Wearable bioelectronics are revolutionizing personalized healthcare by enabling continuous monitoring of physiochemical indicators. Despite significant advancements, challenges remain in creating platforms that can accurately detect multiple biomarkers while being energy autonomous. This study introduces a wearable, minimally invasive microneedle array platform for real-time monitoring of clinically relevant biomarkers during exercise. The device features stainless-steel microneedles (SS-MNs) with ion-selective membranes and a glucose-sensing layer, allowing simultaneous detection of sodium (Na+), potassium (K+), calcium (Ca2 +), pH levels, and glucose in interstitial fluid (ISF). A triboelectric nanogenerator (TENG) paired with an electromagnetic generator (EMG) harnesses energy from mechanical movements to power the data transmission system, eliminating the need for external power sources. Material characterization confirmed the composition and structure of the ion-selective membranes, while in vitro electrochemical tests showed excellent sensitivity and selectivity across physiologically relevant concentration ranges. The hybrid power generation system (HPGS), combined with the microneedle array-based biosensing system (MABS), offers a modern solution for wearable electronics. Additionally, on-body trials confirmed the platform's ability to continuously track biomarker levels during daily activities. This innovative, minimally invasive system marks a significant advancement in wearable biosensing technology, with potential uses in personalized medicine, chronic disease management, and telemedicine.

Recently, the incorporation of Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) has gained important attention because of its potential to provision intelligent, large-scale monitoring and data transmission systems. Nonetheless, managing limited resources, like energy, bandwidth, and computational power remains a critical challenge in WSN–IoT environments. Efficient network management, predominantly in routing and Cluster Head Selection (CHS), is vital to confirm prolonged network lifetime and reliable data transmission. To address these challenges, machine learning and nature-inspired optimization models have been increasingly adopted. This study presents a Convolutional Neural Network (CNN) approach designed to predict multi-objective constraints. The CNN was trained by employing a Cat-and-Mouse Optimizer (CMBO) model that was utilized to adjust the learning rate of the CNN. Improved predictive accuracy and stability through a combination of CNN and CMBO models for fine-tuning. Following the prediction of multi-objective constraints, CH Selection was performed. Efficient CHS to optimize energy consumption and enhance network lifetime. Additionally, the Cheetah Optimizer (COA) was applied to enhance the routing performance of IoT devices within a WSN–IoT network. Enhanced routing performance and optimized data transmission in IoT devices within WSN–IoT networks using COA. The analysis demonstrated that the proposed model outperformed existing approaches across multiple performance metrics. Specifically, it achieved superior results in terms of the residual energy (0.838), LLT (0.453), trust (0.923), QoS (0.761), and throughput (0.838), indicating its overall effectiveness and efficiency.

In today’s digitally connected world, ensuring the secure and covert transmission of sensitive information has become a critical challenge. Traditional encryption alone reveals the existence of secret communication, inviting targeted attacks. This paper presents Stegno Vault, a web-based secure data transmission system that integrates Least Significant Bit (LSB) steganography, Advanced Encryption Standard (AES) encryption, and Deep Genetic Algorithm (GA) optimization to hide secret messages within digital images, audio, and video files. The system is built on a Java Spring Boot backend, MySQL database, and a responsive HTML/CSS/JavaScript frontend, and is accessible through any standard web browser without local installation. The Genetic Algorithm selects optimal, high-entropy embedding positions in each media file, while AES encryption ensures hidden data remains unreadable even if detected. Role-based access control, OTP-based email verification, BCrypt password hashing, and a dedicated Admin governance panel provide enterprise-grade security. Experimental results confirm PSNR above 50 dB for image steganography and 100% data recovery accuracy across all three media types.

Now-a-days wireless communication uses radio waves. Spectrum is the one of the most essential requirement for wireless communication. With the advancement in technology and the number of users, the existing radio-wave spectrum fails to cater to this need. To resolve the issues of scalability, availability and security, we have come up with the concept of transmitting data wirelessly through light using LED‘s. An indoor visible data transmission system utilizing LEDs is proposed. In this system, these devices are used not only for illuminating rooms, but also for an optical wireless communication system. Also with this, our project also has audio system that is well suited for use in a small confined area with many audio transmitters broadcasting different audio signals.

