In this work, the authors introduce an entirely solar-powered LoRa-based WSN consisting of several nodes, two stoplights, and four cameras. The system has been used to monitor the semi-rural area of Panni (FG), Puglia, Italy. The WSN has a totally custom implementation in both the node-gateway side and the gateway-user interface side. In particular, the communication framework is entirely IoT-based, featuring both the MQTT protocol, for the direct control of apparatuses from the system user interface, and the more traditional TCP/IP protocol, implemented on NB-IoT. The proposed system is entirely solar-powered and features a 34.68 mWh/day consumption. Around a single communication session, the average power consumption inside the single node amounts to 1.4 mW. This paper gives an overview of the proposed system, with detailed explanations of each part, and measurements retrieved over a wide period to assess the functionality of the system.

This study focuses on identifying an optimised synchronisation word based on multiple practical criteria for wireless internet-of-things systems, where reliable and energy-efficient synchronisation is essential to extend battery life by reducing unnecessary receiver activations (wake-ups) and false detections. To address the limitations of purely theoretical designs, a weighted multi-criteria evaluation framework is presented specifically tailored for resource-constrained receivers (e.g., IQRF). A mathematical receiver model utilising a correlation detector operating in additive white Gaussian noise is formulated, and the effects of synchronisation-word selection on detectability and the rate of false detections are analytically determined for both fixed alignment and sliding-window search methods. The methodology is augmented by laboratory measurements conducted on a pair of parallel Texas Instruments receivers. The resulting data are compared with theoretical expectations, and both agreements and discrepancies are analysed in terms of their underlying causes and system-level implications. The results yield practical design recommendations for low-power wireless internet-of-things devices, including recommendations on bit balance, suppression of aperiodic autocorrelation sidelobes, robustness to cyclic shifts, and diminished cross-correlation with recurring traffic patterns, demonstrating that the proposed framework identifies synchronisation words that reduce false alarms by orders of magnitude compared to standard baselines.

The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in the long term, future wireless systems must fundamentally prioritize energy-efficient and autonomous operation. Integrated sensing and communication (ISAC) is emerging as a key enabler for next-generation systems by jointly supporting sensing and communication through shared spectrum, hardware, and signal processing resources. In IoT systems, sensing of target parameters, e.g., range, angle, velocity and identity, etc., form the basis of autonomous and environment-aware applications. However, this integration increases overall power consumption due to the added coordination overhead and the workload placed on shared hardware components. To this end, backscatter communication provides a low-power alternative that enables passive data transmission through energy harvesting and sharply reduces the need for active radio circuits. However, the coexistence of sensing and backscatter functions introduces mutual interference, which often requires large multiple-input multiple-output (MIMO) arrays for effective mitigation. Furthermore, sensing performance depends heavily on line-of-sight conditions, while backscatter links operate only over short ranges. Although increasing array size or transmit power can extend coverage, it imposes substantial energy and hardware costs and undermines sustainability goals. To address these limitations, cell-free MIMO is emerging as a promising candidate technology for next-generation systems. Cell-free MIMO relies on a dense deployment of distributed access points that cooperate to serve devices across a wide area. This cooperation enables effective beamforming and interference management, providing spatial diversity comparable to large, centralized antenna arrays without incurring their associated hardware or power costs. They also enable aggregation of weak double-hop reflections, reduced effective-illumination distances, multi-view sensing, and robustness to blockage, which is invaluable to backscatter communication. This perspective article introduces the foundations, challenges, and architectural considerations of cell-free backscatter-aided integrated sensing and communication (CF-BISAC) systems. By leveraging the advantages of battery-less backscatter IoT devices and the distributed nature of cell-free MIMO, CF-ISABC aims to maximize sensing and communication performance under strict energy constraints, contributing toward energy-aware ISAC systems capable of supporting high-density, low-power wireless applications.

The latest results of a research project pursuing an energy-autonomous Structural Health Monitoring (SHM) system for wind farms are presented. The SHM system is based on the development of low-power IoT wireless nodes and electromagnetic harvesters to capture energy from low-frequency vibrations of wind towers. Computationally efficient operational modal analysis methods suited to the low-cost IoT edge nodes are also explored. The work carried out aims to extend the lifetime of existing wind farms by properly monitoring their structural integrity. Key words. Wind farms, Structural Health Monitoring, IoT.

