Journal of Sensor and Actuator Networks

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 Jalal Hasnain, Ali Ashraf and Nazmul Islam

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

Viewed by 311

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)

► Show Figures

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

Viewed by 266

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)

► Show Figures

Figure 1

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

Viewed by 325

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

► Show Figures

Figure 1

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

Viewed by 938

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

► Show Figures

Figure 1

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

Viewed by 345

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)

► Show Figures

Figure 1

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

Viewed by 762

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)

► Show Figures

Figure 1

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

Viewed by 578

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)

► Show Figures

Figure 1

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

Viewed by 526

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

► Show Figures

Figure 1

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

Viewed by 620

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)

► Show Figures

Figure 1

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

Viewed by 794

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)

► Show Figures

Figure 1

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

Viewed by 1014

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))

► Show Figures

Figure 1

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

Viewed by 1085

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)

► Show Figures

Figure 1

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

Viewed by 910

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)

► Show Figures

Figure 1

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

Viewed by 1106

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)

► Show Figures

Figure 1

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

Viewed by 2032

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)

► Show Figures

Figure 1

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

Viewed by 659

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

► Show Figures

Figure 1

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

Viewed by 596

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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

J. Sens. Actuator Netw., Volume 13, Issue 6 (December 2024) – 17 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI