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Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments
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Edge Computing-Aided Dynamic Wireless Charging and Trip Planning of UAVs
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Interdigitated Gear-Shaped Screen-Printed Electrode Using G-PANI Ink for Sensitive Electrochemical Detection of Dopamine
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 19 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in the second half of 2024).
- 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:
3.3 (2023);
5-Year Impact Factor:
3.2 (2023)
Latest Articles
Heterogeneity Challenges of Federated Learning for Future Wireless Communication Networks
J. Sens. Actuator Netw. 2025, 14(2), 37; https://doi.org/10.3390/jsan14020037 - 1 Apr 2025
Abstract
Two technologies of great interest in recent years—Artificial Intelligence (AI) and massive wireless communication networks—have found a significant point of convergence through Federated Learning (FL). Federated Learning is a Machine Learning (ML) technique that enables multiple participants to collaboratively train a model while
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Two technologies of great interest in recent years—Artificial Intelligence (AI) and massive wireless communication networks—have found a significant point of convergence through Federated Learning (FL). Federated Learning is a Machine Learning (ML) technique that enables multiple participants to collaboratively train a model while keeping their data local. Several studies indicate that while improving performance metrics—such as accuracy, loss reduction, or computation time—is a primary goal, achieving this in real-world scenarios remains challenging. This difficulty arises due to various heterogeneity characteristics inherent to the wireless devices participating in the Federation. Heterogeneity in Federated Learning arises when participants contribute differently, leading to challenges in the model training process. Heterogeneity in Federated Learning may appear in architecture, statistics, and behavior. System heterogeneity arises from differences in device capabilities, including processing power, transmission speeds, availability, energy constraints, and network limitations, among others. Statistical heterogeneity occurs when participants contribute non-independent and non-identically distributed (non-IID) data. This situation can harm the global model instead of improving it, especially when the data are of poor quality or too scarce. The third type, behavioral heterogeneity, refers to cases where participants are unwilling to engage or expect rewards despite minimal effort. Given the growing research in this area, we present a summary of heterogeneity characteristics in Federated Learning to provide a broader perspective on this emerging technology. We also outline key challenges, opportunities, and future directions for Federated Learning. Finally, we conduct a simulation using the LEAF framework to illustrate the impact of heterogeneity in Federated Learning.
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(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)
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Open AccessArticle
Lower-Complexity Multi-Layered Security Partitioning Algorithm Based on Chaos Mapping-DWT Transform for WA/SNs
by
Tarek Srour, Mohsen A. M. El-Bendary, Mostafa Eltokhy, Atef E. Abouelazm, Ahmed A. F. Youssef and Ali M. El-Rifaie
J. Sens. Actuator Netw. 2025, 14(2), 36; https://doi.org/10.3390/jsan14020036 - 31 Mar 2025
Abstract
The resource limitations of Low-Power Wireless Networks (LP-WNs), such as Wireless Sensor Networks (WSNs), Wireless Actuator/Sensor Networks (WA/SNs), and Internet of Things (IoT) outdoor applications, restrict the utilization of the error-performance-enhancing techniques and the use of the powerful and robust security tools. Therefore,
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The resource limitations of Low-Power Wireless Networks (LP-WNs), such as Wireless Sensor Networks (WSNs), Wireless Actuator/Sensor Networks (WA/SNs), and Internet of Things (IoT) outdoor applications, restrict the utilization of the error-performance-enhancing techniques and the use of the powerful and robust security tools. Therefore, these LP-WN applications require special techniques to satisfy the requirements of a low data loss rate and satisfy the security requirements while considering the accepted level of complexity and power efficiency of these techniques. This paper focuses on proposing a power-efficient, robust cryptographic algorithm for the WA/SNs. The lower-complexity cryptographic algorithm is proposed, based on merging the data composition tools utilizing data transforms and chaos mapping techniques. The decomposing tool is performed by the various data transforms: Discrete Cosine Transform (DCT), Discrete Cosine Wavelet (DWT), Fast Fourier Transform (FFT), and Walsh Hadamard Transform (WHT); the DWT performs better with efficient complexity. It is utilized to separate the plaintext into the main portion and side information portions to reduce more than 50% of complexity. The main