The study presents the development and verification of an adaptive data transmission system for controlling unmanned surface vehicles (USVs) in unstable communication channels. The work aims to overcome the limitations of existing technologies, which include LTE networks and satellite systems that fail to deliver stable service quality for USV remote control operations. The proposed adaptive routing algorithm evaluates communication channel status through three vital indicators, which include delay, packet loss, and availability. The algorithm selects the best channels according to changing weight parameters. Experimental results confirmed a significant reduction in data transmission delays, stable real-time video streaming with a delay of 1–4 seconds, and a reduction in packet loss to below 2 %. In addition, the system implements the use of modern video coding standards (e.g., H.265) and secure VPN channels, which increase bandwidth efficiency and the level of cybersecurity. The results confirm the practical suitability of the proposed system for USV operation in real marine conditions, as well as its potential for use in critical scenarios that require stable, low-latency communication.

Smart mining represents the core direction of the intelligent transformation in the coal and mining industry, and real-time data transmission is the vital lifeline for the efficient operation and safety management of smart mines. Traditional mine data transmission modes suffer from pain points such as insufficient bandwidth, high latency, poor anti-interference performance and limited access for massive terminals, making it difficult to adapt to the complex underground operating environment and intelligent operation requirements. 5G technology, with its core advantages of high bandwidth, low latency, massive connectivity and high reliability, provides communication support for the high-speed transmission of massive mine data. Edge computing sinks computing power to network edge nodes, enabling local data processing and rapid instruction delivery, which further reduces transmission latency, alleviates cloud computing pressure and ensures the stability of data transmission. Focusing on the integrated 5G+edge computing technology, this paper analyzes the core requirements for real-time data transmission in smart mines, expounds the technical characteristics and integration logic of 5G and edge computing, explores in depth the practical applications of this technology in scenarios such as environmental monitoring, remote equipment control, unmanned mining and emergency rescue, and analyzes the current technical challenges and optimization paths in its application. It aims to provide theoretical reference and practical guidance for the upgrading of the data transmission system in smart mines and the high-quality development of mining intelligence.

This paper presents the design and evaluation of a LoRa-based Internet of Things (IoT) communication system for water quality monitoring integrated with a micro smart grid. The system operates at 923.2 MHz with 125 kHz bandwidth using the SX1276 transceiver and FHSS modulation to achieve long-range, low-power communication. A single-channel LoRa gateway, built on a Raspberry Pi 3, forwards sensor data to the ThingSpeak cloud platform through a LoRa Network and Application Server for real-time visualization of environmental and electrical parameters. Theoretical modeling with the Free-Space Path Loss (FSPL) model and Keysight ADS simulation predicted a received power of –72.8 dBm at 2 km. Field measurements recorded –108 dBm, showing an extra 35 dB attenuation from Fresnel obstruction, multipath, and ground reflection. Despite this, the system achieved a 95% packet delivery ratio (PDR) with a measured SNR of +9 dB, consistent with link budget analysis. With a 30-byte payload, the time-on-air was ~4.8 ms, yielding an effective throughput of 47.5 kbps. Results confirm the system’s reliability, efficiency, and suitability for solar-powered monitoring stations, supporting smart grid and water management applications.

Abstract The growing demand for batteryless indoor Internet of Things (IoT) devices has stimulated interest in visible light communication (VLC) systems capable of simultaneously delivering information and harvesting energy from ambient illumination. This paper presents a photovoltaic (PV)-based VLC system employing asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM) to enable simultaneous lightwave information and power transfer (SLIPT) using a single PV receiver. A bias-tee front-end separates the harvested direct-current (DC) component from the data-bearing alternating-current (AC) signal, while an adaptive normalized least mean squares (NLMS) equalizer is applied to mitigate illumination-dependent gain drift, PV nonlinearity, and maximum power point tracking (MPPT)-induced ripple. Comprehensive simulations evaluate bit error rate (BER), spectral efficiency (SE), and harvested power under practical indoor conditions. Results show that reliable communication with BER below 10 −3 is achieved at approximately 18 dB SNR for 16-QAM and 25 dB for 64-QAM. The system attains spectral efficiency approaching 1 bit/s/Hz and 1.5 bit/s/Hz for 64-QAM at moderate-to-high SNRs while maintaining energy-neutral operation at illumination levels above approximately 800 lux. These findings confirm the feasibility of PV-based VLC as an effective solution for low-power, batteryless indoor IoT applications.

