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Experimental Study of Lidar System for a Static Object in Adverse Weather Conditions
Socio-Organisational Challenges and Impacts of IoT: A Review in Healthcare and Banking

Journal Description

Journal of Sensor and Actuator Networks

Journal of Sensor and Actuator Networks is an international, peer-reviewed, open access journal on the science and technology of sensor and actuator networks, published bimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Information Systems) / CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.6 days after submission; acceptance to publication is undertaken in 5.3 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 4.2 (2024); 5-Year Impact Factor: 3.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2224-2708

Latest Articles

20 pages, 7588 KiB

Open AccessArticle

Dual-Purpose Star Tracker and Space Debris Detector: Miniature Instrument for Small Satellites

by Beltran N. Arribas, João G. Maia, João P. Castanheira, Joel Filho, Rui Melicio, Hugo Onderwater, Paulo Gordo, R. Policarpo Duarte and André R. R. Silva

J. Sens. Actuator Netw. 2025, 14(4), 75; https://doi.org/10.3390/jsan14040075 (registering DOI) - 16 Jul 2025

This paper presents the conception, design and real miniature instrument implementation of a dual-purpose sensor for small satellites that can act as a star tracker and space debris detector. In the previous research work, the authors conceived, designed and implemented a breadboard consisting [...] Read more.

This paper presents the conception, design and real miniature instrument implementation of a dual-purpose sensor for small satellites that can act as a star tracker and space debris detector. In the previous research work, the authors conceived, designed and implemented a breadboard consisting of a computer laptop, a camera interface and camera controller, an image sensor, an optics system, a temperature sensor and a temperature controller. It showed that the instrument was feasible. In this paper, a new real star tracker miniature instrument is designed, physically realized and tested. The implementation follows a New Space approach; it is made with Commercial Off-the-Shelf (COTS) components with space heritage. The instrument’s development, implementation and testing are presented. Full article

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23 pages, 6850 KiB

Open AccessArticle

Optimizing Energy Consumption in Public Institutions Using AI-Based Load Shifting and Renewable Integration

by Otilia Elena Dragomir, Florin Dragomir and Marius Păun

J. Sens. Actuator Netw. 2025, 14(4), 74; https://doi.org/10.3390/jsan14040074 (registering DOI) - 15 Jul 2025

This paper details the development and implementation of an intelligent energy efficiency system for an electrical grid that incorporates renewable energy sources, specifically photovoltaic systems. The system is applied in a small locality of approximately 8000 inhabitants and aims to optimize energy consumption [...] Read more.

This paper details the development and implementation of an intelligent energy efficiency system for an electrical grid that incorporates renewable energy sources, specifically photovoltaic systems. The system is applied in a small locality of approximately 8000 inhabitants and aims to optimize energy consumption in public institutions by scheduling electrical appliances during periods of surplus PV energy production. The proposed solution employs a hybrid neuro-fuzzy approach combined with scheduling techniques to intelligently shift loads and maximize the use of locally generated green energy. This enables appliances, particularly schedulable and schedulable non-interruptible ones, to operate during peak PV production hours, thereby minimizing reliance on the national grid and improving overall energy efficiency. This directly reduces the cost of electricity consumption from the national grid. Furthermore, a comprehensive power quality analysis covering variables including harmonic distortion and voltage stability is proposed. The results indicate that while photovoltaic systems, being switching devices, can introduce some harmonic distortion, particularly during peak inverter operation or transient operating regimes, and flicker can exceed standard limits during certain periods, the overall voltage quality is maintained if proper inverter controls and grid parameters are adhered to. The system also demonstrates potential for scalability and integration with energy storage systems for enhanced future performance. Full article

(This article belongs to the Section Network Services and Applications)

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15 pages, 2538 KiB

Open AccessArticle

Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks

by Jin Hyung Park, Heoncheol Lee and Myonghun Han

J. Sens. Actuator Netw. 2025, 14(4), 73; https://doi.org/10.3390/jsan14040073 (registering DOI) - 15 Jul 2025

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time [...] Read more.

