Journal of Sensor and Actuator Networks

17 pages, 4669 KB

Open AccessArticle

Compact Bio-Inspired Terahertz Ultrawideband Antenna: A Viburnum tinus-Based Approach for 6G and Beyond Applications

by Jeremiah O. Abolade, Dominic B. O. Konditi, Pradeep Kumar and Grace Olaleru

J. Sens. Actuator Netw. 2025, 14(6), 107; https://doi.org/10.3390/jsan14060107 - 30 Oct 2025

A compact bio-inspired terahertz wideband antenna is presented in this work. The proposed antenna is based on Viburnum tinus leaf shape, a defective ground plane, a folded-ring slot, and parasitic elements. The footprint of the proposed antenna is [...] Read more.

A compact bio-inspired terahertz wideband antenna is presented in this work. The proposed antenna is based on Viburnum tinus leaf shape, a defective ground plane, a folded-ring slot, and parasitic elements. The footprint of the proposed antenna is

0.46 \times 0.18 λ_{g}^{2}

at 0.18 THz. A bandwidth of 0.536 THz (0.18–0.72 THz) is achieved with a band notch at 0.35 THz (0.3–0.36 THz). The proposed antenna has a peak gain of 5 dBi and the stable radiation patterns. The proposed antenna is validated through a finite difference time domain simulator and the equivalent circuit analysis. The results from show a good correlation. Also, an extensive parametric analysis is performed, and the comparative analysis of the proposed antenna with the existing antennas shows that the proposed antenna is compact with competitive performance metrics such as gain, efficiency, and notch-band characteristics. Therefore, the proposed antenna (hereafter referred to as VTB-A) is a promising candidate for future terahertz wireless communications (5G, 6G, and beyond) and terahertz imaging. Full article

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38 pages, 23830 KB

Open AccessArticle

Improving Audio Steganography Transmission over Various Wireless Channels

by Azhar A. Hamdi, Asmaa A. Eyssa, Mahmoud I. Abdalla, Mohammed ElAffendi, Ali Abdullah S. AlQahtani, Abdelhamied A. Ateya and Rania A. Elsayed

J. Sens. Actuator Netw. 2025, 14(6), 106; https://doi.org/10.3390/jsan14060106 - 30 Oct 2025

Abstract

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This [...] Read more.

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels. Full article

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

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15 pages, 3144 KB

Open AccessReview

Neural Interfaces for Robotics and Prosthetics: Current Trends

by Saket Sarkar and Redwan Alqasemi

J. Sens. Actuator Netw. 2025, 14(6), 105; https://doi.org/10.3390/jsan14060105 - 27 Oct 2025

Abstract

The integration of neural interfaces with assistive robotics has transformed the field of prosthetics, rehabilitation, and brain–computer interfaces (BCIs). From brain-controlled wheelchairs to Artificial Intelligence (AI)-synchronized robotic arms, the innovations offer autonomy and improved quality of life for people with mobility disorders. This [...] Read more.

The integration of neural interfaces with assistive robotics has transformed the field of prosthetics, rehabilitation, and brain–computer interfaces (BCIs). From brain-controlled wheelchairs to Artificial Intelligence (AI)-synchronized robotic arms, the innovations offer autonomy and improved quality of life for people with mobility disorders. This article discusses recent trends in brain–computer interfaces and their application in robotic assistive devices, such as wheelchair-mounted arms, drone control systems, and robotic limbs for activities of daily living (ADLs). It also discusses the incorporation of AI systems, including ChatGPT-4, into BCIs, with an emphasis on new innovations in shared autonomy, cognitive assistance, and ethical considerations. Full article

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

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5 pages, 162 KB

Open AccessEditorial

Fault Diagnosis in IoT Applications: Advances, Challenges, and Future Directions

by Giovanni Cicceri and Fabrizio De Vita

J. Sens. Actuator Netw. 2025, 14(6), 104; https://doi.org/10.3390/jsan14060104 - 27 Oct 2025

Abstract

The rise of the Internet of Things (IoT) has revolutionized the way industrial, structural, and environmental systems are monitored and maintained [...] Full article

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

17 pages, 4603 KB

Open AccessArticle

Development of Optical and Electrical Sensors for Non-Invasive Monitoring of Plant Water Status

by Nasreddine Makni, Riccardo Collu and Massimo Barbaro

J. Sens. Actuator Netw. 2025, 14(5), 103; https://doi.org/10.3390/jsan14050103 - 21 Oct 2025

Abstract

Monitoring plant water status is vital for optimizing irrigation in precision agriculture. This study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues. This [...] Read more.

