Search Results (782)

This paper investigates backscatter-aided relaying for interactive dual-HAP wireless-powered sensor networks (WPSNs), in which two cooperative sensor groups transmit sensed data to opposite hybrid access points (HAPs) using harvested radio-frequency energy. Each group consists of multiple source sensor nodes (SNs) and one relay SN selected according to its proximity to the target HAP. To reduce local cooperation overhead, source SNs reuse the wireless power transfer (WPT) signal as a controllable carrier and convey their information to the relay SN through passive backscatter communication. The collected information is then delivered to the target HAPs through direct source transmission and relay forwarding. A source common-throughput maximization problem is formulated by jointly optimizing time allocation, transmit energy allocation, and dual-HAP energy beamforming, subject to energy-causality and relay minimum-rate constraints. To address the resulting non-convexity, an alternating optimization algorithm is developed, where the time-and-energy allocation subproblem is transformed into a convex form and the energy beamforming matrices are updated through energy-feasibility margin maximization. Numerical results show that the proposed scheme outperforms active cooperation without backscatter and direct transmission, demonstrating the effectiveness of integrating passive local information collection, relay-assisted uplink transmission, and optimized dual-HAP WPT. Full article

Introduction: Biological invasions are a major driver of biodiversity loss, particularly in freshwater ecosystems. The Iberian Peninsula, a hotspot of endemic diversity, is increasingly threatened by invasive predatory fish, which may exert higher predatory rates under warmer environmental conditions, disrupting/endangering native fish communities. One such species is the European catfish (Silurus glanis), a large and voracious apex predator. Despite growing research, most telemetry studies have focused on lentic systems, limiting our understanding of its behaviour in lotic environments. Moreover, high-resolution biologging approaches remain largely unexplored. Objective: This study aims to characterize the habitat use and activity patterns of European catfish in a non-native lotic section of the lower Tagus River, and to identify key environmental drivers shaping its predatory behaviour. Methodology: Adult individuals were tagged with radio telemetry transmitters equipped with temperature, pressure (depth), and 3D-accelerometer archival sensors. A preliminary controlled experiment established activity thresholds to classify behaviours. Ten adult fish were then actively tracked over one year, combining spatial data with high-resolution biologging. Habitat use and activity patterns were analyzed across seasonal and circadian scales. Generalized Additive Models (GAMs) were used to assess the effects of environmental variables on activity levels and depth use, while Hurdle models were applied to identify the environmental drivers influencing the occurrence and frequency of burst activity events (predatory behaviour proxies). Results: Fish displayed strong site fidelity, frequently using structured habitats near riverbanks. European catfish also showed clear seasonal and circadian patterns in habitat use and activity, occupying deeper habitats in winter and shallower areas in warmer seasons. Activity occurred year-round, increasing in spring and summer and peaking at dusk, being influenced by temperature, river flow, season, and time of day. Burst activity occurred more often in spring and at dusk. Conclusions: This study unveils insights on European catfish behaviour in invaded lotic systems, highlighting consistent patterns linked to environmental conditions. These findings can support more targeted and effective management strategies for controlling this invasive species. Full article

The growing expansion of the Internet of Things and wireless sensor networks has created an urgent demand for compact and reliable radio frequency energy-harvesting circuits. This study introduces the design, simulation and extensive performance of a high-efficiency single band radio frequency detection system optimized for 1.8 GHz operation. The detector is realized on a Rogers RO4003C substrate and employs the SMS7630-079LF Schottky diode, selected for its excellent detection capability and economic viability. The introduction of this filtering stage effectively suppresses undesired harmonic components produced during the rectification process, thereby improving the sensitivity and overall power conversion efficiency of the system. The circuit shows a sensitivity of 1.8 mV for every dBm through its simulation tests. The system shows increased sensitivity to 2.2 mV/dBm because of the band stop filter implementation. The system reaches its peak power conversion efficiency of 65.28% at a 1.5 kΩ load, which makes it suitable for applications that require low-power energy harvesting. These combined attributes establish the developed 1.8 GHz detector as a strong candidate for next-generation energy harvesting modules, self-powered sensor networks and intelligent embedded computing platforms within the expanding domain of the Internet of Things. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture. Full article

Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy without explicitly linking statistical error metrics to system-level design parameters, thus limiting their practical interpretability in deployment scenarios. This work presents an extensive comparative evaluation among well-known propagation models versus machine learning regressors, and a lightweight convolutional neural network (CNN) for path loss prediction, using transmitter–receiver distance and carrier frequency as input features. A pairwise communication model is adopted to ensure consistent analysis across heterogeneous environments while preserving physical interpretability of the propagation process. Building upon this evaluation, a unified analytical framework is proposed that correlates path loss (PL) prediction accuracy to system-level metrics relevant to WSN–IoT design. Moreover, in this work we apply the Root Mean Square Error (RMSE) of the best-performing model as an empirical estimate of the shadowing standard deviation, under standard statistical assumptions, thereby allowing its direct use in link budget and fade margin calculations. Extensive experimental results across five heterogeneous wireless link datasets demonstrate that improved prediction accuracy leads to reduced transmission power requirements, lower energy consumption, enhanced communication reliability, and extended node lifetime. Full article

