional data and the need for real-time detection. This paper proposes a comprehensive intrusion detection method utilizing a novel wrapped [...] Read more.

Intrusion detection in network systems is a critical challenge due to the ever-increasing volume and complexity of cyber-attacks. Traditional methods often struggle with high-dimensional data and the need for real-time detection. This paper proposes a comprehensive intrusion detection method utilizing a novel wrapped feature selection approach combined with a long short-term memory classifier optimized with the whale optimization algorithm to address these challenges effectively. The proposed method introduces a novel feature selection technique using a multi-layer perceptron and a hybrid genetic algorithm-particle swarm optimization algorithm to select salient features from the input dataset, significantly reducing dimensionality while retaining critical information. The selected features are then used to train a long short-term memory network, optimized by the whale optimization algorithm to enhance its classification performance. The effectiveness of the proposed method is demonstrated through extensive simulations of intrusion detection tasks. The feature selection approach effectively reduced the feature set from 78 to 68 features, maintaining diversity and relevance. The proposed method achieved a remarkable accuracy of 99.62% in DDoS attack detection and 99.40% in FTP-Patator/SSH-Patator attack detection using the CICIDS-2017 dataset and an anomaly attack detection accuracy of 99.6% using the NSL-KDD dataset. These results highlight the potential of the proposed method in achieving high detection accuracy with reduced computational complexity, making it a viable solution for real-time intrusion detection. Full article

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

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21 pages, 2302 KiB

Open AccessArticle

Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

by Irrai Anbu Jayaraj, Bharanidharan Shanmugam, Sami Azam and Suresh Thennadil

J. Sens. Actuator Netw. 2024, 13(6), 72; https://doi.org/10.3390/jsan13060072 - 1 Nov 2024

Abstract

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based [...] Read more.

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively. Full article

(This article belongs to the Section Wireless Control Networks)

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20 pages, 3567 KiB

Open AccessArticle

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

by Ahmad Hani El Fawal, Ali Mansour, Hussein El Ghor, Nuha A. Ismail and Sally Shamaa

J. Sens. Actuator Netw. 2024, 13(6), 71; https://doi.org/10.3390/jsan13060071 - 30 Oct 2024

Abstract

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its [...] Read more.

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated. Full article

(This article belongs to the Section Communications and Networking)

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16 pages, 5482 KiB

Open AccessArticle

Holistic Sensor-Based Approach for Assessing Community Mobility and Participation of Manual Wheelchair Users in the Real World

by Grace McClatchey, Maja Goršič, Madisyn R. Adelman, Wesley C. Kephart and Jacob R. Rammer

J. Sens. Actuator Netw. 2024, 13(6), 70; https://doi.org/10.3390/jsan13060070 - 24 Oct 2024

Abstract

Given the unique challenges faced by manual wheelchair users, improving methods to accurately measure and enhance their participation in community life is critical. This study explores a comprehensive method to evaluate the real-world community mobility and participation of manual wheelchair users by combining [...] Read more.

Given the unique challenges faced by manual wheelchair users, improving methods to accurately measure and enhance their participation in community life is critical. This study explores a comprehensive method to evaluate the real-world community mobility and participation of manual wheelchair users by combining GPS mobility tracking, heart rate, and activity journals. Collecting qualitative and quantitative measures such as the life space assessment, wheelchair user confidence scale, and physical performance tests alongside GPS mobility tracking from ten manual wheelchair users provided insight into the complex relationship between physical, psychological, and social factors that can impact their daily community mobility and participation. This study found significant, strong correlations between the recorded journal time outside of the home and the GPS mean daily heart rate (r = −0.750, p = 0.032) as well as between the upper limb strength assessments with cardiovascular assessments, physiological confidence, and GPS participation indicators (0.732 < r < 0.884, 0.002 < p < 0.039). This method of manual wheelchair user assessment reveals the complex relationships between different aspects of mobility and participation. It provides a means of enhancing the ability of rehabilitation specialists to focus rehabilitation programs toward the areas that will help manual wheelchair users improve their quality of life. Full article

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

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28 pages, 4362 KiB

Open AccessArticle

Enhancing Signal-to-Noise Ratio in Vehicle-to-Vehicle Visible Light Communication Systems Through Diverse LED Array Transmitter Geometries

by Ahmet Deniz, Melike Oztopal and Heba Yuksel

J. Sens. Actuator Netw. 2024, 13(6), 69; https://doi.org/10.3390/jsan13060069 - 23 Oct 2024

Abstract

In this paper, a novel method is introduced to enhance the performance of vehicle-to-vehicle (V2V) visible light communication (VLC) by employing different transmitter (Tx) light-emitting diode (LED) array arrangements with different LED orientations. Improving the signal-to-noise ratio (SNR) is crucial for V2V VLC [...] Read more.

