Journal of Sensor and Actuator Networks

25 pages, 5171 KiB

Open AccessArticle

A Novel Method for Simulation Model Generation of Production Systems Using PLC Sensor and Actuator State Monitoring

by Norbert Szántó, Szabolcs Fischer and Gergő Dávid Monek

J. Sens. Actuator Netw. 2025, 14(3), 55; https://doi.org/10.3390/jsan14030055 (registering DOI) - 21 May 2025

This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and [...] Read more.

This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and digital experimentation in Industry 4.0, it remains a resource-intensive and time-consuming endeavor—especially for small and medium-sized enterprises. The approach introduced in this research eliminates the need for prior system knowledge, physical inspection, or modification of existing control logic, thereby reducing human involvement and streamlining the model development process. The results confirm that essential structural and operational parameters—such as process routing, operation durations, and resource allocation logic—can be accurately inferred from runtime data. The proposed approach addresses the challenge of simulation model obsolescence caused by evolving automation and shifting production requirements. It offers a practical and scalable solution for maintaining up-to-date digital representations of manufacturing systems and provides a foundation for further extensions into Digital Shadow and Digital Twin applications. Full article

(This article belongs to the Special Issue AI and IoT Convergence for Sustainable Smart Manufacturing)

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30 pages, 6468 KiB

Open AccessArticle

EWOD Sensor for Rapid Quantification of Marine Dispersants in Oil Spill Management

by Oriol Caro-Pérez, María Blanca Roncero and Jasmina Casals-Terré

J. Sens. Actuator Netw. 2025, 14(3), 54; https://doi.org/10.3390/jsan14030054 - 21 May 2025

Abstract

In this study, we introduce a novel Electrowetting-on-Dielectric (EWOD) sensor designed to quantify marine dispersants at the spill point. The sensor quantifies changes in the surface tension of liquid droplets at varying dispersant concentrations through the deformation response of the droplet under applied [...] Read more.

In this study, we introduce a novel Electrowetting-on-Dielectric (EWOD) sensor designed to quantify marine dispersants at the spill point. The sensor quantifies changes in the surface tension of liquid droplets at varying dispersant concentrations through the deformation response of the droplet under applied voltage. Analyzed responses include droplet length and contact angle (CA) on the device surface upon sensor activation. This sensor offers significant advantages over existing chemical methods, which are costly and complex. Moreover, compared to conventional methods based on the same principle, it demonstrates enhanced sensitivity at low concentrations. Additionally, the sensor’s portability enables instantaneous and in situ measurements of marine dispersant concentrations, thus providing a crucial tool for effective oil spill response by facilitating on-site decision-making and offering higher temporal resolution for studies on the marine dispersant’s environmental impact. The device’s potential extends beyond marine dispersants to detecting various contaminants affecting surface tension. Its adaptability underscores the EWOD device’s role as a versatile tool for environmental monitoring and on-site analysis, addressing the urgent need for efficient and sustainable solutions in environmental management. Full article

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26 pages, 2634 KiB

Open AccessArticle

Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications

by Doan Perdana, Pascal Lorenz and Bagus Aditya

J. Sens. Actuator Netw. 2025, 14(3), 53; https://doi.org/10.3390/jsan14030053 - 19 May 2025

Abstract

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load [...] Read more.

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

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34 pages, 792 KiB

Open AccessArticle

Data-Driven Approaches for Efficient Vehicle Driving Analysis: A Survey

by Iryna I. Husyeva, Ismael Navas-Delgado and José García-Nieto

J. Sens. Actuator Netw. 2025, 14(3), 52; https://doi.org/10.3390/jsan14030052 - 19 May 2025

Viewed by 66

Abstract

Efficient vehicle driving generally intends to reduce fuel consumption, emissions of harmful substances, and accident rates based on energy-efficient driving patterns as a set of parameters defining optimal vehicle and route characteristics, together with specific ways of driving a vehicle that the particular [...] Read more.

Efficient vehicle driving generally intends to reduce fuel consumption, emissions of harmful substances, and accident rates based on energy-efficient driving patterns as a set of parameters defining optimal vehicle and route characteristics, together with specific ways of driving a vehicle that the particular driver applies. To gain environmental friendliness in driving, two main approaches can be outlined: optimal route planning and driver training based on the principles of ecological driving. The latter can be supported by using software for real-time, efficient vehicle driving recommendations. In order to develop the principles of ecological driving as well as generate relevant real-time recommendations, it is necessary to identify the specific parameters required to analyze driver behavior and vehicle performance, determine the corresponding energy consumption, and understand the influence of route and environmental conditions on overall efficient vehicle driving. These tasks require a large amount of data, often obtained from heterogeneous sources, which, when publicly available, are complex for consolidation, transmission, and processing, not to mention the complexity of the data model itself. This study provides a thorough review of the current data sources and techniques for efficient vehicle driving analysis, focusing on the availability and relevance of dataset sources and repositories. The categorization of parameters and data processing techniques enabling efficient vehicle driving analysis is carried out according to efficiency types such as driver’s efficiency, resource consumption efficiency, and route planning efficiency. For each type of efficiency, we provide a list of contextual groups and features, identifying the dataset containing the necessary feature, making it possible not only to determine the parameters defining, for example, driver efficiency, but also locate the corresponding dataset serving as a stepping stone for researchers and practitioners to join the community investigating efficient vehicle driving analysis. We also discuss future trends and perspectives, identifying alternative data sources for efficient vehicle driving analysis, and focus on data collection issues revealed by the practical use case of collecting data from mobile phone sensors. Full article