Monitoring plant water status is vital for optimizing irrigation in precision agriculture. This study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues. This approach is well-suited for the realization of large networks of distributed sensors wirelessly connected to a central hub. An outdoor experiment was conducted over two phases of 20 day-experiment involving six Hydrangea macrophylla plants subjected to two irrigation treatments: a control group (well-irrigated) and a test group (poorly irrigated) designed to induce water stress. The standard relative water content (RWC) method validated the treatment effects on the plants, and both EIS and IR sensors effectively distinguished between the two groups. Impedance-derived parameters, particularly the normalized intracellular resistance (R0) and the cell membrane capacitance (C0), exhibited statistically significant differences between the treatments. In addition, the IR measurements showed moderate correlations with RWC, with determination coefficients of R² = 0.56 and R² = 0.51 for first and second phases of the experiment, respectively. Despite some limitations concerning the electrode–leaf conformity and external sunlight interference, the results point to the advantages of these methods for real-time plant monitoring and decision-making in smart irrigation systems. Full article

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

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28 pages, 3748 KB

Open AccessArticle

Enabling Adaptive Food Monitoring Through Sampling Rate Adaptation for Efficient, Reliable Critical Event Detection

by Elia Henrichs, Dana Jox, Pia Schweizer and Christian Krupitzer

J. Sens. Actuator Netw. 2025, 14(5), 102; https://doi.org/10.3390/jsan14050102 - 14 Oct 2025

Abstract

Monitoring systems are essential in many fields, such as food production, storage, and supply, to collect information about applications or their environments to enable decision-making. However, these systems generate massive amounts of data that require substantial processing. To improve data analysis efficiency and [...] Read more.

Monitoring systems are essential in many fields, such as food production, storage, and supply, to collect information about applications or their environments to enable decision-making. However, these systems generate massive amounts of data that require substantial processing. To improve data analysis efficiency and reduce data collectors’ energy demand, adaptive monitoring is a promising approach to reduce the gathered data while ensuring the monitoring of critical events. Adaptive monitoring is a system’s ability to adjust its monitoring activity during runtime in response to internal and external changes. This work investigates the application of adaptive monitoring—especially, the adaptation of the sensor sampling rate—in dynamic and unstable environments. This work evaluates 11 distinct approaches, based on threshold determination, statistical analysis techniques, and optimization methods, encompassing 33 customized implementations, regarding their data reduction extent and identification of critical events. Furthermore, analyses of Shannon’s entropy and the oscillation behavior allow for estimating the efficiency of the adaptation algorithms. The results demonstrate the applicability of adaptive monitoring in food storage environments, such as cold storage rooms and transportation containers, but also reveal differences in the approaches’ performance. Generally, some approaches achieve high observation accuracies while significantly reducing the data collected by adapting efficiently. Full article

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30 pages, 27154 KB

Open AccessArticle

The Modeling and Detection of Vascular Stenosis Based on Molecular Communication in the Internet of Things

by Zitong Shao, Pengfei Zhang, Xiaofang Wang and Pengfei Lu

J. Sens. Actuator Netw. 2025, 14(5), 101; https://doi.org/10.3390/jsan14050101 - 10 Oct 2025

Abstract

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which [...] Read more.

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article

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

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36 pages, 1954 KB

Open AccessArticle

VeMisNet: Enhanced Feature Engineering for Deep Learning-Based Misbehavior Detection in Vehicular Ad Hoc Networks

by Nayera Youness, Ahmad Mostafa, Mohamed A. Sobh, Ayman M. Bahaa and Khaled Nagaty

J. Sens. Actuator Netw. 2025, 14(5), 100; https://doi.org/10.3390/jsan14050100 - 9 Oct 2025

Abstract

Ensuring secure and reliable communication in Vehicular Ad hoc Networks (VANETs) is critical for safe transportation systems. This paper presents Vehicular Misbehavior Network (VeMisNet), a deep learning framework for detecting misbehaving vehicles, with primary contributions in systematic feature engineering and scalability analysis. VeMisNet [...] Read more.