In this paper, a novel method is introduced to enhance the performance of vehicle-to-vehicle (V2V) visible light communication (VLC) by employing different transmitter (Tx) light-emitting diode (LED) array arrangements with different LED orientations. Improving the signal-to-noise ratio (SNR) is crucial for V2V VLC systems to provide long communication ranges. For this purpose, six transmitter configurations are proposed: single-LED transmitters, as well as 3 × 3 square-, single hexagonal-, octagonal-, 5 × 5 square-, and honeycomb hexagonal-shaped LED arrays. Indoor VLC studies using LED arrays offer a uniform SNR, while outdoor studies focus on optimizing the receiver side to enhance system performance. This paper optimizes system performance by increasing the SNR and communication range of V2V VLC systems by changing the geometry of the Tx LED array and LED orientations. A V2V VLC system using on–off keying (OOK) is modeled in MATLAB, and the SNR and bit error rate (BER) are simulated for different Tx configurations. Our results show that the honeycomb hexagonal transmitter design provides a 19% improvement in system performance with a spacing of 1 cm, and maintains a 16% improvement when the array size is reduced by a factor of 100, making it smaller than one of the smallest industrial headlight modules. Full article

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19 pages, 9389 KiB

Open AccessArticle

Indicators for Assessing the Combustion Intensity of Coal Particles Using a Single UV Sensor

by Dariusz Choiński, Krzysztof Stebel, Andrzej Malcher, Paweł Bocian, Beata Glot, Witold Ilewicz, Piotr Skupin, Patryk Grelewicz and J. Angela Jennifa Sujana

J. Sens. Actuator Netw. 2024, 13(6), 68; https://doi.org/10.3390/jsan13060068 - 22 Oct 2024

Abstract

This paper deals with the evaluation of the combustion intensity of coal particles on the basis of measurement data (UV radiation) from a scanning point photodiode. UV radiation is measured using a custom UV scanner at different distances from the burner in the [...] Read more.

This paper deals with the evaluation of the combustion intensity of coal particles on the basis of measurement data (UV radiation) from a scanning point photodiode. UV radiation is measured using a custom UV scanner at different distances from the burner in the vertical combustion chamber. The designed scanner uses a sensitive silicon carbide (SiC) photodiode, and its dynamical properties are investigated in the present work. Subsequently, experiments are performed for coal particles at different combustion temperatures and at different measuring locations of the scanner. The measurement data are processed in the frequency domain using the proposed algorithm, and two indicators for evaluating the combustion intensity are proposed. The obtained results show that the proposed indicators provide unequivocal information about the combustion intensity as a function of the combustion temperature. Full article

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

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19 pages, 985 KiB

Open AccessArticle

On the Energy Behaviors of the Bellman–Ford and Dijkstra Algorithms: A Detailed Empirical Study

by Othman Alamoudi and Muhammad Al-Hashimi

J. Sens. Actuator Netw. 2024, 13(5), 67; https://doi.org/10.3390/jsan13050067 - 12 Oct 2024

Abstract

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware [...] Read more.

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind. Full article

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22 pages, 4093 KiB

Open AccessArticle

Helicopters Turboshaft Engines Neural Network Modeling under Sensor Failure

by Serhii Vladov, Anatoliy Sachenko, Valerii Sokurenko, Oleksandr Muzychuk and Victoria Vysotska

J. Sens. Actuator Netw. 2024, 13(5), 66; https://doi.org/10.3390/jsan13050066 - 10 Oct 2024

Abstract

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such [...] Read more.

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 °C and 24 °C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model’s accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios. Full article

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16 pages, 4527 KiB

Open AccessArticle

High-Transparency Linear Actuator Using an Electromagnetic Brake for Damping Modulation in Physical Human–Robot Interaction

by Zahid Ullah, Thachapan Sermsrisuwan, Khemwutta Pornpipatsakul, Ronnapee Chaichaowarat and Witaya Wannasuphoprasit

J. Sens. Actuator Netw. 2024, 13(5), 65; https://doi.org/10.3390/jsan13050065 - 10 Oct 2024

Abstract

Enhancing the transparency of high-transmission-ratio linear actuators is crucial for improving the safety and capability of high-force robotic systems having physical contact with humans in unstructured environments. However, realizing such enhancement is challenging. A proposed solution for active body weight support systems involves [...] Read more.

