Journal of Sensor and Actuator Networks

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

Abstract

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

Abstract

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

Viewed by 304

Abstract

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

Viewed by 148

Abstract

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

Viewed by 310

Abstract

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

Viewed by 401

Abstract

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

Viewed by 343

Abstract

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

Viewed by 274

Abstract

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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J. Sens. Actuator Netw., Volume 14, Issue 4 (August 2025) – 8 articles

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