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

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20 pages, 7183 KiB

Open AccessArticle

A Two-Stage Strategy Integrating Gaussian Processes and TD3 for Leader–Follower Coordination in Multi-Agent Systems

by Xicheng Zhang, Bingchun Jiang, Fuqin Deng and Min Zhao

J. Sens. Actuator Netw. 2025, 14(3), 51; https://doi.org/10.3390/jsan14030051 - 14 May 2025

Viewed by 233

Abstract

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming [...] Read more.

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming to enhance adaptability and multi-objective optimization. Initially, GPs are utilized to model the uncertain dynamics of agents based on sensor data, providing a stable and noiseless training virtual environment for the first phase of TD3 strategy network training. Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. Additionally, the approach employs an enhanced dual-layer reward mechanism tailored to different stages of learning, ensuring robustness and improved convergence speed. Experimental results using a differential drive robot simulation demonstrate the superiority of this method over traditional controllers. The integration of the TD3 compensation network further improves the cooperative reward among agents. Full article

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29 pages, 2615 KiB

Open AccessReview

A Review: Applications of MOX Sensors from Air Quality Monitoring to Biomedical Diagnosis and Agro-Food Quality Control

by Elisabetta Poeta, Estefanía Núñez-Carmona and Veronica Sberveglieri

J. Sens. Actuator Netw. 2025, 14(3), 50; https://doi.org/10.3390/jsan14030050 - 9 May 2025

Viewed by 420

Abstract

Metal oxide semiconductor (MOX) sensors are emerging as a groundbreaking technology due to their remarkable features: high sensitivity, rapid response time, low cost, and potential for miniaturization. Their ability to detect volatile organic compounds (VOCs) in real time makes them ideal tools for [...] Read more.

Metal oxide semiconductor (MOX) sensors are emerging as a groundbreaking technology due to their remarkable features: high sensitivity, rapid response time, low cost, and potential for miniaturization. Their ability to detect volatile organic compounds (VOCs) in real time makes them ideal tools for applications across various fields, including environmental monitoring, medicine, and the food industry. This paper explores the evolution and growing utilization of MOX sensors, with a particular focus on atmospheric pollution monitoring, non-invasive disease diagnostics through the analysis of volatile compounds emitted by the human body, and food quality assessment. The crucial role of MOX sensors in monitoring the freshness of food and water, detecting chemical and biological contamination, and identifying food fraud is specifically examined. The rapid advancement of this technology offers new opportunities to improve quality of life, food safety, and public health, positioning MOX sensors as a key tool to address future challenges in these vital sectors. Full article

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

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33 pages, 1438 KiB

Open AccessArticle

Mental Disorder Assessment in IoT-Enabled WBAN Systems with Dimensionality Reduction and Deep Learning

by Damilola Olatinwo, Adnan Abu-Mahfouz and Hermanus Myburgh

J. Sens. Actuator Netw. 2025, 14(3), 49; https://doi.org/10.3390/jsan14030049 - 7 May 2025

Viewed by 179

Abstract

Mental health is an important aspect of an individual’s overall well-being. Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. Accurate and interpretable predictions of mental disorders are crucial for [...] Read more.

Mental health is an important aspect of an individual’s overall well-being. Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. Accurate and interpretable predictions of mental disorders are crucial for effective intervention. This study develops a hybrid deep learning model, integrating CNN and BiLSTM applied to EEG data, to address this need. To conduct a comprehensive analysis of mental disorders, we propose a two-tiered classification strategy. The first tier classifies the main disorder categories, while the second tier classifies the specific disorders within each main disorder category to provide detailed insights into classifying mental disorder. The methodology incorporates techniques to handle missing data (kNN imputation), class imbalance (SMOTE), and high dimensionality (PCA). To enhance clinical trust and understanding, the model’s predictions are explained using local interpretable model-agnostic explanations (LIME). Baseline methods and the proposed CNN–BiLSTM model were implemented and evaluated at both classification tiers using PSD and FC features. On unseen test data, our proposed model demonstrated a 3–9% improvement in prediction accuracy for main disorders and a 4–6% improvement for specific disorders, compared to existing methods. This approach offers the potential for more reliable and explainable diagnostic tools for mental disorder prediction. Full article

(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)

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22 pages, 2565 KiB

Open AccessReview

Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions

by Fabian Horacio Diaz, Carlos Borrás Pinilla and Cecilia E. García Cena

J. Sens. Actuator Netw. 2025, 14(3), 48; https://doi.org/10.3390/jsan14030048 - 1 May 2025

Viewed by 533

Abstract

This paper explores the design, control, construction, and leading manufacturers of upper limb rehabilitation robots through a thorough literature review. Utilizing databases such as Scopus, IEEE Xplore, Science Direct, Springer Link, and the Clinical Trials database, the research adhered to a rigorous screening [...] Read more.