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned design performs on-demand data acquisition, logs measurements in the FRAM, and communicates the measured results through an ISO15693 compliant NFC link while powered entirely by the reader’s radio-frequency (RF) field. Since NFC provides only limited harvested power, efficient energy management is critical. The proposed scheme continuously monitors the storage capacitor voltage and activates each sensor only when sufficient energy is available. After every measurement, the system reassesses the stored charge before triggering the next acquisition, ensuring stable multi-sensor operation. A BMP390 temperature and pressure sensor and the on-chip temperature sensor demonstrate the platform’s capability. Experimental results show that the system harvests 1.064 mW (1.85 V, 560 µA), achieves a wireless operating range of up to 40 mm, and delivers a response time of 800 ms, demonstrating its suitability for low-power temperature and pressure sensing applications. Full article

An IoT-based technique is suggested for detecting single-phase earth faults (SEFs) in 6–35 kV cable networks with an isolated neutral. Unlike existing methods based on measuring zero-sequence currents with traditional current transformers, the suggested technique uses a passive magnetically controlled contact (reed switch) placed in the magnetic field of a cable. This enables recording fault currents of 0.5–2.0 A without external power supply and ensures galvanic isolation. The novelty of this technique is the combination of a reed switch current sensor with an IoT platform: instantaneous values of current are measured by the duration of the closed state of the contacts, then the data are transmitted via a radio channel (LoRa 433 MHz, LoRaWAN, or NB-IoT) to a cloud-based SCADA/EMS system for remote monitoring. The amplitude of the current is calculated from the pickup and resetting currents, as well as the duration of the closed state of the contacts; no high-frequency ADC is required. During experimental tests of a prototype with a KEM-5 reed switch and a TZL-10 current transformer, the difference between the calculated and actual protection operation current was no more than 10–5%. Oscillograms confirmed the correct operation of the device when starting, under load, and during an artificial SEF with a current of 1.6 A. The device response time is a fraction of the industrial frequency period, which significantly reduces the emergency mode duration. The suggested system enables decreasing the system average interruption duration index (SAIDI) and the system average interruption frequency index (SAIFI) by selectively disconnecting a damaged section and preventing cascading faults. The use of two independent channels (current transformer and reed switch) increases the reliability of SEF detection and reduces the risk of false operation. Thus, the developed IoT-based technique improves the reliability, safety, and cost-effectiveness of cable network operation. Full article

Partial discharge (PD) detection effectively identifies insulation defects in power equipment. Radio frequency (RF) methods for PD detection offer promising advantages due to their non-invasive measurement capability and ability to locate discharge sources. However, microstrip antennas used as RF sensors for PD detection suffer from narrow bandwidth and limited installation flexibility. To address these limitations, this paper presents a novel flexible microstrip antenna design. By incorporating a partial ground plane and oblique-cut meandering techniques and optimizing the structural parameters using an improved whale optimization algorithm (I-WOA), the operating bandwidth is expanded from 0.612–0.625 GHz to 0.346–2.0 GHz, while the overall size is reduced to 75.3% of its original dimensions. The antenna’s performance was validated through GTEM cell measurements and PD calibration pulse tests, confirming its suitability for RF detection of PD in power equipment such as transformers and cable joints. Notably, when the antenna was conformally wrapped around a cable joint, the response amplitude increased by 14%. This study contributes to the development of a low-cost, broadband, and flexibly installable RF sensor for partial discharge detection. Full article

Optical fiber sensors deployed in harsh industrial fields, e.g., high-temperature wet-oxygen, face severe challenges in signal attenuation and mechanical degradation. While amorphous silicon oxycarbide (a-SiOC) coatings offer a promising solution due to their adjustable thermo-mechanical properties, balancing their structural density with environmental stability remains a critical technical bottleneck. In this study, a-SiOC coatings were deposited on optical fibers using hexamethyldisilane (HMDS) and trace oxygen via radio-frequency capacitively coupled plasma-enhanced chemical vapor deposition (PECVD). A systematic investigation was conducted to determine the impact of deposition temperature (70–420 °C) on the precursor dissociation kinetics, microstructural evolution, and corrosion resistance of the coatings. An elevation in temperature promotes the elimination of organic terminal groups (–CH₃, –H) and enhances surface diffusion, driving the coating from a loose, carbon-rich “polymer-like” structure (dominated by Si–C bonds) to a dense, inorganic “silica-like” skeleton (dominated by Si–O–Si bonds). High-temperature corrosion tests in a wet-oxygen environment (500–900 °C) demonstrate that the failure mechanism is highly dependent on deposition temperature. Coatings deposited at low temperatures suffer catastrophic cracking due to pronounced oxidative shrinkage and the release of volatile species, whereas coatings deposited at 420 °C exhibit microcracking caused by severe carbon phase separation and stress concentration within the rigid inorganic network. In the present system, 350 °C is identified as the optimal deposition temperature, as it achieves the best balance of network densification and structural flexibility, while exhibiting the best mechanical performance. Full article