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time routing a persistent challenge. In this paper, we employ field-programmable gate arrays (FPGAs) to overcome the resource limitations of on-board computers (OBCs) and to manage energy consumption effectively using the Eclipse-Aware Routing (EAR) algorithm, and we implement the K-Shortest Paths (KSP) algorithm directly on the FPGA. Our method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate, eclipse duration, and estimated transmission load as evaluated by EAR. In large-scale LEO networks, the computational burden of KSP grows substantially as connectivity data become more voluminous and complex. To enhance performance, we accelerate complex computations in the programmable logic (PL) via pipelining and design a collaborative architecture between the processing system (PS) and PL, achieving approximately a 3.83× speedup compared to a PS-only implementation. We validate the feasibility of the proposed approach by successfully performing remote routing-table updates on the SpaceWire-based SpaceWire Brick MK4 network system. Full article

(This article belongs to the Section Communications and Networking)

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19 pages, 3841 KiB

Open AccessArticle

An Improved Chosen Plaintext Attack on JPEG Encryption

by Junhui He, Kaitian Gu, Yihan Huang, Yue Li and Xiang Chen

J. Sens. Actuator Netw. 2025, 14(4), 72; https://doi.org/10.3390/jsan14040072 (registering DOI) - 14 Jul 2025

Format-compatible encryption can be used to ensure the security and privacy of JPEG images. Recently, a JPEG image encryption method proved to be secure against known plaintext attacks by employing an adaptive encryption key, which depends on the histogram of the number of [...] Read more.

Format-compatible encryption can be used to ensure the security and privacy of JPEG images. Recently, a JPEG image encryption method proved to be secure against known plaintext attacks by employing an adaptive encryption key, which depends on the histogram of the number of non-zero alternating current coefficients (ACC) in Discrete Cosine Transform (DCT) blocks. However, this scheme has been demonstrated to be vulnerable to chosen-plaintext attacks (CPA) based on the run consistency of MCUs (RCM) between the original image and the encrypted image. In this paper, an improved CPA scheme is proposed. The method of incrementing run-length values instead of permutation is utilized to satisfy the uniqueness of run sequences of different minimum coded units (MCUs). The experimental results show that the proposed method can successfully recover the outlines of plaintext images from the encrypted images, even with lower-quality factors. Full article

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17 pages, 1820 KiB

Open AccessArticle

A Federated Learning Architecture for Bird Species Classification in Wetlands

by David Mulero-Pérez, Javier Rodriguez-Juan, Tamai Ramirez-Gordillo, Manuel Benavent-Lledo, Pablo Ruiz-Ponce, David Ortiz-Perez, Hugo Hernandez-Lopez, Anatoli Iarovikov, Jose Garcia-Rodriguez, Esther Sebastián-González, Olamide Jogunola, Segun I. Popoola and Bamidele Adebisi

J. Sens. Actuator Netw. 2025, 14(4), 71; https://doi.org/10.3390/jsan14040071 - 9 Jul 2025

Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural [...] Read more.

Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural areas with limited bandwidth resources. Despite the potential of federated learning to fine-tune deep learning models using data collected from edge devices in low-resource environments, its application in the field of bird monitoring remains underexplored. This study proposes a federated learning pipeline tailored for bird species classification in wetlands. The proposed approach is based on lightweight convolutional neural networks optimized for use on resource-constrained devices. Since the performance of federated learning is strongly influenced by the models used and the experimental setting, this study conducts a comprehensive comparison of well-known lightweight models such as WideResNet, EfficientNetV2, MNASNet, GoogLeNet and ResNet in different training settings. The results demonstrate the importance of the training setting in federated learning architectures and the suitability of the different models for bird species recognition. This work contributes to the wider application of federated learning in ecological monitoring and highlights its potential to overcome challenges such as bandwidth limitations. Full article

(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)

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18 pages, 3283 KiB

Open AccessArticle

AI-Driven Differentiation and Quantification of Metal Ions Using ITIES Electrochemical Sensors

by Muzammil M. N. Ahmed, Parth Ganeriwala, Anthi Savvidou, Nicholas Breen, Siddhartha Bhattacharyya and Pavithra Pathirathna

J. Sens. Actuator Netw. 2025, 14(4), 70; https://doi.org/10.3390/jsan14040070 - 9 Jul 2025

Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes [...] Read more.

Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes with closely spaced half-wave potentials, such as Cd²⁺ and Cu²⁺, remains a challenge, especially for non-expert users. In this work, we present a novel methodology that integrates advanced artificial intelligence (AI) models with ITIES-based sensing to automate and enhance metal ion detection. Our approach first employed a convolutional neural network to classify CVs as either ideal or faulty with an accuracy exceeding 95 percent. Ideal CVs were then further analyzed for metal ion identification, achieving a classification accuracy of 99.15 percent between Cd²⁺ and Cu²⁺ responses. Following classification, an artificial neural network was used to quantitatively predict metal ion concentrations, yielding low mean absolute errors of 0.0158 for Cd²⁺ and 0.0127 for Cu²⁺. This integrated AI–ITIES system not only provides a scientific methodology for differentiating analyte responses based on electrochemical signatures but also substantially lowers the expertise barrier for sensor signal interpretation. To our knowledge, this is the first report of the AI-assisted differentiation and quantification of metal ions from ITIES-based CVs, establishing a robust framework for the future development of user-friendly, automated electrochemical sensing platforms for environmental and biological applications. Full article

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20 pages, 682 KiB

Open AccessReview

Detecting Abnormal Behavior Events and Gatherings in Public Spaces Using Deep Learning: A Review

by Rafael Rodrigo-Guillen, Nahuel Garcia-D’Urso, Higinio Mora-Mora and Jorge Azorin-Lopez

J. Sens. Actuator Netw. 2025, 14(4), 69; https://doi.org/10.3390/jsan14040069 - 2 Jul 2025

Public security is a crucial aspect of maintaining social order. Although crime rates in Western cultures may be considered socially acceptable, it is important to continually improve security measures to prevent potential risks. With the advancements in artificial intelligence methods, particularly in deep [...] Read more.

Public security is a crucial aspect of maintaining social order. Although crime rates in Western cultures may be considered socially acceptable, it is important to continually improve security measures to prevent potential risks. With the advancements in artificial intelligence methods, particularly in deep learning and computer vision, it has become possible to detect abnormal event patterns in groups of people. This paper presents a review of the deep learning techniques employed for identifying gatherings of people and detecting anomalous events to enhance public security. Some of the open research areas are identified, including the lack of works addressing multiple cases of anomalies in large concentrations of people, which leaves open an important avenue for future scientific work. Full article

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18 pages, 1572 KiB

Open AccessCase Report

Graphene–PLA Printed Sensor Combined with XR and the IoT for Enhanced Temperature Monitoring: A Case Study

by Rohith J. Krishnamurthy and Abbas S. Milani

J. Sens. Actuator Netw. 2025, 14(4), 68; https://doi.org/10.3390/jsan14040068 - 30 Jun 2025

This case study aims to combine the advantage of the additive manufacturing of sensors with a mixed reality (MR) app, developed in a lab-scale workshop, to safely monitor and control the temperature of parts. Namely, the measurements were carried out in real time [...] Read more.

This case study aims to combine the advantage of the additive manufacturing of sensors with a mixed reality (MR) app, developed in a lab-scale workshop, to safely monitor and control the temperature of parts. Namely, the measurements were carried out in real time via a 3D-printed graphene–PLA nanocomposite sensor and communicated wirelessly using a low-power microcontroller with the IoT capability, and then transferred to the user display in the MR. In order to investigate the performance of the proposed computer-mediated reality, a user experience experiment (n = 8) was conducted. Statistical analysis results showed that the system leads to faster (>2.2 times) and more accurate (>82%) temperature control and monitoring by the users, as compared to the conventional technique using a thermal camera. Using a Holistic Presence Questionnaire (HPQ) scale, the users’ experience/training was significantly improved, while they reported less fatigue by 50%. Full article

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

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23 pages, 1560 KiB

Open AccessArticle

Practical Aspects of Cross-Vendor TSN Time Synchronization Using IEEE 802.1AS

by Kilian Brunner, Florian Frick, Martin Ostertag and Armin Lechler

J. Sens. Actuator Netw. 2025, 14(4), 67; https://doi.org/10.3390/jsan14040067 - 30 Jun 2025

Multi-vendor interoperability is essential for the stable operation, scalability, and successful market adoption of Time-Sensitive Networking (TSN). Conformance tests address protocol conformance. Informal interoperability testing and plugfests help to improve the quality and interoperability of specific implementations, and of the underlying international standard [...] Read more.