This paper explores the design, control, construction, and leading manufacturers of upper limb rehabilitation robots through a thorough literature review. Utilizing databases such as Scopus, IEEE Xplore, Science Direct, Springer Link, and the Clinical Trials database, the research adhered to a rigorous screening process in accordance with PRISMA guidelines. This included analyzing abstracts and conducting comprehensive reviews of full articles when necessary. A total of fourteen relevant papers were systematically selected for in-depth analysis. The study offers a detailed classification of robotic technologies along with their Technology Readiness Levels (TRLs), discusses the primary challenges hindering their adoption, and proposes strategic research directions to address these issues. In conclusion, while upper limb robotic devices exhibit significant potential, persistent technological and design challenges must be addressed, underscoring the need for ongoing research and multidisciplinary collaboration to facilitate broader and more effective adoption. Full article

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23 pages, 3481 KiB

Open AccessArticle

Evaluating QoS in Dynamic Virtual Machine Migration: A Multi-Class Queuing Model for Edge-Cloud Systems

by Anna Kushchazli, Kseniia Leonteva, Irina Kochetkova and Abdukodir Khakimov

J. Sens. Actuator Netw. 2025, 14(3), 47; https://doi.org/10.3390/jsan14030047 - 25 Apr 2025

Viewed by 302

Abstract

The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. This study addresses the challenges of dynamically balancing [...] Read more.

The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. This study addresses the challenges of dynamically balancing server loads while minimizing downtime and migration costs under stochastic task arrivals and variable processing times. We propose a queuing theory-based model employing continuous-time Markov chains (CTMCs) to capture the interplay between VM migration decisions, server resource constraints, and task processing dynamics. The model incorporates two migration policies—one minimizing projected post-migration server utilization and another prioritizing current utilization—to evaluate their impact on system performance. The numerical results show that the blocking probability for the first VM for Policy 1 is 2.1% times lower than for Policy 2 and the same metric for the second VM is 4.7%. The average server’s resource utilization increased up to 11.96%. The framework’s adaptability to diverse server–VM configurations and stochastic demands demonstrates its applicability to real-world cloud systems. These results highlight predictive resource allocation’s role in dynamic environments. Furthermore, the study lays the groundwork for extending this framework to multi-access edge computing (MEC) environments, which are integral to 6G networks. Full article

(This article belongs to the Section Communications and Networking)

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41 pages, 5573 KiB

Open AccessReview

Socio-Organisational Challenges and Impacts of IoT: A Review in Healthcare and Banking

by Tahera Kalsoom, Naeem Ramzan, Shehzad Ahmed, Nadeem Anjum, Ghazanfar Ali Safdar and Masood Ur Rehman

J. Sens. Actuator Netw. 2025, 14(3), 46; https://doi.org/10.3390/jsan14030046 - 24 Apr 2025

Viewed by 565

Abstract

The Internet of Things (IoT) is transforming how organisations and individuals connect and interact with digital ecosystems, especially in sectors like healthcare and banking. While technological benefits have been widely discussed, the societal and organisational impacts of IoT adoption remain underexplored. This study [...] Read more.

The Internet of Things (IoT) is transforming how organisations and individuals connect and interact with digital ecosystems, especially in sectors like healthcare and banking. While technological benefits have been widely discussed, the societal and organisational impacts of IoT adoption remain underexplored. This study aims to address this gap by conducting a systematic literature review (SLR) of 110 peer-reviewed publications from 2012 to 2024 across four major academic databases. The review identifies and categorises the key applications of IoT, its social and organisational drivers, and the challenges of its implementation within the healthcare and banking sectors. The analysis reveals that critical barriers to IoT adoption include security, privacy, interoperability, and legal compliance, alongside concerns around workforce displacement and trust. This study also introduces the 5Cs framework—connectivity, continuity, compliance, coexistence, and cybersecurity—as a practical lens for addressing these challenges. The findings highlight the need for responsible IoT integration that balances innovation with ethical, social, and organisational accountability. Implications of this research inform policymakers, practitioners, and researchers on how to design human-centric and socially sustainable IoT strategies in sensitive sectors. Full article

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J. Sens. Actuator Netw., Volume 14, Issue 3 (June 2025) – 10 articles

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