This study investigates the dielectric behavior and NO_x storage properties of Pt/Ba–Al₂O₃ NO_x storage materials using microwave cavity perturbation, operando DRIFTS, and impedance spectroscopy with respect to their applicability in a radio-frequency-based NO_x dosimeter-type sensor. Dielectric losses (ε″) are identified as the most sensitive indicator of NO_x storage, exhibiting a clear linear correlation with both the accumulated NO_x dose and the utilization of Ba storage sites. Approximately 35% of the available Ba sites participate in nitrite and nitrate formation, and the absolute dielectric loss response increases proportionally with the Ba content of the NOx storage catalyst. In contrast, the permittivity (ε′) shows only minor changes, which are mainly influenced by temperature. Temperature-dependent experiments reveal stable NO_x storage with negligible desorption up to 350 °C, whereas pronounced desorption processes at 400 °C significantly limit the linear dosimeter behavior. Operando DRIFTS measurements on Pt/Ba–Al₂O₃ functional films confirm temperature-dependent formation of nitrites and nitrates, with nitrates dominating the NO_x storage at elevated temperatures. Capacitance measurements show a slight increase during NO_x storage, indicating a moderate increase in permittivity. Overall, Pt/Ba–Al₂O₃ NO_x storage materials exhibit a robust, quantitatively interpretable dielectric response that is well suited for radio-frequency-based, dosimeter-type NO_x sensing. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

The evolution toward sixth-generation (6G) networks envisions humans as active nodes within a fully interconnected digital ecosystem, supported by data collected from in-body and on-body sensors. Since many of these devices are not equipped to connect directly to 6G networks, Wireless Body Area Networks (WBANs) serve as an essential intermediate layer. However, conventional radio-frequency technologies face limitations in terms of energy efficiency, security, and data integrity, motivating the adoption of lightweight security mechanisms. Physical Layer Security (PLS), and in particular Physical Key Extraction (PKE), offers a promising solution by enabling legitimate devices to derive shared cryptographic keys from the reciprocal properties of the communication channel. Galvanic coupling (GC) communication has recently emerged as an on-body transmission technology alternative to radio-frequency (RF), which exploits low-power electrical signals propagating through biological tissue. Building on prior feasibility studies, this work proposes a PKE framework tailored to GC channels, integrating a lightweight key reconciliation method, based on Hamming (7,4) error-correction codes, and evaluating system performance through dedicated reliability and security Key Performance Indicators (KPIs). Results reveal a trade-off shaped by electrode placement and channel quantization parameters. Among the ones tested, the optimal configuration is achieved with a 3 cm transverse inter-electrode spacing at both transmitter and receiver, and a 3 cm longitudinal separation between transmitter and receiver, by quantizing the channel impulse response with two quantization bits. While this work focuses on validating the method in controlled conditions in order to establish a reliable study framework, future developments will focus on enhanced reconciliation, privacy amplification, and analysis of the GC channel considering physiological and environmental variations. Full article

This paper presents a novel method for integrating multiple filters into a single board that can be reconfigured through design modifications. The primary objective is to introduce a scalable three-in-one filter, referred to as a triplexer, suitable for diverse applications. The proposed filter is well-suited to applications such as multi-band RF front ends, software-defined radios (SDRs), test instrumentation requiring selectable responses, and compact wireless sensor nodes. The manuscript develops a mathematical model for each filter, enabling adjustment of the cutoff frequency to different values. The model is then expanded to capture the interactions among the three filters and is validated in MATLAB. An experimental four-port filter sample is fabricated to validate the concept. It comprises a $2.85$ GHz low-pass filter (LPF), a $5.10$ GHz band-pass filter (BPF), and a $6.30$ GHz high-pass filter (HPF). The proposed triplexer is designed using step impedance and coupled lines, providing a systematic design approach suitable for various applications due to its adaptability and straightforward structure. The methodology includes calculations in MATLAB, full-wave EM simulation, fabrication on RT/Duroid 5880, and measurements with a four-port network analyzer. The measured results show strong agreement with both calculated and simulated results. Full article