Multi-vendor interoperability is essential for the stable operation, scalability, and successful market adoption of Time-Sensitive Networking (TSN). Conformance tests address protocol conformance. Informal interoperability testing and plugfests help to improve the quality and interoperability of specific implementations, and of the underlying international standard documents. This paper presents three findings related to time synchronization in a multi-vendor TSN system. Differing interpretations of released standards and inconsistent setting of relevant system parameters resulted in undesirable behavior impacting the performance of the complete TSN system. The findings relevant to the standards themselves have been submitted to IEEE as maintenance items or are already being considered in work in progress at IEEE. In addition to interoperability testing, the importance of consistent system engineering and industry-specific TSN profiles are identified as important ingredients for successful implementation of TSN-based systems. Full article

(This article belongs to the Section Communications and Networking)

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25 pages, 6794 KiB

Open AccessArticle

Animal-Borne Adaptive Acoustic Monitoring

by Devin Jean, Jesse Turner, Will Hedgecock, György Kalmár, George Wittemyer and Ákos Lédeczi

J. Sens. Actuator Netw. 2025, 14(4), 66; https://doi.org/10.3390/jsan14040066 - 24 Jun 2025

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable [...] Read more.

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dimensional space, followed by adaptive clustering to identify events of interest. Simulation results demonstrate the system’s ability to normalize the collection of acoustic events across varying abundance levels, with rare events retained at rates of 80–85% while frequent sounds are reduced to 3–10% retention. Initial field deployments on caribou, African elephants, and bighorn sheep show promising application across diverse species and ecological contexts. Power consumption analysis indicates the need for additional optimization to achieve multi-month deployments. This technology enables the creation of novel wilderness datasets while addressing the limitations of traditional static acoustic monitoring approaches, offering new possibilities for wildlife research, ecosystem monitoring, and conservation efforts. Full article

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27 pages, 611 KiB

Open AccessReview

Edge Computing and Its Application in Robotics: A Survey

by Nazish Tahir and Ramviyas Parasuraman

J. Sens. Actuator Netw. 2025, 14(4), 65; https://doi.org/10.3390/jsan14040065 - 23 Jun 2025

The edge computing paradigm has gained prominence in both academic and industry circles in recent years. When edge computing facilities and services are implemented in robotics, they become a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications [...] Read more.

The edge computing paradigm has gained prominence in both academic and industry circles in recent years. When edge computing facilities and services are implemented in robotics, they become a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network’s edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics. Full article

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

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17 pages, 4425 KiB

Open AccessArticle

Design and Implementation of a Secure Communication Architecture for IoT Devices

by Cezar-Gabriel Dumitrache and Petre Anghelescu

J. Sens. Actuator Netw. 2025, 14(4), 64; https://doi.org/10.3390/jsan14040064 - 23 Jun 2025

This paper explores the integration of Internet of Things (IoT) devices into modern cybersecurity frameworks, and it is intended to be a binder for the incorporation of these devices into emerging cybersecurity paradigms. Most IoT devices rely on WPA2-personal protocol, a wireless protocol [...] Read more.

This paper explores the integration of Internet of Things (IoT) devices into modern cybersecurity frameworks, and it is intended to be a binder for the incorporation of these devices into emerging cybersecurity paradigms. Most IoT devices rely on WPA2-personal protocol, a wireless protocol with known security flaws, being effortless to penetrate by using various specific tools. Through this paper, we proposed the use of two Raspberry Pi platforms, with the help of which we created a secure wireless connection by implementing the 802.1X protocol and using digital certificates. Implementing this type of architecture and the devices used, we obtained huge benefits from the point of view of security and energy consumption. We tested multiple authentication methods, including EAP-TLS and EAP-MSCHAPv2, with the Raspberry Pi acting as an authentication server and certificate manager. Performance metrics such as power consumption, latency, and network throughput were analysed, confirming the architecture’s effectiveness and scalability for larger IoT deployments. 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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19 pages, 744 KiB

Open AccessArticle

Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection

by Renkai Zhao and Gia Khanh Tran

J. Sens. Actuator Netw. 2025, 14(3), 63; https://doi.org/10.3390/jsan14030063 - 16 Jun 2025

In Japan, natural disasters occur frequently. Serious disasters may cause damage to traffic networks and telecommunication infrastructures, leading to the occurrence of isolated disaster areas. In this article, unmanned aerial vehicles (UAVs) are used for data collection instead of unavailable ground-based stations in [...] Read more.