plaintext portion is ciphered in the series of cryptography to reduce the complexity and increase the security capabilities of the proposed algorithm by two chaos mappings. The process of reduction saves complexity and is employed to feed the series of chaos cryptography without increasing the complexity. The two chaos mappings are used, and two-dimensional (2D) chaos logistic maps are used due to their high sensitivity to noise and attacks. The chaos 2D baker map is utilized due to its high secret key managing flexibility and high sensitivity to initial conditions and plaintext dimensions. Several computer experiments are demonstrated to evaluate the robustness, reliability, and applicability of the proposed complexity-efficient crypto-system algorithm in the presence of various attacks. The results prove the high suitability of the proposed lower-complexity crypto-system for WASN and LP-WN applications due to its robustness in the presence of attacks and its power efficiency.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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Open AccessArticle
A Zero-Trust Multi-Processor Reporter-Verifier Design of Edge Devices for Firmware Authenticity in Internet of Things and Blockchain Applications
by
Ananda Maiti and Alexander A. Kist
J. Sens. Actuator Netw. 2025, 14(2), 35; https://doi.org/10.3390/jsan14020035 - 31 Mar 2025
Abstract
Firmware authenticity and integrity during upgrades are critical security factors in Internet of Things (IoT) applications in the age of edge artificial intelligence (AI). Data from IoT applications are vital for business decisions. Any unintended or malicious change in data can adversely impact
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Firmware authenticity and integrity during upgrades are critical security factors in Internet of Things (IoT) applications in the age of edge artificial intelligence (AI). Data from IoT applications are vital for business decisions. Any unintended or malicious change in data can adversely impact the goals of an IoT application. Several studies have focused on using blockchain to ensure the authentication of IoT devices and the integrity of data once the data are in the blockchain. Firmware upgrades on IoT edge devices have also been investigated with blockchain applications, with a focus on eliminating external threats during firmware upgrades on IoT devices. In this paper, we propose a new IoT device design that works against internal threats by preventing malicious codes from device manufacturers. In IoT applications that monitor critical data, it is important to ensure that the correct firmware reporting honest data is running on the devices. As devices are owned and operated by a small group of application stakeholders, this multiprocessor design extracts the firmware periodically and checks whether it matches the signatures of the expected firmware designed for the business goals of the IoT applications. The test results show that there is no significant increase in code, disruption, or power consumption when implementing such a device. This scheme provides a hardware-oriented solution utilizing processor-to-processor communication protocols and is an alternative to running lightweight blockchain on IoT edge devices.
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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Sensor Fusion Enhances Anomaly Detection in a Flood Forecasting System
by
Andrew Ma, Abhir Karande, Natalie Dahlquist, Fabien Ferrero and N. Rich Nguyen
J. Sens. Actuator Netw. 2025, 14(2), 34; https://doi.org/10.3390/jsan14020034 - 25 Mar 2025
Abstract
To build an Internet of Things (IoT) infrastructure that provides flood susceptibility forecasts for granular geographic levels, an extensive network of IoT weather sensors in local regions is crucial. However, these IoT devices may exhibit anomalistic behavior due to factors such as diminished
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To build an Internet of Things (IoT) infrastructure that provides flood susceptibility forecasts for granular geographic levels, an extensive network of IoT weather sensors in local regions is crucial. However, these IoT devices may exhibit anomalistic behavior due to factors such as diminished signal strength, physical disturbance, low battery life, and more. To ensure that incorrect readings are identified and addressed appropriately, we devise a novel method for multi-stream sensor data verification and anomaly detection. Our method uses time-series anomaly detection to identify incorrect readings. We expand on the state-of-the-art by incorporating sensor fusion mechanisms between nearby devices to improve anomaly detection ability. Our system pairs nearby devices and fuses them by creating a new time series with the difference between the corresponding readings. This new time series is then input into a time-series anomaly detection model which identifies if any readings are anomalistic. By testing our system with nine different machine learning anomaly detection methods on synthetic data based on one year of real weather data, we find that our system outperforms the previous anomaly detection methods by improving F1-Score by 10.8%.
Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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Open AccessArticle
Environmental Factors in Structural Health Monitoring—Analysis and Removal of Effects from Resonance Frequencies
by
Rims Janeliukstis, Lasma Ratnika, Liga Gaile and Sandris Rucevskis
J. Sens. Actuator Netw. 2025, 14(2), 33; https://doi.org/10.3390/jsan14020033 - 20 Mar 2025
Abstract
Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology
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Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology that is applicable to the long-term monitoring of such structures. It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. The novelty lies in two additional steps that supplement this procedure, namely, the nonlinear estimation of the relative effects of various environmental factors, such as temperature, humidity, and ambient loads on the resonant frequencies, and the selection of the most informative resonant frequency features using a non-parametric neighborhood component analysis algorithm. This methodology is validated on a wooden two-story truss structure with different artificial structural modifications that simulate damage in a non-destructive manner. It is found that, firstly, out of all environmental factors, temperature has a dominating decreasing effect on resonance frequencies, followed by humidity, wind speed, and precipitation. Secondly, the selection of only a handful of the most informative resonance frequency features not only reduces the feature space, but also increases the classification performance, albeit with a trade-off between false alarms and missed damage detection. The proposed approach effectively minimizes false alarms and ensures consistent damage detection under varying environmental conditions, offering tangible benefits for long-term SHM applications.
Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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Robust Distributed Collaborative Beamforming for WSANs in Dual-Hop Scattered Environments with Nominally Rectangular Layouts
by
Oussama Ben Smida, Sofiène Affes, Dushantha Jayakody and Yoosuf Nizam
J. Sens. Actuator Netw. 2025, 14(2), 32; https://doi.org/10.3390/jsan14020032 - 19 Mar 2025
Abstract
We introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load
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We introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X and Y —refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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Optimizing Sensor Locations for Electrodermal Activity Monitoring Using a Wearable Belt System
by
Riley Q. McNaboe, Youngsun Kong, Wendy A. Henderson, Xiaomei Cong, Aolan Li, Min-Hee Seo, Ming-Hui Chen, Bin Feng and Hugo F. Posada-Quintero
J. Sens. Actuator Netw. 2025, 14(2), 31; https://doi.org/10.3390/jsan14020031 - 18 Mar 2025
Abstract
Wearable devices for continuous health monitoring in humans are constantly evolving, yet the signal quality may be improved by optimizing electrode placement. While the commonly used locations to measure electrodermal activity (EDA) are at the fingers or the wrist, alternative locations, such as
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Wearable devices for continuous health monitoring in humans are constantly evolving, yet the signal quality may be improved by optimizing electrode placement. While the commonly used locations to measure electrodermal activity (EDA) are at the fingers or the wrist, alternative locations, such as the torso, need to be considered when applying an integrated multimodal approach of concurrently recording multiple bio-signals, such as the monitoring of visceral pain symptoms like those related to irritable bowel syndrome (IBS). This study aims to quantitatively determine the EDA signal quality at four torso locations (mid-chest, upper abdomen, lower back, and mid-back) in comparison to EDA signals recorded from the fingers. Concurrent EDA signals from five body locations were collected from twenty healthy participants as they completed a Stroop Task and a Cold Pressor task that elicited salient autonomic responses. Mean skin conductance (meanSCL), non-specific skin conductance responses (NS.SCRs), and sympathetic response (TVSymp) were derived from the torso EDA signals and compared with signals from the fingers. Notably, TVSymp recorded from the mid-chest location showed significant changes between baseline and Stroop phase, consistent with the TVSymp recorded from the fingers. A high correlation (0.77–0.83) was also identified between TVSymp recorded from the fingers and three torso locations: mid-chest, upper abdomen, and lower back locations. While the fingertips remain the optimal site for EDA measurement, the mid-chest exhibited the strongest potential as an alternative recording site, with the upper abdomen and lower back also demonstrating promising results. These findings suggest that torso-based EDA measurements have the potential to provide reliable measurement of sympathetic neural activities and may be incorporated into a wearable belt system for multimodal monitoring.