Ensuring secure and reliable communication in Vehicular Ad hoc Networks (VANETs) is critical for safe transportation systems. This paper presents Vehicular Misbehavior Network (VeMisNet), a deep learning framework for detecting misbehaving vehicles, with primary contributions in systematic feature engineering and scalability analysis. VeMisNet introduces domain-informed spatiotemporal features—including DSRC neighborhood density, inter-message timing patterns, and communication frequency analysis—derived from the publicly available VeReMi Extension Dataset. The framework evaluates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM architectures across dataset scales from 100 K to 2 M samples, encompassing all 20 attack categories. To address severe class imbalance (59.6% legitimate vehicles), VeMisNet applies SMOTE post train–test split, preventing data leakage while enabling balanced evaluation. Bidirectional LSTM with engineered features achieves 99.81% accuracy and F1-score on 500 K samples, with remarkable scalability maintaining >99.5% accuracy at 2 M samples. Critical metrics include 0.19% missed attack rates, under 0.05% false alarms, and 41.76 ms inference latency. The study acknowledges important limitations, including reliance on simulated data, single-split evaluation, and potential adversarial vulnerability. Domain-informed feature engineering provides 27.5% relative improvement over dimensionality reduction and 22-fold better scalability than basic features. These results establish new VANET misbehavior detection benchmarks while providing honest assessment of deployment readiness and research constraints. Full article

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34 pages, 3231 KB

Open AccessReview

A Review of Smart Crop Technologies for Resource Constrained Environments: Leveraging Multimodal Data Fusion, Edge-to-Cloud Computing, and IoT Virtualization

by Damilola D. Olatinwo, Herman C. Myburgh, Allan De Freitas and Adnan M. Abu-Mahfouz

J. Sens. Actuator Netw. 2025, 14(5), 99; https://doi.org/10.3390/jsan14050099 - 9 Oct 2025

Abstract

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent [...] Read more.

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

(This article belongs to the Special Issue Remote Sensing and IoT Application for Smart Agriculture)

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28 pages, 1927 KB

Open AccessSystematic Review

Drone Imaging and Sensors for Situational Awareness in Hazardous Environments: A Systematic Review

by Siripan Rattanaamporn, Asanka Perera, Andy Nguyen, Thanh Binh Ngo and Javaan Chahl

J. Sens. Actuator Netw. 2025, 14(5), 98; https://doi.org/10.3390/jsan14050098 - 29 Sep 2025

Abstract

Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This [...] Read more.

Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This study explores the integration of advanced technologies, focusing on imaging and sensor technologies such as thermal, spectral, and multispectral cameras, deployed in critical zones. By merging these technologies into UAV platforms, responders gain access to essential real-time information while reducing human exposure to hazardous conditions. This study presents case studies and practical applications, highlighting the effectiveness of these technologies in a range of hazardous situations. Full article

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

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42 pages, 5827 KB

Open AccessReview

A Review of Reconfigurable Intelligent Surfaces in Underwater Wireless Communication: Challenges and Future Directions

by Tharuka Govinda Waduge, Yang Yang and Boon-Chong Seet

J. Sens. Actuator Netw. 2025, 14(5), 97; https://doi.org/10.3390/jsan14050097 - 26 Sep 2025

Abstract

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater [...] Read more.

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article

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

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15 pages, 603 KB

Open AccessArticle

A Hybrid CNN–GRU Deep Learning Model for IoT Network Intrusion Detection

by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga and Oyeniyi Akeem Alimi

J. Sens. Actuator Netw. 2025, 14(5), 96; https://doi.org/10.3390/jsan14050096 - 26 Sep 2025

Abstract

Internet of Things (IoT) networks are constantly exposed to various security challenges and vulnerabilities, including manipulative data injections and cyberattacks. Traditional security measures are often inadequate, overburdened, and unreliable in adapting to the heterogeneous yet diverse nature of IoT networks. This emphasizes the [...] Read more.