Enhancing the transparency of high-transmission-ratio linear actuators is crucial for improving the safety and capability of high-force robotic systems having physical contact with humans in unstructured environments. However, realizing such enhancement is challenging. A proposed solution for active body weight support systems involves employing a macro–mini linear actuator incorporating an electrorheological-fluid brake to connect a high-force unit with an agile, highly back-drivable unit. This paper introduces the use of an electromagnetic (EM) brake with reduced rotor inertia to address this challenge. The increased torque capacity of the EM brake enables integration with a low-gear-ratio linear transmission. The agile translation of the endpoint is propelled by a low-inertia motor (referred to as the “mini”) via a pulley-belt mechanism to achieve high transparency. The rotor of the EM brake is linked to the pulley. Damping modulation under high driving force is achieved through the adjustment of the brake torque relative to the rotational speed of the pulley. When the brake is engaged, it prevents any relative motion between the endpoint and the moving carrier. The endpoint is fully controlled by the ball screw of the high-force unit, referred to as the “macro”. A scaled prototype was constructed to experimentally characterize the damping force generated by the mini motor and the EM brake. The macro–mini linear actuator, equipped with an intrinsic failsafe feature, can be utilized for active body weight support systems that demand high antigravity force. Full article

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

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20 pages, 3912 KiB

Open AccessArticle

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

by Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin and Jang-Wook Hur

J. Sens. Actuator Netw. 2024, 13(5), 64; https://doi.org/10.3390/jsan13050064 - 9 Oct 2024

Abstract

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would [...] Read more.

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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17 pages, 4996 KiB

Open AccessArticle

Safeguarding Personal Identifiable Information (PII) after Smartphone Pairing with a Connected Vehicle

by Jason Carlton and Hafiz Malik

J. Sens. Actuator Netw. 2024, 13(5), 63; https://doi.org/10.3390/jsan13050063 - 6 Oct 2024

Abstract

The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system [...] Read more.

The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system (MAS)-based hierarchical architectures and privacy-preserving strategies for mixed-autonomy platoon control, underscore the increasing complexity of privacy management within these environments. Rental cars with infotainment systems pose substantial challenges, as renters often fail to delete their data, leaving it accessible to subsequent renters. This study investigates the risks associated with PII in connected vehicles and emphasizes the necessity of automated solutions to ensure data privacy. We introduce the Vehicle Inactive Profile Remover (VIPR), an innovative automated solution designed to identify and delete PII left on infotainment systems. The efficacy of VIPR is evaluated through surveys, hands-on experiments with rental vehicles, and a controlled laboratory environment. VIPR achieved a 99.5% success rate in removing user profiles, with an average deletion time of 4.8 s or less, demonstrating its effectiveness in mitigating privacy risks. This solution highlights VIPR as a critical tool for enhancing privacy in connected vehicle environments, promoting a safer, more responsible use of connected vehicle technology in society. Full article

(This article belongs to the Special Issue Feature Papers in the Section of Network Security and Privacy)

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30 pages, 3145 KiB

Open AccessArticle

Create a Realistic IoT Dataset Using Conditional Generative Adversarial Network

by Miada Almasre and Alanoud Subahi

J. Sens. Actuator Netw. 2024, 13(5), 62; https://doi.org/10.3390/jsan13050062 - 3 Oct 2024

Abstract

The increased use of Internet of Things (IoT) devices has led to greater threats to privacy and security. This has created a need for more effective cybersecurity applications. However, the effectiveness of these systems is often limited by the lack of comprehensive and [...] Read more.

The increased use of Internet of Things (IoT) devices has led to greater threats to privacy and security. This has created a need for more effective cybersecurity applications. However, the effectiveness of these systems is often limited by the lack of comprehensive and balanced datasets. This research contributes to IoT security by tackling the challenges in dataset generation and providing a valuable resource for IoT security research. Our method involves creating a testbed, building the ‘Joint Dataset’, and developing an innovative tool. The tool consists of two modules: an Exploratory Data Analysis (EDA) module, and a Generator module. The Generator module uses a Conditional Generative Adversarial Network (CGAN) to address data imbalance and generate high-quality synthetic data that accurately represent real-world network traffic. To showcase the effectiveness of the tool, the proportion of imbalance reduction in the generated dataset was computed and benchmarked to the BOT-IOT dataset. The results demonstrated the robustness of synthetic data generation in creating balanced datasets. 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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17 pages, 491 KiB

Open AccessArticle

Dynamic Event-Triggered Control for Sensor–Controller–Actuator Networked Control Systems

by Mahmoud Abdelrahim and Dhafer Almakhles

J. Sens. Actuator Netw. 2024, 13(5), 61; https://doi.org/10.3390/jsan13050061 - 1 Oct 2024

Abstract

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering [...] Read more.