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(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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Key Enabling Technologies for 6G: The Role of UAVs, Terahertz Communication, and Intelligent Reconfigurable Surfaces in Shaping the Future of Wireless Networks
by
Wagdy M. Othman, Abdelhamied A. Ateya, Mohamed E. Nasr, Ammar Muthanna, Mohammed ElAffendi, Andrey Koucheryavy and Azhar A. Hamdi
J. Sens. Actuator Netw. 2025, 14(2), 30; https://doi.org/10.3390/jsan14020030 - 17 Mar 2025
Abstract
Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability.
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Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability. Among them, unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are three major enablers in redefining the architecture and performance of next-generation wireless systems. This survey provides a comprehensive review of these transformative technologies, exploring their potential, design challenges, and integration into future 6G ecosystems. UAV-based communication provides flexible, on-demand communication in remote, harsh areas and is a vital solution for disasters, self-driving, and industrial automation. THz communication taking place in the 0.1–10 THz band reveals ultra-high bandwidth capable of a data rate of multi-gigabits per second and can avoid spectrum bottlenecks in conventional bands. IRS technology based on programmable metasurface allows real-time wavefront control, maximizing signal propagation and spectral/energy efficiency in complex settings. The work provides architectural evolution, active current research trends, and practical issues in applying these technologies, including their potential contribution to the creation of intelligent, ultra-connected 6G networks. In addition, it presents open research questions, possible answers, and future directions and provides information for academia, industry, and policymakers.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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Open AccessArticle
Comprehensive Exploration of Limitations of Simplified Machine Learning Algorithm for Fault Diagnosis Under Fault and Ground Resistances of Multiterminal High-Voltage Direct Current System
by
Raheel Muzzammel
J. Sens. Actuator Netw. 2025, 14(2), 29; https://doi.org/10.3390/jsan14020029 - 17 Mar 2025
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High power density and better efficiency make the multiterminal high-voltage direct current (MT-HVDC) system the best candidate for long-distance bulk power transfer in the cases of onshore and offshore power systems. Many machine learning-based algorithms have been developed for the protection of MT-HVDC
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High power density and better efficiency make the multiterminal high-voltage direct current (MT-HVDC) system the best candidate for long-distance bulk power transfer in the cases of onshore and offshore power systems. Many machine learning-based algorithms have been developed for the protection of MT-HVDC systems. However, the exploration of the effects of change in the fault and ground resistances of MT-HVDC systems has not been studied comprehensively. In this study, a four-terminal HVDC test system is employed for the analysis of the effects on fault diagnosis under change in the fault and ground resistances. A simplified medium tree-based machine learning algorithm that works on Gini’s index of diversity is developed for fault diagnosis in the MT-HVDC system. It is found from the simulation analysis that the preprocessing based on mean and differences in featured data extracted for fault current is required to reduce the impacts of the accuracy of machine learning algorithms. The preprocessing not only retains the accuracy of the machine learning algorithm in different cases of faults, but also minimizes the reduction in accuracy in some fault cases. In the test cases, the accuracy is 88.7%, 60%, and 57.1% without preprocessing of featured data for the machine learning algorithm under different values of fault and ground resistances, but the accuracy is improved to 99.5%, 84.1%, and 77.8%, respectively. Hence, the machine learning algorithm can be made applicable under different values of fault and ground resistances for the protection of the MT-HVDC system. This helps to develop a protected MT-HVDC system for long distances without the fear of different soil conditions.