Internet of Things (IoT) networks are constantly exposed to various security challenges and vulnerabilities, including manipulative data injections and cyberattacks. Traditional security measures are often inadequate, overburdened, and unreliable in adapting to the heterogeneous yet diverse nature of IoT networks. This emphasizes the need for intelligent and effective methodologies. In recent times, deep learning models have been extensively used to monitor and detect intrusions in complex applications. The models can effectively learn and understand the dynamic characteristics of voluminous IoT datasets to prompt efficient decision-making predictions. This study proposes a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) algorithm to enhance intrusion detection in IoT environments. The proposed CNN-GRU model is validated using two benchmark datasets: the IoTID20 and BoT-IoT intrusion detection datasets. The proposed model incorporates an effective technique to handle the class imbalance issues that are peculiar to voluminous datasets. The results demonstrate superior accuracy, precision, recall, F1-score, and area under the curve, with a reduced false positive rate compared to similar models in the literature. Specifically, the proposed CNN–GRU achieved up to 99.83% and 99.01% accuracy, surpassing baseline models by a margin of 2–3% across both datasets. These findings highlight the model’s potential for real-time cybersecurity applications in IoT networks and general industrial control systems. 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, 585 KB

Open AccessArticle

T-Way Combinatorial Testing Strategy Using a Refined Evolutionary Heuristic

by Peng Lin, Jinzhao She and Xiang Chen

J. Sens. Actuator Netw. 2025, 14(5), 95; https://doi.org/10.3390/jsan14050095 - 25 Sep 2025

Abstract

In complex testing scenarios of large-scale information systems, communication networks, and the Internet of Things, exhaustive testing is always prohibitively expensive and time-consuming. T-way combinatorial testing has emerged as a cost-effective solution. To address the problem of generating test suites for t [...] Read more.

In complex testing scenarios of large-scale information systems, communication networks, and the Internet of Things, exhaustive testing is always prohibitively expensive and time-consuming. T-way combinatorial testing has emerged as a cost-effective solution. To address the problem of generating test suites for t-way combinatorial testing, a Logical Combination Index Table (LCIT) is proposed. Utilizing the LCIT, the t-way combinatorial coverage model (t-wCCM) is constructed to guide the test case generation process. Multi-start Construction Procedure (MsCP) algorithm is employed to generate an initial solution set, and then local optimization is performed using a low-complexity Balanced Local Search (BLS) algorithm. Further, Evolutionary Path Relinking combined with the BLS (EvPR + BLS) algorithm is proposed to accelerate the convergence process. Experiments show that the proposed Refined Evolutionary Heuristic (REH) algorithm performs best on 50% of the classic test instances, and performs superior to the average on 66% of the test instances, with a relative improvement in the maximum computation time of approximately 33.33%. Full article

(This article belongs to the Section Communications and Networking)

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42 pages, 12059 KB

Open AccessReview

A Survey of Three-Dimensional Wireless Sensor Networks Deployment Techniques

by Tingting Cao, Fan Yang, Chensiyu Fan, Ru Han, Xing Yang and Lei Shu

J. Sens. Actuator Netw. 2025, 14(5), 94; https://doi.org/10.3390/jsan14050094 - 24 Sep 2025

Abstract

Three-dimensional (3D) wireless sensor networks (WSNs) are gaining increasing significance in applications across complex environments, including underwater monitoring, mountainous terrains, and smart cities. Compared to two-dimensional (2D) WSNs, 3D WSNs introduce unique challenges in coverage, connectivity, map construction, and blind area detection. This [...] Read more.

Three-dimensional (3D) wireless sensor networks (WSNs) are gaining increasing significance in applications across complex environments, including underwater monitoring, mountainous terrains, and smart cities. Compared to two-dimensional (2D) WSNs, 3D WSNs introduce unique challenges in coverage, connectivity, map construction, and blind area detection. This paper provides a comprehensive survey of node deployment strategies in 3D WSNs. We summarize several key design aspects: sensing models, occlusion detection, coverage and connectivity, sensor mobility, signal and protocol effects, and simulation map construction. Deployment algorithms are categorized into six main types: classical algorithms, computational geometry algorithms, virtual force algorithms, evolutionary algorithms, swarm intelligence algorithms, and approximation algorithms. For each category, we review representative works, analyze their design principles, and evaluate their advantages and limitations. Comparative summaries are included to facilitate algorithm selection based on specific deployment requirements. Recent advancements in these strategies have led to significant improvements in network performance, with some algorithms achieving up to 12.5% lower cost and 30% higher coverage compared to earlier methods, and even reaching 100% coverage in certain cases. Thus, this survey aims to present the current research status and highlight practical improvements, offering a reference for understanding existing approaches and selecting appropriate algorithms for diverse deployment scenarios. Full article