This work presents the ongoing development of a low-power wireless triaxial accelerometer designed by Lind Engineering for Structural Health Monitoring (SHM) applications. We have had the experience of implementing various infrastructure monitoring projects, either using well known international technology brands for sensor equipment and data acquisitions, and integrating them into instrumentation cabinets. However, the field working constraints and hurdles, for connecting into the power grid on mining sites, cabinet mounting and cabling, equipment transport among others have pushed us into developing our version for a wireless accelerometer. In addition, well-known international brands have shown to be unreliable on field deployment. The main objective is to provide adaptable technological capabilities for diverse company projects. Additionally, the project aims to scale the technology toward the development of nationally manufactured instrumentation where the Raspberry Pi directly interfaces with the ADXL355Z accelerometer, achieving high sampling rates and reliability comparable to high-end international systems, but at a significantly lower cost. In Chile, structural monitoring projects are still restricted to pilot test on bridges and special structures in diverse industries, o more broadly implemented in mining industry. When implemented, often operate under limited budgets or as part of broader infrastructure or research initiatives. Within this context, the proposed development not only addresses Lind Engineering’s internal needs but is also intended for use by other stakeholders in the field, including academic institutions. The project stages includes: (1) maximizing the operating frequency of the selected sensor, (2) integrating stage 1 with a commercially available data acquisition system, (3) incorporating control systems and IoT communication hardware, (4) developing and integrating power supply hardware, and (5) encapsulation with technological maturity (TRL 6), for further validation within Lind clients. A subsequent phase foresees the development of dedicated acquisition hardware to further optimize performance and reduce costs, though this is beyond the current scope. At present, efforts focus on maximizing the sensor’s sampling frequency and configuring its operating ranges at ±2 g, ±4 g, and ±8 g critical for dynamic analysis of structures exposed to high-frequency vibrations, such as railways, bridges, mining facilities, docks, and other critical infrastructure. Tests have shown the sensor can record signals up to 2000 Hz, far exceeding the target minimum of 125 Hz. The design ensures mechanical and environmental robustness for field applications, with a minimum IP67 rating for dust and moisture resistance. Data access protocols such as FTP, SFTP, and others are included to enable integration with real-time visualization platforms, including Lind Engineering’s integrated monitoring system, ensuring interoperability with current and future systems. In summary, the project strengthens Lind Engineering’s technological capabilities in innovation and applied electronics, while offering a scalable, cost-effective, and high-impact solution for structural monitoring of critical infrastructure.

This work presents the design and simulation of a wideband planar folded dipole antenna (PFDA) aimed at enhancing high-speed wireless communication in the 5–6 GHz frequency band. The antenna is designed using ANSYS HFSS and fabricated on an FR4 substrate with a thickness of 0.6 mm. The proposed design achieves an impedance bandwidth of 420 MHz (5.38 – 5.8 GHz), a gain of 5 dB, and a narrow beamwidth of 20° × 30°, making it suitable for modern wireless, radar, and point-to-point communication systems. A return loss of –22 dB is observed at the center frequency of 5.5 GHz, indicating excellent impedance matching. The antenna demonstrates a stable radiation pattern, efficient power handling (up to 100 W), and linear polarization, contributing to reliable performance in high-frequency environments. By offering compactness and improved directivity compared to traditional broadband antennas, this design supports the development of portable, energy-efficient RF devices. Such advancements align with sustainable technology goals by enabling low-power, spacesaving, and cost-effective wireless systems for future communication infrastructure.

Modern radio telescopes are revolutionising our understanding of non-thermal phenomena in galaxy clusters, collecting large samples of extended sources with unprecedented sensitivity and angular resolution. In this work, we present novel MeerKAT observations for a sample of 21 galaxy clusters that are part of the CHEX-MATE project. These systems were selected based on their high mass and displaying signs of dynamical activity. Thanks to the high-quality data at hand, we were able to detect extended radio emission in every target considered. We report two new halos, one new relic, and two new candidate relics. We also confirm a previous candidate halo and two candidate relics. After investigating the scaling relations with the cluster properties, we confirmed the presence of a radio halo power-mass correlation and relate it to a higher radio halo emissivity in more massive clusters. For radio relics, we highlight the MeerKAT capabilities to significantly extend the depth of radio observations to a new, unexplored field of low-radio power sources (łesssim 10^ 23 ̊m W Hz^ -1 at 1.28 GHz). Thanks to such high-sensitivity data, we have found that the radio relic power can be characterised by a wide range of values for a given cluster mass and relic size. Ultimately, we discuss how current radio observations, in combination with large radio surveys, are increasingly capable of testing numerical simulation predictions and coming close to performing direct comparisons with their data, enabling new insights on the evolution of radio relics.