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Open AccessReview
Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study
by
Lekshmi R. Chandran, Ilango Karuppasamy, Manjula G. Nair, Hongjian Sun and Parvathy Krishnan Krishnakumari
J. Sens. Actuator Netw. 2025, 14(2), 28; https://doi.org/10.3390/jsan14020028 - 7 Mar 2025
Abstract
Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing matrices, sparse bases, and
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Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing matrices, sparse bases, and recovery algorithms, with a focus on its applications in power engineering. CS has demonstrated significant potential in enhancing key areas such as state estimation (SE), fault detection, fault localization, outage identification, harmonic source identification (HSI), Power Quality Detection condition monitoring, and so on. Furthermore, CS addresses challenges in data compression, real-time grid monitoring, and efficient resource utilization. A case study on smart meter data recovery demonstrates the practical application of CS in real-world power systems. By bridging CS theory and its application, this survey underscores its potential to drive innovation, efficiency, and sustainability in power engineering and beyond.
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(This article belongs to the Section Wireless Control Networks)
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Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
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Charbel Mattar, Jacques Bou Abdo, Jacques Demerjian and Abdallah Makhoul
J. Sens. Actuator Netw. 2025, 14(2), 27; https://doi.org/10.3390/jsan14020027 - 4 Mar 2025
Cited by 1
Abstract
Network diffusion algorithms and simulators play a critical role in understanding how information, data, and malware propagate across various network topologies in Internet of Things and Space IoT configurations. This paper conducts a systematic literature review (SLR) of the key diffusion algorithms and
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Network diffusion algorithms and simulators play a critical role in understanding how information, data, and malware propagate across various network topologies in Internet of Things and Space IoT configurations. This paper conducts a systematic literature review (SLR) of the key diffusion algorithms and network simulators utilized in studies over the past decade. The review focuses on identifying the algorithms and simulators employed, their strengths and limitations, and how their performance is evaluated under different IoT network topologies. Common network simulators, such as NS-3, Cooja, and OMNeT++ are explored, highlighting their features, scalability, and suitability for different IoT network scenarios. Additionally, network diffusion algorithms, including epidemic, cascading, and threshold models, are analyzed in terms of their effectiveness, complexity, and applicability in IoT environments with diverse network topologies. This SLR aims to provide a comprehensive reference for researchers and practitioners when selecting appropriate tools and methods for simulating and analyzing network diffusion across IoT and Space IoT configurations.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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A Proactive Charging Approach for Extending the Lifetime of Sensor Nodes in Wireless Rechargeable Sensor Networks
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Omar Banimelhem and Shifa’a Bani Hamad
J. Sens. Actuator Netw. 2025, 14(2), 26; https://doi.org/10.3390/jsan14020026 - 3 Mar 2025
Abstract
Although wireless sensor networks (WSNs) have a wide range of applications, their efficient utilization is still limited by the sensor node battery life. To overcome this issue, wireless power transfer technology (WPT) has recently been used to wirelessly charge sensor nodes and extend
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Although wireless sensor networks (WSNs) have a wide range of applications, their efficient utilization is still limited by the sensor node battery life. To overcome this issue, wireless power transfer technology (WPT) has recently been used to wirelessly charge sensor nodes and extend their lifespan. This technique paved the way to develop a wireless rechargeable sensor network (WRSN) in which a mobile charger (MC) is employed to recharge the sensor nodes. Several wireless charging technologies have been proposed in this field, but they are all tied up in two classes: periodic and on-demand strategies. This paper proposes a proactive charging method as a new charging strategy that anticipates the node’s need for energy in advance based on factors such as the node’s remaining energy, energy consumption rate, and the distance to the MC. The goal is to prevent sensor nodes from depleting their energy before the arrival of the MC. Unlike conventional methods where nodes have to request energy, the proactive charging strategy identifies the nodes that need energy before they reach a critical state. Simulation results have demonstrated that the proactive charging approach using a single MC can significantly improve the network lifespan by 500% and outperform the Nearest Job Next with Preemption (NJNP) and First Come First Serve (FCFS) techniques in terms of the number of survival nodes by 300% and 650%, respectively.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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Monitoring Daily Activities in Households by Means of Energy Consumption Measurements from Smart Meters
by
Álvaro Hernández, Rubén Nieto, Laura de Diego-Otón, José M. Villadangos-Carrizo, Daniel Pizarro, David Fuentes and María C. Pérez-Rubio
J. Sens. Actuator Netw. 2025, 14(2), 25; https://doi.org/10.3390/jsan14020025 - 27 Feb 2025
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Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building,
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Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE.