In Japan, natural disasters occur frequently. Serious disasters may cause damage to traffic networks and telecommunication infrastructures, leading to the occurrence of isolated disaster areas. In this article, unmanned aerial vehicles (UAVs) are used for data collection instead of unavailable ground-based stations in isolated disaster areas. Detailed information about the damage situation will be collected from the user equipment (UE) by a UAV through a fly–hover–fly procedure, and then will be sent to the disaster response headquarters for disaster relief. However, mission completion time minimization becomes a crucial task, considering the requirement of rapid response and the battery constraint of UAVs. Therefore, the author proposed a three-dimensional UAV flight trajectory, discussing the optimal flight altitude and placement of hovering points by transforming the original problem of K-means clustering into a location set cover problem (LSCP) that can be solved via a genetic algorithm (GA) approach. The simulation results have shown the feasibility of the proposed method to reduce the mission completion time. Full article

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31 pages, 1240 KiB

Open AccessArticle

An Adaptive PSO Approach with Modified Position Equation for Optimizing Critical Node Detection in Large-Scale Networks: Application to Wireless Sensor Networks

by Abdelmoujib Megzari, Walid Osamy, Bader Alwasel and Ahmed M. Khedr

J. Sens. Actuator Netw. 2025, 14(3), 62; https://doi.org/10.3390/jsan14030062 - 16 Jun 2025

In recent years, wireless sensor networks (WSNs) have been employed across various domains, including military services, healthcare, disaster response, industrial automation, and smart infrastructure. Due to the absence of fixed communication infrastructure, WSNs rely on ad hoc connections between sensor nodes to transmit [...] Read more.

In recent years, wireless sensor networks (WSNs) have been employed across various domains, including military services, healthcare, disaster response, industrial automation, and smart infrastructure. Due to the absence of fixed communication infrastructure, WSNs rely on ad hoc connections between sensor nodes to transmit sensed data to target nodes. Within a WSN, a sensor node whose failure partitions the network into disconnected segments is referred to as a critical node or cut vertex. Identifying such nodes is a fundamental step toward ensuring the reliability of WSNs. The critical node detection problem (CNDP) focuses on determining the set of nodes whose removal most significantly affects the network’s connectivity, stability, functionality, robustness, and resilience. CNDP is a significant challenge in network analysis that involves identifying the nodes that have a significant influence on connectivity or centrality measures within a network. However, achieving an optimal solution for the CNDP is often hindered by its time-consuming and computationally intensive nature, especially when dealing with large-scale networks. In response to this challenge, we present a method based on particle swarm optimization (PSO) for the detection of critical nodes. We employ discrete PSO (DPSO) along with the modified position equation (MPE) to effectively solve the CNDP, making it applicable to various k-vertex variations of the problem. We examine the impact of population size on both execution time and result quality. Experimental analysisusing different neighborhood topologies—namely, the star topology and the dynamic topology—was conducted to analyze their impact on solution effectiveness and adaptability to diverse network configurations. We consistently observed better result quality with the dynamic topology compared to the star topology for the same population size, while the star topology exhibited better execution time. Our findings reveal the promising efficacy of the proposed solution in addressing the CNDP, achieving high-quality solutions compared to existing methods. Full article

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

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14 pages, 3134 KiB

Open AccessArticle

Movement Direction Classification Using Low-Resolution ToF Sensor and LSTM-Based Neural Network

by Sejik Oh, Kyoung Min Lee, Seok Young Lee and Nam Kyu Kwon

J. Sens. Actuator Netw. 2025, 14(3), 61; https://doi.org/10.3390/jsan14030061 - 11 Jun 2025

This study proposes an effective method for identifying human movement direction in indoor environments by leveraging a low-resolution time-of-flight (ToF) sensor and a long short-term memory (LSTM) neural network model. While previous studies have employed camera-based or high-resolution ToF-based sensors, we utilize an [...] Read more.