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and are unsuitable for continuous use. Here, we present a conformable wearable ultrasound system that combines lens-assisted acoustic focusing with machine-learning regression to enable non-invasive bladder volume estimation, while providing a clear path toward future real-time implementation. A flexible PZT array integrated with a concave acoustic lens enhances lateral energy concentration and depth selectivity, while a Random Forest model was used to map echo-derived features to bladder volume estimates. In a pilot study, bladder-volume estimates generated offline after data collection showed good agreement with a benchtop electrical impedance-based measurement system, supporting the feasibility of non-invasive bladder volume estimation. The device was operated using conservative low-voltage, low-duty-cycle excitation settings designed to minimize acoustic exposure and be consistent with diagnostic-ultrasound safety guidance, and biocompatible, flexible encapsulation is designed to support extended wear. Together with compact packaging and low-power wireless transmission, these attributes support ambulatory, longitudinal bladder monitoring and offer design insights for future wearable ultrasound systems targeting precise and ultimately continuous physiological monitoring.

The proliferation of IoT devices and wireless sensors demands battery-free, long-term power solutions. This letter presents a practical RF energy harvesting (RFEH) module using the commercial Powercast P2110B (915 MHz), focusing on optimized storage capacitor selection for system-level efficiency. Experimental results with a 3 W dedicated transmitter show reliable energy delivery up to 2.2 m. A 47 mF Murata supercapacitor achieved an efficiency ratio of 0.065, outperforming a commercial 50 mF module (0.055). Lower-quality local supercapacitors exhibited high ripple and unstable output, highlighting the critical role of component quality. These results provide a compact, cost-effective solution for battery-free sensors in industrial environments.

LoRa-based IoT systems are increasingly used in smart farming, greenhouse monitoring, and large-scale agricultural sensing, where long-range, energy-efficient communication is essential. However, estimating link quality metrics such as PRR, RSSI, and SNR typically requires continuous packet transmission and sequence logging, an impractical approach for power-constrained field nodes. This study proposes a deep learning-driven framework for real-time prediction of link- and network-level performance in multihop LoRa networks, targeting the LoRaDisC protocol commonly deployed in agricultural environments. By integrating Bayesian surrogate modeling with Random Forest-guided hyperparameter optimization, the system accurately predicts PRR, RSSI, and SNR using multivariate time series features. Experiments on a large-scale outdoor LoRa testbed (ChirpBox) show that aggregated link layer metrics strongly correlate with PRR, with performance influenced by environmental variables such as humidity, temperature, and field topology. The optimized model achieves a mean absolute error (MAE) of 8.83 and adapts effectively to dynamic environmental conditions. This work enables energy-efficient, autonomous communication in agricultural IoT deployments, supporting reliable field sensing, crop monitoring, livestock tracking, and other smart farming applications that depend on resilient low-power wireless connectivity.

Ensuring Reliable Communication for UAV-Assisted Low-Power Wireless Network in Harsh Environments with Interference

Abstract Wind is a form of renewable energy that carries a considerable amount of energy, and the development of efficient methods for harnessing and converting it presents considerable promise for sustainable power generation. This paper presents a novel rotational piezoelectric energy harvester (RPEH) designed to efficiently harness wind energy for powering low-power wireless sensors. Based on Hertzian contact theory and Euler–Bernoulli beam Theory, the study models the response of the piezoelectric beam during both the excitation phase and the damping oscillation phase. A custom-designed experimental testing system was employed to investigate and analyze the structural variables of the energy harvester. Experimental results demonstrate that under conditions of a gear diameter of 70 mm, a rotational speed of 500 rpm, and a 10 kΩ load resistor, a single piezoelectric element achieved a maximum power output of 9.27 mW, which is sufficient to power small wireless sensor devices. Furthermore, the practicality of the piezoelectric energy harvester in energy harvesting applications was validated through LED illumination and charge capacity experiments. In conclusion, the proposed RPEH offers an innovative solution for wind energy capture and wireless sensor power supply, providing a significant reference for the development of self-powered distributed sensor networks.

The proliferation of wireless sensor networks (WSNs) has transformed environmental monitoring, yet underwater environments present unique challenges for real-time water quality assessment. This paper implements a novel approach leveraging Underwater Acoustic Sensor Networks (UWASN) to optimize water quality monitoring through duty-cycled reservation-based MAC protocols. The framework integrates low-power Zigbee radios, hierarchical clustering, and optimization algorithms to address energy constraints, scalability, and data reliability. A Markov chain analytical model evaluates protocol effectiveness, focusing on key parameters such as throughput and packet delivery ratio. The study simulates various network topologies—2D static, 3D dynamic, clustered deployments—and assesses their impact on monitoring diverse water quality factors, including pH, dissolved oxygen, turbidity, conductivity, and temperature. Comparative results highlight MAC protocol advances over commercial systems, demonstrating improved coverage and lifespan. The research closes critical gaps in secure communication, adaptive clustering, and energy-efficient node deployment, with comprehensive tables and graphical results substantiating findings. The presented paradigm not only enhances aquatic resource management but also lays groundwork for future smart sensor systems.