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Open AccessArticle
Making IoT Networks Highly Fault-Tolerant Through Power Fault Prediction, Isolation and Composite Networking in the Device Layer
by
Kodanda Rama Sastry Jammalamadaka, Bhupati Chokara, Sasi Bhanu Jammalamadaka and Balakrishna Kamesh Duvvuri
J. Sens. Actuator Netw. 2025, 14(2), 24; https://doi.org/10.3390/jsan14020024 - 25 Feb 2025
Abstract
High availability of the IoT network is challenging as the networks are prone to failures due to various faults occurring within the different layers of the IoT networks. Most of the failures in the device layer are due to furious signals created by
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High availability of the IoT network is challenging as the networks are prone to failures due to various faults occurring within the different layers of the IoT networks. Most of the failures in the device layer are due to furious signals created by the devices when the power left in the devices approaches the threshold level. Another frequent problem in this layer is communication failures due to a lack of fully functional communication paths. The fault tolerance of the IoT network gets depleted due to these failures.This paper introduces a novel method for predicting power fault occurrence and isolating such devices in the device layer. It also demonstrates the implementation of device clusters using different networking topologies, significantly enhancing the fault tolerance of IoT networks by providing multiple alternate communication paths. The proposed method has shown a remarkable improvement in the success rate (21 Percent), significantly increased the longevity of the IoT network (61 percent), and drastically reduced the false alarm rate (77 percent). It has also enhanced accuracy (1 Percent) compared to the nearest available models, demonstrating its effectiveness.
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(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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Open AccessReview
Application of Wearable Sensors in Parkinson’s Disease: State of the Art
by
Anastasia Bougea
J. Sens. Actuator Netw. 2025, 14(2), 23; https://doi.org/10.3390/jsan14020023 - 20 Feb 2025
Cited by 1
Abstract
(1) Background: Wearable sensors have emerged as a promising technology in the management of Parkinson’s disease (PD). These sensors can provide continuous and real-time monitoring of various motor and non-motor symptoms of PD, allowing for early detection and intervention. In this paper, I
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(1) Background: Wearable sensors have emerged as a promising technology in the management of Parkinson’s disease (PD). These sensors can provide continuous and real-time monitoring of various motor and non-motor symptoms of PD, allowing for early detection and intervention. In this paper, I review current research on the application of wearable sensors in PD, focusing on gait, tremor, bradykinesia, and dyskinesia monitoring.(2) Methods: this involved a literature search that spanned the 2000–2024 period and included the following keywords: “wearable sensors”, “Parkinson’s Disease”, “Inertial sensors”, “accelerometers’’, ‘’gyroscopes’’, ‘’magnetometers”, “Smartphones”, and “Smart homes”. (3) Results: Despite favorable outcomes from the early development of inertial sensors, like gyroscopes and accelerometers in smartphones, the application of wearable sensors is still restricted because there are no standards, harmonization, or consensus for both clinical and analytical validation. As a result, several clinical trials were created to compare the effectiveness of wearable sensors with conventional evaluation methods in order to track the course of the disease and enhance the quality of life and results. (4) Conclusions: wearable sensors hold great promise in the management of PD and are likely to play a significant role in future healthcare systems.