This study proposes an effective method for identifying human movement direction in indoor environments by leveraging a low-resolution time-of-flight (ToF) sensor and a long short-term memory (LSTM) neural network model. While previous studies have employed camera-based or high-resolution ToF-based sensors, we utilize an 8 × 8 array ToF sensor, which is neither expensive nor related to any privacy issues. Furthermore, in contrast to the conventional rule-based algorithm, the proposed method employs the LSTM model to effectively handle the sequential time-series data. Experimental evaluations, including both basic single-person scenarios and complex multi-user challenge scenarios, confirm that the proposed LSTM-based approach achieves outstanding accuracy of 98% in identifying human entry and exit movements. 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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16 pages, 8659 KiB

Open AccessArticle

Dielectric Wireless Passive Temperature Sensor

by Taimur Aftab, Shah Hussain, Leonhard M. Reindl and Stefan Johann Rupitsch

J. Sens. Actuator Netw. 2025, 14(3), 60; https://doi.org/10.3390/jsan14030060 - 6 Jun 2025

Resonators are passive components that respond to an excitation signal by oscillating at their natural frequency with an exponentially decreasing amplitude. When combined with antennas, resonators enable purely passive chipless sensors that can be read wirelessly. In this contribution, we investigate the properties [...] Read more.

Resonators are passive components that respond to an excitation signal by oscillating at their natural frequency with an exponentially decreasing amplitude. When combined with antennas, resonators enable purely passive chipless sensors that can be read wirelessly. In this contribution, we investigate the properties of dielectric resonators, which combine the following functionalities: They store the readout signal for a sufficiently long time and couple to free space electromagnetic waves to act as antennas. Their mode spectrum, along with their resonant frequencies, quality factor, and coupling to electromagnetic waves, is investigated using a commercial finite element program. The fundamental mode exhibits a too-low overall Q factor. However, some higher modes feature overall Q factors of several thousand, which allows them to act as transponders operating without integrated circuits, batteries, or antennas. To experimentally verify the simulations, isolated dielectric resonators exhibiting modes with similarly high radiation-induced and dissipative quality factors were placed on a low-loss, low permittivity ceramic holder, allowing their far-field radiation properties to be measured. The radiation patterns investigated in the laboratory and outdoors agree well with the simulations. The resulting radiation patterns show a directivity of approximately 7.5 dBi at 2.5 GHz. The sensor was then heated in a ceramic furnace with the readout antenna located outside at room temperature. Wireless temperature measurements up to 700 °C with a resolution of 0.5 °C from a distance of 1 m demonstrated the performance of dielectric resonators for practical applications. Full article

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

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32 pages, 5088 KiB

Open AccessArticle

IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming

by Nezha Kharraz, András Revoly and István Szabó

J. Sens. Actuator Netw. 2025, 14(3), 59; https://doi.org/10.3390/jsan14030059 - 4 Jun 2025

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce ( [...] Read more.

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

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40 pages, 3224 KiB

Open AccessArticle

A Comparative Study of Image Processing and Machine Learning Methods for Classification of Rail Welding Defects

by Mohale Emmanuel Molefe, Jules Raymond Tapamo and Siboniso Sithembiso Vilakazi

J. Sens. Actuator Netw. 2025, 14(3), 58; https://doi.org/10.3390/jsan14030058 - 29 May 2025

Defects formed during the thermite welding process of two sections of rails require the welded joints to be inspected for quality, and the most used non-destructive method for inspection is radiography testing. However, the conventional defect investigation process from the obtained radiography images [...] Read more.