High-dose melphalan (MEL) combined with autologous stem cell transplantation (ASCT) effectively treats multiple myeloma (MM) but often causes oral mucositis (OM), affecting patient outcomes. This study aims to evaluate the effectiveness of an enhanced oral care model in preventing MEL-induced OM. This retrospective study reviewed the medical records of 87 patients with MM who underwent ASCT at the First Affiliated Hospital of Soochow University between December 2019 and February 2022. According to the oral care protocols that had been implemented during their hospitalization, patients were classified into 2 groups: a control group (n = 49) that had received routine oral care and an observation group (n = 38) that had received an enhanced oral care regimen. All patients had received cryotherapy during MEL infusion as part of standard supportive care. The enhanced oral care protocol included prolonged (30-second) gargling with oropharyngeal involvement, cheek puffing exercises, and nurse-supervised education to ensure proper technique and adherence. Relevant clinical data were extracted from medical records, including the incidence, severity, onset, and healing time of OM; pain scores; neutrophil counts; nutritional status; weight change; and length of hospital stay. Baseline characteristics were comparable between groups. The observation group experienced significantly shorter hospital stays and less weight loss (P < .05). OM onset was delayed, and fewer patients developed OM in the observation group (P < .05). At OM healing, the observation group had higher neutrophil counts and better nutritional scores (P < .05). No significant differences were observed in OM pain, severity, or healing duration. Overall, the enhanced regimen improved recovery and reduced OM incidence and impact. However, the retrospective single-center design and limited sample size may restrict the generalizability of these findings, which should be validated in larger prospective studies. Enhanced oral care – incorporating standardized gargling techniques, patient education, and cryotherapy – effectively reduces OM incidence and improves clinical outcomes in MM patients undergoing ASCT. Optimizing oral hygiene protocols may enhance quality of care during high-dose MEL therapy.

To address the issue that the output voltage and power of medium- and low-power wireless power transfer (WPT) systems cannot remain constant under coil misalignment, this paper proposes a single-switch WPT system with misalignment-tolerant characteristics. Based on a single-switch topology, the system combines the LCC-S and S-S compensation networks through an input-series and output-series connection, forming a simplified hybrid-compensated single-switch WPT topology. By exploiting the complementary output characteristics of the two compensation networks, a stable output voltage is achieved under varying mutual inductance conditions. To further enhance misalignment adaptability, a grid-type flat spiral (GFSP) coil is designed for the magnetic coupler. This coil configuration avoids magnetic flux cancelation during lateral displacement, while maintaining a consistent mutual inductance variation trend between the dual windings, thereby exhibiting strong tolerance to misalignment along the X-axis. The proposed system is validated through MATLAB/Simulink simulations and experiments on a 50 W prototype. The results demonstrate that the system maintains resonance and achieves zero-voltage switching (ZVS) of the power device under ±60 mm X-axis misalignment, with output voltage fluctuation below 4% and efficiency fluctuation below 3%, verifying the proposed system’s effectiveness in misalignment tolerance.

Flying Ad-hoc Networks (FANETs), formed by collaborating unmanned aerial vehicles (UAVs), have emerged as a promising paradigm for enabling dynamic, infrastructure-free wireless communication in three-dimensional (3-D) aerial environments. This paper reviews the state-of-the-art in FANET research, covering its architecture, communication technologies, routing strategies, recent innovations, and open challenges. Key enabling technologies such as low-power wide area wireless protocols, machine-learning based routing, and clustering algorithms are examined. We also highlight significant limitations—including high mobility, rapid topology changes, limited energy resources, and security threats—that hinder reliable large-scale deployment. The review concludes by identifying possible future research directions to overcome these challenges and fully realize the potential of FANETs across civilian, industrial, and military applications.