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(This article belongs to the Section Actuators, Sensors and Devices)
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Open AccessReview
Optimal Sensor Placement for Structural Health Monitoring: A Comprehensive Review
by
Zhiyan Sun, Mojtaba Mahmoodian, Amir Sidiq, Sanduni Jayasinghe, Farham Shahrivar and Sujeeva Setunge
J. Sens. Actuator Netw. 2025, 14(2), 22; https://doi.org/10.3390/jsan14020022 - 20 Feb 2025
Cited by 1
Abstract
The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable
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The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable alternative to traditional visual inspection methods. A key challenge in SHM is optimal sensor placement (OSP), which directly impacts monitoring accuracy, cost-efficiency, and overall system performance. This review explores recent advancements in SHM techniques, sensor technologies, and OSP methodologies, with a primary focus on bridge infrastructure. It evaluates sensor configuration strategies based on criteria such as the modal assurance criterion (MAC) and mean square error (MSE) while examining optimisation approaches like the Effective Independence (EI) method, Kinetic Energy Optimisation (KEO), and their advanced variants. Despite these advancements, several research gaps remain. Future studies should focus on scalable OSP strategies for large-scale bridge networks, integrating machine learning (ML) and artificial intelligence (AI) for adaptive sensor deployment. The implementation of digital twin (DT) technology in SHM can enhance predictive maintenance and real-time decision-making, improving long-term infrastructure resilience. Additionally, research on sensor robustness against environmental noise and external disturbances, as well as the integration of edge computing and wireless sensor networks (WSNs) for efficient data transmission, will be critical in advancing SHM applications. This review provides critical insights and recommendations to bridge the gap between theoretical innovations and real-world implementation, ensuring the effective monitoring and maintenance of bridge infrastructure in modern civil engineering.
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(This article belongs to the Section Actuators, Sensors and Devices)
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Open AccessArticle
Advanced Digital Solutions for Food Traceability: Enhancing Origin, Quality, and Safety Through NIRS, RFID, Blockchain, and IoT
by
Matyas Lukacs, Fruzsina Toth, Roland Horvath, Gyula Solymos, Boglárka Alpár, Peter Varga, Istvan Kertesz, Zoltan Gillay, Laszlo Baranyai, Jozsef Felfoldi, Quang D. Nguyen, Zoltan Kovacs and Laszlo Friedrich
J. Sens. Actuator Netw. 2025, 14(1), 21; https://doi.org/10.3390/jsan14010021 - 17 Feb 2025
Abstract
The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple
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The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple and affordable digital system that could be implemented in most production processes to improve transparency and productivity. The system combines non-destructive, rapid quality assessment methods, such as near infrared spectroscopy (NIRS) and computer/machine vision (CV/MV), with track and tracing functionalities revolving around the Internet of Things (IoT) and radio frequency identification (RFID). Meanwhile, authenticity is provided by a self-developed blockchain-based solution that validates all data and documentation “from farm to fork”. The system is introduced by taking certified Hungarian sweet potato production as a model scenario. Each element of the proposed system is discussed in detail individually and as a part of an integrated system, capable of automatizing most production flows while maintaining complete transparency and compliance with authority requirements. The results include the data and trust model of the system with sequence diagrams simulating the interactions between participants. The study lays the groundwork for future research and industrial applications combining digital tools to improve the productivity and authenticity of the agri-food industry, potentially increasing the level of trust between participants, most importantly for the consumers.
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(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)
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Open AccessArticle
Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems
by
Adeb Salh, Mohammed A. Alhartomi, Ghasan Ali Hussain, Chang Jing Jing, Nor Shahida M. Shah, Saeed Alzahrani, Ruwaybih Alsulami, Saad Alharbi, Ahmad Hakimi and Fares S. Almehmadi
J. Sens. Actuator Netw. 2025, 14(1), 20; https://doi.org/10.3390/jsan14010020 - 12 Feb 2025
Abstract
High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify
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High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks.
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(This article belongs to the Section Communications and Networking)
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Open AccessArticle
Non-Invasive Differential Temperature Monitoring Using Sensor Array for Microwave Hyperthermia Applications: A Subspace-Based Approach
by
Ji Wu, Fan Yang, Jinchuan Zheng, Hung T. Nguyen and Rifai Chai
J. Sens. Actuator Netw. 2025, 14(1), 19; https://doi.org/10.3390/jsan14010019 - 11 Feb 2025
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Non-invasive temperature monitoring is highly valuable in applications such as microwave hyperthermia treatment, where overheating may damage healthy tissue. This paper presents a subspace-based method for real-time temperature monitoring using a sensor array configuration. The proposed method improves upon the conventional Born approximation
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Non-invasive temperature monitoring is highly valuable in applications such as microwave hyperthermia treatment, where overheating may damage healthy tissue. This paper presents a subspace-based method for real-time temperature monitoring using a sensor array configuration. The proposed method improves upon the conventional Born approximation (BA) approach by accurately estimating the total field through primary induced currents. The temperature-dependent dielectric properties of breast tissues are modeled using data from porcine tissues, and a sigmoid function is employed to create realistic temperature transition zones in the numerical breast phantom. The method is validated through extensive simulations under noise-free and noisy conditions (SNR = 30 dB and 20 dB). The results demonstrate that our method maintains consistent performance across different temperature levels (38–45 °C), achieving reconstruction accuracy within ±0.2 °C at SNR = 30 dB and ±0.5 °C at SNR = 20 dB. While the computational overhead of calculating primary induced currents slightly increases the overall processing time, it leads to a faster convergence in the cost function minimization. These findings suggest that the proposed method offers a promising solution for real-time temperature monitoring in microwave hyperthermia applications.
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Open AccessArticle
Energy-Efficient and Secure Double RIS-Aided Wireless Sensor Networks: A QoS-Aware Fuzzy Deep Reinforcement Learning Approach
by
Sarvenaz Sadat Khatami, Mehrdad Shoeibi, Reza Salehi and Masoud Kaveh
J. Sens. Actuator Netw. 2025, 14(1), 18; https://doi.org/10.3390/jsan14010018 - 10 Feb 2025
Abstract
Wireless sensor networks (WSNs) are a cornerstone of modern Internet of Things (IoT) infrastructure, enabling seamless data collection and communication for many IoT applications. However, the deployment of WSNs in remote or inaccessible locations poses significant challenges in terms of energy efficiency and
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Wireless sensor networks (WSNs) are a cornerstone of modern Internet of Things (IoT) infrastructure, enabling seamless data collection and communication for many IoT applications. However, the deployment of WSNs in remote or inaccessible locations poses significant challenges in terms of energy efficiency and secure communication. Sensor nodes, with their limited battery capacities, require innovative strategies to minimize energy consumption while maintaining robust network performance. Additionally, ensuring secure data transmission is critical for safeguarding the integrity and confidentiality of IoT systems. Despite various advancements, existing methods often fail to strike an optimal balance between energy efficiency and quality of service (QoS), either depleting limited energy resources or compromising network performance. This paper introduces a novel framework that integrates double reconfigurable intelligent surfaces (RISs) into WSNs to enhance energy efficiency while ensuring secure communication. To jointly optimize both RIS phase shift matrices, we employ a fuzzy deep reinforcement learning (FDRL) framework that integrates reinforcement learning (RL) with fuzzy logic and long short-term memory (LSTM)-based architecture. The RL component learns optimal actions by iteratively interacting with the environment and updating Q-values based on a reward function that prioritizes both energy efficiency and secure communication. The LSTM captures temporal dependencies in the system state, allowing the model to make more informed predictions about future network conditions, while the fuzzy logic layer manages uncertainties by using optimized membership functions and rule-based inference. To explore the search space efficiently and identify optimal parameter configurations, we use the advantage of the multi-objective artificial bee colony (MOABC) algorithm as an optimization strategy to fine-tune the hyperparameters of the FDRL framework while simultaneously optimizing the membership functions of the fuzzy logic system to improve decision-making accuracy under uncertain conditions. The MOABC algorithm enhances convergence speed and ensures the adaptability of the proposed framework in dynamically changing environments. This framework dynamically adjusts the RIS phase shift matrices, ensuring robust adaptability under varying environmental conditions and maximizing energy efficiency and secure data throughput. Simulation results validate the effectiveness of the proposed FDRL-based double RIS framework under different system configurations, demonstrating significant improvements in energy efficiency and secrecy rate compared to existing methods. Specifically, quantitative analysis demonstrates that the FDRL framework improves energy efficiency by 35.4%, the secrecy rate by 29.7%, and RSMA by 27.5%, compared to the second-best approach. Additionally, the model achieves an R² score improvement of 12.3%, confirming its superior predictive accuracy.
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(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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