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20 pages, 7858 KB

Open AccessArticle

Optimizing CO₂ Monitoring: Evaluating a Sensor Network Design

by Kenia Elizabeth Sabando-Bravo, Marlon Navia and Jorge Luis Zambrano-Martinez

J. Sens. Actuator Netw. 2025, 14(5), 93; https://doi.org/10.3390/jsan14050093 - 19 Sep 2025

Abstract

In the present work, a sensor network design for monitoring carbon dioxide (CO₂) pollution in Portoviejo City, Ecuador, is evaluated through a methodology that combines simulation and physical implementation. This methodology involves the development and evaluation of two scenarios: an initial [...] Read more.

In the present work, a sensor network design for monitoring carbon dioxide (CO₂) pollution in Portoviejo City, Ecuador, is evaluated through a methodology that combines simulation and physical implementation. This methodology involves the development and evaluation of two scenarios: an initial scenario (A), developed through both physical implementation and simulation, and another simulation scenario (B). Both simulated scenarios are created using CupCarbon version 6.51 software. In these scenarios, the functionality of Wireless Sensor Networks (WSNs) is analyzed by implementing the LoRaWAN communication technology. Furthermore, the MQ-135 sensor is used to obtaining data on the PPM of (CO₂) in order to examine the areas that concentrate the most significant amount of this atmospheric pollutant. The proposed networks are evaluated using the packet loss metric during data transmission. After implementation, analysis, and respective evaluation, it can be concluded that the network simulated in Scenario B is suitable for monitoring (CO₂) and other pollutants that can be analyzed within the urban environment. Full article

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

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16 pages, 15073 KB

Open AccessArticle

A Bidirectional, Full-Duplex, Implantable Wireless CMOS System for Prosthetic Control

by Riccardo Collu, Cinzia Salis, Elena Ferrazzano and Massimo Barbaro

J. Sens. Actuator Netw. 2025, 14(5), 92; https://doi.org/10.3390/jsan14050092 - 10 Sep 2025

Abstract

Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s [...] Read more.

Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s health because of possible toxic chemical release or overheating, and it requires periodic surgery for replacement. This paper proposes a batteryless implantable device powered by an inductive link and equipped with bidirectional wireless communication channels. The device, designed in a 180 nm CMOS process, is based on two different pairs of mutually coupled inductors that provide, respectively, power and a low-bitrate bidirectional communication link and a separate, high-bitrate, one-directional upstream connection. The main link is based on a 13.56 MHz carrier and allows power transmission and a half-duplex two-way communication at 106 kbps (downlink) and 30 kbps (uplink). The secondary link is based on a 27 MHz carrier, which provides one-way communication at 2.25 Mbps only in uplink. The low-bitrate links are needed to send commands and monitor the implanted system, while the high-bitrate link is required to receive a continuous stream of information from the implanted sensing devices. The microchip acts as a hub for power and data wireless transmission capable of managing up to four different neural recording and stimulation front ends, making the device employable in a complex, distributed, bidirectional neural prosthetic system. Full article

(This article belongs to the Topic Wireless Energy Harvesting and Power Transfer for Communications and Networks)

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24 pages, 3485 KB

Open AccessArticle

Impact Evaluation of Sound Dataset Augmentation and Synthetic Generation upon Classification Accuracy

by Eleni Tsalera, Andreas Papadakis, Gerasimos Pagiatakis and Maria Samarakou

J. Sens. Actuator Netw. 2025, 14(5), 91; https://doi.org/10.3390/jsan14050091 - 9 Sep 2025

Cited by 1

Abstract

We investigate the impact of dataset augmentation and synthetic generation techniques on the accuracy of supervised audio classification based on state-of-the-art neural networks used as classifiers. Dataset augmentation techniques are applied upon the raw sound and its transformed image format. Specifically, sound augmentation [...] Read more.