Defects formed during the thermite welding process of two sections of rails require the welded joints to be inspected for quality, and the most used non-destructive method for inspection is radiography testing. However, the conventional defect investigation process from the obtained radiography images is costly, lengthy, and subjective as it is conducted manually by trained experts. Additionally, it has been shown that most rail breaks occur due to a crack initiated from the weld joint defect that was either misclassified or undetected. To improve the condition monitoring of rails, the railway industry requires an automated defect investigation system capable of detecting and classifying defects automatically. Therefore, this work proposes a method based on image processing and machine learning techniques for the automated investigation of defects. Histogram Equalization methods are first applied to improve image quality. Then, the extraction of the weld joint from the image background is achieved using the Chan–Vese Active Contour Model. A comparative investigation is carried out between Deep Convolution Neural Networks, Local Binary Pattern extractors, and Bag of Visual Words methods (with the Speeded-Up Robust Features extractor) for extracting features in weld joint images. Classification of features extracted by local feature extractors is achieved using Support Vector Machines, K-Nearest Neighbor, and Naive Bayes classifiers. The highest classification accuracy of 95% is achieved by the Deep Convolution Neural Network model. A Graphical User Interface is provided for the onsite investigation of defects. Full article

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

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27 pages, 1103 KiB

Open AccessSystematic Review

Authentication Techniques in Internet of Drones (IoD): Taxonomy, Open Challenges and Future Directions

by Alanoud F. Aldweesh and Abdullah M. Almuhaideb

J. Sens. Actuator Netw. 2025, 14(3), 57; https://doi.org/10.3390/jsan14030057 - 27 May 2025

Recently, Internet of Drones (IoD) applications have grown in various fields, including the military, healthcare, smart agriculture, and traffic monitoring. Drones are equipped with computation resources, communication units, and embedded systems that allow them to sense, collect, and deliver data in real-time through [...] Read more.

Recently, Internet of Drones (IoD) applications have grown in various fields, including the military, healthcare, smart agriculture, and traffic monitoring. Drones are equipped with computation resources, communication units, and embedded systems that allow them to sense, collect, and deliver data in real-time through public communication channels. However, this fact introduces the risk of attack on data transmitted over unsecured public channels. Addressing several security threats is crucial to ensuring the secure operation of IoD networks. Robust authentication protocols play a vital role in establishing secure processes in the IoD environment. However, designing efficient and lightweight authentication solutions is a complex task due to the unique characteristics of the IoD and the limitations of drones in terms of their communication and computational capabilities. There is a need to review the role of authentication processes in controlling security threats in the IoD due to the increasing complexity and frequency of security breaches. This review will present the primary issues and future path directions for authentication schemes in the IoD and provide a framework for relevant existing schemes to facilitate future research into the IoD. Consequently, in this paper, we review the literature to highlight the research conducted in this area of the IoD. This study reviews several existing methods for authenticating entities in the IoD environment. Moreover, this study discusses security requirements and highlights several challenges encountered with the authentication schemes used in the IoD. The findings of this paper suggest future directions for research to consider in order for this domain to continue to evolve. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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20 pages, 5170 KiB

Open AccessArticle

Experimental Study of Lidar System for a Static Object in Adverse Weather Conditions

by Saulius Japertas, Rūta Jankūnienė and Roy Knechtel

J. Sens. Actuator Netw. 2025, 14(3), 56; https://doi.org/10.3390/jsan14030056 - 26 May 2025

Thanks to light detection and ranging (LiDAR), unmanned ground vehicles (UGVs) are able to detect different objects in their environment and measure the distance between them. This device gives the ability to see its surroundings in real time. However, the accuracy of LiDAR [...] Read more.

Thanks to light detection and ranging (LiDAR), unmanned ground vehicles (UGVs) are able to detect different objects in their environment and measure the distance between them. This device gives the ability to see its surroundings in real time. However, the accuracy of LiDAR can be reduced, especially in rainy weather, fog, urban smog and the like. These factors can have disastrous consequences as they increase the errors in the vehicle’s control computer. The aim of this research was to determine the most appropriate LiDAR frequency for static objects, depending on the distance to them and the scanning frequency in different weather conditions; therefore, it is based on empiric data obtained by using the RoboPeak A1M8 LiDAR. The results obtained in rainy conditions are compared with the same ones in clear weather, using stochastic methods. A direct influence of both the frequencies used and the rain on the accuracy of the LiDAR measurements was found. The range measurement errors increase in rainy weather; as the scanning frequency increases, the results become more accurate but capture a smaller number of object points. The higher frequencies lead to about five times less error at the farthest distances compared to the lower frequencies. Full article

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

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