Recent technological advancements in data transmission and acquisition systems for drilling operations, along with the growing global energy demand, have led the Oil & Gas industry to pursue data-driven solutions in real-time to improve performance and reduce well construction costs. In this context, the Rate of Penetration (ROP) serves as a key drilling performance metric, reflecting the effective speed of the drill string as the bit penetrates the rock formation. Predictive models for ROP can be employed in real time to suggest appropriate operational parameters that optimize ROP, potentially reducing drilling time and overall operating costs. This work investigates the application of Long Short-Term Memory (LSTM) networks for ROP prediction within a continual learning scenario based on drilling data. LSTM networks are well-suited for this task due to their ability to model dependencies in sequential data. The results obtained using the LSTM model are compared with benchmark results from Multilayer Perceptron (MLP) networks. A scenario is simulated in which the models are incrementally trained with new data received during drilling, predicting ROP in subsequent intervals. Given the transient nature of drilling, abrupt changes in data distribution are expected and may significantly impact predictive performance. Therefore, the model's performance in the test intervals is analyzed in relation to statistical characterizations of the newly received data. The dataset used comprises public data from three wells in the Volve field in the North Sea. It includes both operational parameters and lithological information, the latter being particularly important for analyzing the results obtained in this study. Model performance is evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results demonstrate the potential of the adopted strategies using the LSTM model. It is expected that the comparative analyses presented in this study will contribute to key aspects of developing data-driven solutions for real-time drilling applications.

ABSTRACT Large‐scale cross‐domain data exchange via 6G networks is needed for intelligent applications. Centralized AI frameworks expose sensitive data, compromising privacy and security. This study proposes a privacy‐preserving decentralized artificial intelligence (PPDAI) system for safe and verifiable data transmission using federated learning (FL) and blockchain‐based access control (BAC). Avoiding data breaches and unwanted access, PPDAI saves raw data locally and delivers encrypted model updates via an immutable blockchain ledger. Breakthroughs include a hybrid privacy‐preserving method that combines model aggregation with decentralized ledger verification and a lightweight consensus mechanism for communication efficiency. Testing shows that PPDAI increases model accuracy by 4%–6%, decreases communication cost, and minimizes inference attack susceptibility compared to standard FL systems. The strong, scalable, and privacy‐aware basis for AI deployment in next‐generation 6G networks advances safe data sharing and decentralized intelligence frameworks.

All-organic red-green-blue (RGB) visible light communication (VLC) systems hold significant promise for future wireless communications because they can be readily integrated with existing lighting infrastructures. However, the stability, efficiency, and exciton decay times of printed deep-blue organic light-emitting diodes (OLEDs) currently fall short of the high bandwidth, rapid response, and swift data transmission requirements of VLC systems. Herein, two printable deep-blue fluorescent light-emitting π-conjugated polymers (LπCPs) were fabricated based on a multi-dimensional self-encapsulation strategy for application in all-OLED RGB VLC systems. The printed fluorescent films displayed remarkably fast decay life-times of ∼0.30ns, enabling high bandwidth and fast response. The deep-blue OLEDs presented a CIE coordinate of (0.15, 0.06), narrow deep-blue emission with a full width at half maximum (FWHM) of 21nm, high external quantum efficiency (EQE) of 1.94%, and high brightness of 6698cd/m2 with remarkable durability. Finally, preliminary printed all-OLED RGB VLC systems were successfully established, and through efficient energy transfer, demonstrated the transmission of pseudo-random binary sequence (PRBS) signals and audio data at a rate of 1Mbps. The fast response times, on the order of microseconds, highlight the potential of these all-OLED VLC systems for high-speed data transmission.

BACKGROUND: There has been an increasing focus on the use of digital systems for remote monitoring (RM) of patient health recently. AIM: To evaluate the clinical effectiveness of the RM system in patients with type 1 and type 2 diabetes (T1D and T2D) compared to traditional outpatient care. MATERIALS AND METHODS: a non-randomized prospective open comparative multicenter study with parallel groups was conducted in 7 regions of the Russian Federation from March to September 2024. The study included patients with T1D, T2D on non-insulin therapy, and T2D on insulin therapy. The intervention group used a glycaemia RM system, which included a glucometer with a data transmission set, a mobile application that received data from the glucometer via Bluetooth technology, and a data transmission system for the doctor. In the control group, glycemia was assessed as part of routine clinical practice (in-person visits with a self-monitoring diary). RESULTS: A total of 1,572 patients were included in the study. After a 180-day follow-up, the overall completion rate was 48% (754 patients). The primary endpoint, HbA1c levels, decreased comparably in the RM and control groups in patients with T1DM and in both cohorts of patients with T2DM. The proportion of individuals who achieved HbA1c target values was higher in the RM group compared to the control group in patients with T1DM (26.06% vs. 10.91%, respectively, p=0.023) and T2DM on non-insulin therapy (51.5% vs. 33%, respectively, p=0.003). RM use was associated with a reduction in unscheduled medical interventions. CONCLUSION: RM has shown clinical efficacy in increasing the proportion of patients achieving HbA1c target values in the group of patients with T1D and T2D on non-insulin antidiabetic therapy.