We investigate the impact of dataset augmentation and synthetic generation techniques on the accuracy of supervised audio classification based on state-of-the-art neural networks used as classifiers. Dataset augmentation techniques are applied upon the raw sound and its transformed image format. Specifically, sound augmentation techniques are applied prior to spectral-based transformation and include time stretching, pitch shifting, noise addition, volume controlling, and time shifting. Image augmentation techniques are applied after the transformation of the sound into a scalogram, involving scaling, shearing, rotation, and translation. Synthetic sound generation is based on the AudioGen generative model, triggered through a series of customized prompts. Augmentation and synthetic generation are applied to three sound categories: (a) human sounds, (b) animal sounds, and (c) sounds of things, with each category containing ten sound classes with 20 samples retrieved from the ESC-50 dataset. Sound- and image-orientated neural network classifiers have been used to classify the augmented datasets and their synthetic additions. VGGish and YAMNet (sound classifiers) employ spectrograms, while ResNet50 and DarkNet53 (image classifiers) employ scalograms. The streamlined AI-based process of augmentation and synthetic generation, enhanced classifier fine-tuning and inference allowed for a consistent, multicriteria-comparison of the impact. Classification accuracy has increased for all augmentation and synthetic generation scenarios; however, the increase has not been uniform among the techniques, the sound types, and the percentage of the training set population increase. The average increase in classification accuracy ranged from 2.05% for ResNet50 to 9.05% for VGGish. Our findings reinforce the benefit of audio augmentation and synthetic generation, providing guidelines to avoid accuracy degradation due to overuse and distortion of key audio features. Full article

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

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28 pages, 9195 KB

Open AccessArticle

DAR-MDE: Depth-Attention Refinement for Multi-Scale Monocular Depth Estimation

by Saddam Abdulwahab, Hatem A. Rashwan, Moumen T. El-Melegy and Domenec Puig

J. Sens. Actuator Netw. 2025, 14(5), 90; https://doi.org/10.3390/jsan14050090 - 1 Sep 2025

Abstract

Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose [...] Read more.

Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose DAR-MDE, a novel framework that combines an autoencoder backbone with a Multi-Scale Feature Aggregation (MSFA) module and a Refining Attention Network (RAN). The MSFA module enables the model to capture geometric details across multiple resolutions, while the RAN enhances depth predictions by attending to structurally important regions guided by depth-feature similarity. We also introduce a multi-scale loss based on curvilinear saliency to improve edge-aware supervision and depth continuity. The proposed model achieves robust and accurate depth estimation across varying object scales, cluttered scenes, and weak-texture regions. We evaluated DAR-MDE on the NYU Depth v2, SUN RGB-D, and Make3D datasets, demonstrating competitive accuracy and real-time inference speeds (19 ms per image) without relying on auxiliary sensors. Our method achieves a

δ

< 1.25 accuracy of 87.25% and a relative error of 0.113 on NYU Depth v2, outperforming several recent state-of-the-art models. Our approach highlights the potential of lightweight RGB-only depth estimation models for real-world deployment in robotics and scene understanding. Full article

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26 pages, 17668 KB

Open AccessArticle

Real-Time Damage Detection and Localization on Aerospace Structures Using Graph Neural Networks

by Emiliano Del Priore and Luca Lampani

J. Sens. Actuator Netw. 2025, 14(5), 89; https://doi.org/10.3390/jsan14050089 - 29 Aug 2025

Abstract

This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the [...] Read more.

This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the edges capture spatial and structural relationships among sensors. Strain mode shapes, extracted via Automated Operational Modal Analysis (AOMA), are used as input features for the GNN. Two architectures are developed: one for binary damage detection and another for damage localization, the latter outputting a spatial probability distribution of damage over the structure. Both networks are trained and validated on synthetic datasets generated from high-fidelity finite element transient simulations performed on a composite wing equipped with 40 strain sensors. The obtained results show strong effectiveness in both detection and localization tasks, thus highlighting the potential of leveraging GNNs for topology-aware Structural Health Monitoring applications. In particular, the proposed framework achieves an AUC of 0.97 for damage detection and a mean localization error of approximately 3% of the wingspan on the synthetic dataset. The performance of the GNN is also compared with a fully connected and a convolutional neural network, demonstrating significant improvements in the localization accuracy. Full article

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

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Journal of Sensor and Actuator Networks

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