Even though riverine ecosystems constitute the basis of ecological stability, biodiversity conservation, and provision of critical ecosystem services, there is a growing threat concerning the levels of aquatic pollution caused by industrial effluents, agricultural runoff, municipal waste discharge, and overt urban growth and expansion. Manual sampling and lab analysis based traditional methods of water quality monitoring are commonly slow, spatially limited, and incapable of defining the great dynamism of pollution signatures within flowing river systems. To overcome these shortcomings, this paper suggests a holistic AI integrated remote sensing system based on IoT to operate in real-time, high-resolution, spatio-temporal measures of aquatic pollution and ecosystem well-being from riverine settings. The framework combines these elements in a low-power wireless sensor network (WSNs) of continuous in-situ monitoring, multispectral/hyperspectral satellite data (such as Sentinel-2 and Landsat-8) on a platform, and unmanned aerial vehicle (UAV)-mounted optical and thermal already holds useful information to create a multi-source/ multi-scale environmental dataset. The feature extraction is being performed using the advanced artificial intelligence model such as deep neural networks (DNN), long short-term memory (LSTM) networks, gradient boosting algorithms, and spatio-temporal kriging; the predictive models, anomaly detection, and estimation of key water quality indicators such as pH, dissolved oxygen (DO), turbidity, total dissolved solids (TDS), nitrate concentration, and chlorophyll-a can be done. The unified system also includes analytics in the clouds and geospatial decision support tools to create pollution heatmaps, predict cases of contamination, and an analysis of the index of ecosystem health. As is evident in experimental validation with real world field data, the proposed framework is far more effective than the traditional method of monitoring in terms of prediction accuracy, latency, spatial coverage and also allows the ability to issue early-warnings. In general, the created AI-IoT-enabled remote sensing architecture provides an efficient, intelligent, and scalable framework of managing sustainable river basin, environmental policy control, and data-driven ecosystem security in response to emerging pressures caused by humans.

RF energy harvesting has emerged as a promising solution for powering low-power IoT nodes, wireless sensors, and portable electronics, offering an alternative to conventional batteries. This review provides a concise overview of rectenna-based RF harvesting systems, outlining their operating principles and summarizing recent advancements in wideband, multi-band, flexible, and wearable designs. Developments in rectenna arrays and metasurface-based architectures are also discussed, with emphasis on strategies to enhance harvested power. Key challenges and future research directions are highlighted, focusing on efficiency improvements and scalable integration.

Classroom lecture detection and tracking systems have emerged as one of the important IoT-driven solutions in educational institutions for automating the monitoring of the lecturer presence, which is also known to increase the operational efficiency resulting in better resource utilization. This survey reviews the advancements employing RFID for contactless identification, ESP32 microcontrollers for edge processing and also Zigbee protocols for low-power wireless networking. After reviewing few available reviews, we trace architectural evolutions, implementation methodologies and performance metrics, achieving 90 - 98 per cent accuracy in real time tracking. The methodology mentioned outlines a modular IoT framework with cloud integration for flexible deployments. Literature highlights the RFID's dominance, not to ignore the challenges like privacy and interference, while future scopes include AI hybrids and 5G enhancements. Results demonstrate that there is 70 to 80 per cent reductions in administrative overhead, highlighting these systems' role in smart campuses. Continuous innovations in architectures and training strategies position IoT-RFID as mainstay for generative educational modeling

ABSTRACTBackgroundDysgeusia is a side effect of the anti‐GPRC5DxCD3 bispecific antibody talquetamab (TAL), but other myeloma treatments, such as high‐dose melphalan (MEL) with autologous stem cell transplantation (ASCT), are also known to alter taste perception in patients with multiple myeloma (MM). This study investigates the spectrum, prevalence and severity of dysgeusia in patients receiving TAL and MEL and compares the results with anti‐BCMA bispecifics as a control for T‐cell‐engaging therapies.MethodsGustatory and olfactory performance was assessed in 87 MM patients divided into three treatment groups: TAL (n = 26), MEL/ASCT (n = 35), and BCMA bispecifics (n = 26). Evaluations included Taste Strips, Sniffin' Sticks Identification Test 16, and comprehensive questionnaires on taste perception, dietary issues, quality of life (QoL), mood, and treatment compliance.ResultsTAL‐treated patients exhibited severe taste impairment, with 96.2% reporting marked declines. Taste alterations were also observed in patients receiving MEL/ASCT and BCMA bispecifics, though these were less pronounced, affecting 62.9% and 30.8% of cases, respectively. Xerostomia incidence was highest in the TAL group. Patients considering discontinuation of TAL (30%) cited taste alterations as the primary reason. MEL was associated with higher incidences of nausea, vomiting, and appetite loss.ConclusionTAL‐associated taste disturbances have a major impact on patients and require further investigation and mitigation strategies. Enhanced patient support, proactive monitoring, and targeted interventions are critical to improving the well‐being and adherence of MM patients.