Weakly robust global optimization of max-plus linear systems with non-negative constraint sets and its application to optimizing time parameters in data transmission systems

Abstract Mosaic sCMOS cameras are critical for wide-field astronomical surveys. However, the massive data volume generated by these high-resolution arrays imposes severe strains on traditional architectures, creating bottlenecks in data transmission bandwidth and system integration. To address these limitations, this paper presents the design and performance evaluation of the H-455, an intelligent mosaic camera integrating two SONY IMX455 Back-Side Illuminated sensors. Unlike conventional systems, the H-455 employs a System-on-Chip architecture combining an FPGA and an embedded Linux system, enabling autonomous onboard control and data processing. Specifically, we demonstrate the deployment of core algorithms from the Mini-SiTian Instrumental Effects Removal pipeline on the embedded platform, to perform real-time statistical noise characterization, high-precision flat-field synthesis, and satellite track identification. Systematic laboratory characterization demonstrates that the camera achieves scientific-grade performance, featuring a readout noise of ∼1.5 e − in high-gain mode, a linearity of R 2 > 0.9998, and a dark current of 0.0031 e − pixel −1 s −1 at −20°C. A comparative analysis of the two mosaic sensors reveals intrinsic performance disparities: one sensor exhibits superior background stability, while the other demonstrates tighter nonlinearity error control, highlighting the necessity for individual chip calibration in mosaic arrays. On-sky validation using the 1 m telescope at the NAOC Xinglong Observatory confirmed the system’s observational capabilities, achieving a signal-to-noise ratio (SNR) of 13.6 for a 19.4 mag star with a 180 s exposure. Based on this measurement, the estimated limiting magnitude reaches approximately 20.47 mag (SNR = 5). These results validate the H-455 as a viable solution for future time-domain and deep-sky survey applications.

Design and implementation of an RDMA-based data transmission system prototype for ETF

The article highlights the peculiarities of the technology for using a control hive on apiaries of various types: honey production, queen-rearing, package bee, industrial, and amateur. The control hive is an innovative educational and research tool developed through the work of a student scientific group to improve the efficiency of observing bee colony conditions without disturbing the microclimate of the nest. The study focuses on optimizing technological processes in beekeeping through the use of the control hive for continuous monitoring of bees’ physiological and behavioral states, productivity control, brood development analysis, and honey flow assessment. The paper presents a detailed methodology for integrating the control hive into different types of apiaries. For honey-producing apiaries, the hive records daily weight changes to determine nectar flow dynamics; for queen-rearing apiaries, it controls the temperature and humidity during queen breeding; and for package bee apiaries, it helps monitor colony strength before forming bee packages. In industrial apiaries, the control hive allows remote monitoring of multiple colonies using automated data collection systems, while in amateur apiaries, it improves management accuracy and overall beekeeping culture. Particular attention is paid to digital technologies, including the use of sensor modules, weighing platforms, temperature sensors, wireless data transmission systems, and specialized software for data storage and analysis. This integration forms a unified monitoring database that can be used for breeding, productivity forecasting, honey quality control, and queen performance evaluation. The article also emphasizes the educational significance of the control hive as a training and research tool in agricultural education institutions. It contributes to the development of students’ professional competencies in digital monitoring, analytical thinking, and ecological management in beekeeping. The research results demonstrate that the use of the control hive increases the accuracy of bee colony assessment by 20–25%, reduces productivity losses during inspections by 10–15%, and improves nest microclimate stability. The findings confirm the feasibility and effectiveness of implementing control hives as an essential element of technological monitoring, education, and scientific research in modern beekeeping.