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering rules are constructed to generate the transmission instants of the submitted signals. The proposed approach is dynamic in the sense that the triggering rules involve internal dynamical variables to allow for further reduction in the communication load. Moreover, the inter-transmission times for both sides of the channel are lower bound by enforced dwell times to prevent the occurrence of Zeno phenomena. The problem is challenging due to mutual interactions between the sampling errors of the plant output and the control input, which requires careful handling to ensure closed-loop stability. The triggering mechanisms are designed by emulation as we first ignore the effect of the network and stabilize the plant in continuous-time. Then, the communication constraints are taken into account to derive the triggering conditions such that the stability of the networked control system is preserved. The required conditions are formulated in terms of a linear matrix inequality. The effectiveness of the technique is demonstrated by numerical simulations. Full article

(This article belongs to the Section Communications and Networking)

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30 pages, 4047 KiB

Open AccessArticle

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

by Dong-Yun Kim, Akeem Bayo Kareem, Daryl Domingo, Baek-Cheon Shin and Jang-Wook Hur

J. Sens. Actuator Netw. 2024, 13(5), 60; https://doi.org/10.3390/jsan13050060 - 25 Sep 2024

Abstract

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and [...] Read more.

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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20 pages, 3288 KiB

Open AccessArticle

Task Scheduling Algorithm for Power Minimization in Low-Cost Disaster Monitoring System: A Heuristic Approach

by Chanankorn Jandaeng , Jongsuk Kongsen , Peeravit Koad, May Thu and Sirirat Somchuea

J. Sens. Actuator Netw. 2024, 13(5), 59; https://doi.org/10.3390/jsan13050059 - 24 Sep 2024

Abstract

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm [...] Read more.

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments. Full article

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27 pages, 22292 KiB

Open AccessArticle

RFSoC Softwarisation of a 2.45 GHz Doppler Microwave Radar Motion Sensor

by Peter Hobden, Edmond Nurellari and Saket Srivastava

J. Sens. Actuator Netw. 2024, 13(5), 58; https://doi.org/10.3390/jsan13050058 - 23 Sep 2024

Abstract

Microwave Doppler sensors are used extensively in motion detection as they are energy-efficient, small-size and relatively low-cost sensors. Common applications of microwave Doppler sensors are for detecting intrusion behind a car roof liner inside an automotive vehicle and to detect moving objects. These [...] Read more.

Microwave Doppler sensors are used extensively in motion detection as they are energy-efficient, small-size and relatively low-cost sensors. Common applications of microwave Doppler sensors are for detecting intrusion behind a car roof liner inside an automotive vehicle and to detect moving objects. These applications require a millisecond response from the target for effective detection. A Doppler microwave sensor is ideally suited to the task, as we are only interested in movement of a large water-based mass (i.e., a person) (FMCW Radar also detect static objects). Although microwave components at

2.45

G

Hz

are now relatively cheap due to mass production of other Industrial Scientific and Medical application (ISM) devices, they do require tuning for temperature compensation, dielectric, and manufacturing variability. A digital solution would be ideal, as chip solutions are known to be more repeatable, but Application-Specific Integrated Circuits (ASICs) are expensive to initially prototype. This paper presents the first completely digital Doppler motion sensor solution at

2.45

G

Hz

, implemented on the new RFSoC from Xilinx without the need to up/downconvert the frequency externally. Our proposed system uses a completely digital approach bringing the benefits of product repeatability, better overtemperature performance and softwarisation, without compromising any performance metric associated with a comparable analogue motion sensor. The RFSoC shows to give superior distance versus false detection, as the Signal-to-Noise Ratio (SNR) is better than a typical analogue system. This is mainly due to the high gain amplification requirement of an analogue system, making it susceptible to electrical noise appearing in the intermediate-frequency (IF) baseband. The proposed RFSoC-based Doppler sensor shows how digital technology can replace traditional analogue radio frequency (RF). A case study is presented showing how we can use a novel method of using multiple Doppler channels to provide range discrimination, which can be performed in both analogue and in a digital implementation (RFSoC). Full article

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20 pages, 566 KiB

Open AccessArticle

Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

by Enrico Zero, Mohamed Sallak and Roberto Sacile

J. Sens. Actuator Netw. 2024, 13(5), 57; https://doi.org/10.3390/jsan13050057 - 19 Sep 2024

Abstract

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to [...] Read more.

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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Journal of Sensor and Actuator Networks

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