In the era of rapidly expanding artificial intelligence (AI) applications, ensuring secure data storage and transmission within AI computing power platforms remains a critical challenge. This research presents a novel data security storage and transmission system, termed as secure artificial intelligence data storage and transmission (Secure AI-DST), tailored for AI computing environments. The proposed framework integrates a hybrid encryption mechanism that combines Amended Merkle Tree (AMerT) hashing with Secret Elliptic Curve Cryptography (SEllC) enhanced data confidentiality. For secure storage and decentralization, the system leverages blockchain with InterPlanetary File System (IPFS) integration, ensuring tamper-proof and scalable data handling. To classify various attack types, a novel deep learning model attention bidirectional gated recurrent unit-assisted residual network (Att-BGR) is deployed, offering accurate detection of intrusions. Simulation studies conducted in MATLAB® 2023b using both synthetic and real-time datasets show that the Secure AI-DST system reduces unauthorized access attempts by 92.7%, maintains data integrity with 99.98% accuracy under simulated cyberattacks, and achieves a packet validation success rate of 97.6% across edge-to-cloud transmissions. Furthermore, the proposed method introduces only a 4.3% computational overhead, making it highly suitable for real-time AI workloads. These outcomes confirm the effectiveness of Secure AI-DST in ensuring end-to-end data guard, resilience against cyber threats, and scalable presentation for next-generation AI computing substructures.

This study presents the design and implementation of a prototype Remote Monitoring System (RMS) adapted to the climatic and geographic conditions of the Colombian amazon rainforest. The system has been developed to address the challenges faced by monitoring and conservation efforts in protected areas. The prototype integrates three modules: a remote sensing module using radar technology and a pan-tilt-zoom (PTZ) camera to detect and visualize unauthorized activities; a telecommunications module employing a satellite system for real-time data transmission; and an autonomous power supply module based on a photovoltaic system. The system's functionality was assessed through a field experiment in the Amacayacu National Natural Park, where its capacity to monitor the real-time movement of vessels on the Amacayacu River, identify potential threats, and generate e-mail alerts with pertinent information was evaluated.

The article is a further development of the authors' research in the field of information transmission systems, specifically the method of concentrating integral transformations. The object of study is one of the dominant factors leading to the limitation of information transmission speed over communication channels. This is intersymbol distortion or intersymbol interference. Previously, to reduce intersymbol distortions, the authors proposed a homomorphic filter. Its application allows the multiplicatively interacting information signal and the sampling window to be represented additively, as well as to isolate the error caused by the intersymbol interference itself. This error is the key factor that necessitates the reduction of information transmission speed. In the present article, an analysis of the described homomorphic filter is conducted, and an assessment of the error of the homomorphic filter for multiplicatively interacting signals and sampling windows under periodic evaluation is performed. The task of finding an estimate of the effectiveness of the formed homomorphic filter is solved by a method based on the use of an asymptotic approach. The main result presented in this work is an analytical estimate of the level of intersymbol interference of an asymptotic type for the adopted research model. The novelty lies in the obtained asymptotic estimate of the level of intersymbol distortions, which are the main factor limiting the speed of information transmission. It should also be noted that intersymbol interference is not only a problem for communication channels of classical data transmission systems but also a rapidly developing issue in the currently emerging G5 cellular networks. The article provides a generalized estimate of the level of intersymbol distortions for the model of a communication channel in the form of an ideal low-pass filter and rectangular data windows. Based on a simplified model of the behavior of this type of error, a partial estimate of the level of intersymbol interference of an asymptotic type is obtained.

Koi fish farming requires careful monitoring of water temperature and pH to prevent adverse impacts on the fish. This study presents a prototype IoT-based autofeeder that integrates real-time water quality monitoring and automatic feeding, controllable via both an Android application and local device buttons. The system allows users to configure feeding schedules, feed throw levels, and durations, as well as set pH thresholds. When the pH exceeds the safe range, the system automatically stops feeding and sends notifications, enabling the user to inspect and maintain pond water quality. The findings demonstrate that the dispensing level significantly influences the feed-throwing distance; higher dispensing levels result in longer distances. Small-sized feed (S) consistently produced the highest output, followed by medium-sized (M) and large-sized (L). Increasing the feeding duration enhanced the weight of the released feed. Additionally, the average delay in sensor data transmission to the database was recorded at 5.48 seconds. The data loss rate during the testing period was 1.72%, which is considered acceptable and does not adversely affect system operations. The data transmission system demonstrated good and stable performance with relatively low data loss.