Real-time Data Transmission Research Articles

Evaluation of Impact of Soil on Performance of Monopole Antenna for IoT Applications in Urban Agriculture

Built indoor IoT-based urban farms successfully combine the cultivation of fresh vegetables with attractive architectural designs. Moreover, implementing IoT-driven urban agriculture requires installing multiple IoT devices containing sensors, controllers, transceivers, and antennas for real-time data transmission. In this context, several factors, including the height of the IoT device above the soil level and the water content in the soil, can affect antenna performance and, consequently, the propagation of radio waves. This paper presents the results from numerical and experimental studies that evaluate the impact of soil on the performance of a monopole antenna for three different antenna positions relative to the soil in a pot and two soil water contents, presented by twelve scenarios. The results show that the antenna has a stable performance in six of the twelve scenarios, with a minimal shift in the resonant frequency of 3% and a narrowing of the frequency bandwidth by 2% compared to the antenna in free space. In the worst-case scenario, the antennas demonstrate a reduction in radiation efficiency of 44%, with the frequency bandwidth narrowing by up to 14% for the antenna fabricated on a PLA substrate and up to 17% for the one built on a foam board substrate.

Leveraging Mobile Interaction Technologies for Real-Time Decision Making in Enterprise Management Systems

With the widespread adoption of mobile internet and smart devices, enterprises face rapidly changing market environments and complex decision-making requirements. How to utilize mobile interaction technologies to enhance real-time decision-making capabilities in enterprise management systems has become a critical issue in modern business management. Through real-time data transmission and interaction via mobile devices, businesses can more flexibly acquire information, monitor statuses, and optimize decision-making processes, thereby improving the timeliness and accuracy of decisions. Existing research primarily focuses on traditional decision support systems (DSS) and data mining technologies, but these methods fall short when addressing the dynamic and real-time decision-making needs, especially in complex enterprise management scenarios. One major challenge remains the effective integration of multiple data sources. Therefore, leveraging real-time interactive data from mobile technologies to improve decision support in enterprise management systems becomes a pressing research topic. This paper proposes a real-time decision-making framework for enterprise management systems based on mobile interaction technologies, focusing on the problem description of real-time decision-making in enterprise management and a decision-making mining framework based on mobile interaction relationships and mixed user discourse (including decision belief propagation based on mobile interaction relationships and the UCBert model based on user content analysis). Through this framework, the paper aims to enhance decision-making accuracy and timeliness, enabling enterprises to make more informed and efficient decisions in a dynamically changing market.

Intelligent information systems for power grid fault analysis by computer communication technology

This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations.

Application analysis of wireless communication technology in China's smart grid

With the rapid advancement of China's economy and technological innovation, wireless communication technology has seen considerable development. Concurrently, global energy and environmental challenges have intensified, heightening the demand for efficient and sustainable energy management solutions. In response to evolving energy demands, China has introduced new standards for digital power grid construction, prioritizing the use of wireless communication technologies to improve energy efficiency and develop smarter grid systems. These technologies facilitate real-time data transmission, remote monitoring of equipment, and network interconnectivity, thereby improving operational efficiency and monitoring capabilities. This paper reviews recent applications of wireless communication technologies within smart grids, aiming to provide valuable insights for future developments based on existing literature and data. Wireless communication technology in smart grids mainly enhances the intelligence and efficiency of the grid by enabling real-time data transmission, remote device management, and optimized energy distribution. Technologies such as Wi-Fi, 5G, and LoRa facilitate key functions like smart meter monitoring, equipment status management, and demand response, driving the automation and intelligent development of power systems. Future research should focus on optimizing the integration of these technologies, strengthening network security, and leveraging big data and artificial intelligence to advance the intelligence of power systems, thereby addressing critical energy challenges and fostering sustainable development

Improvement of Internet of Things (IoT) Interference Based on Pre-Coding Techniques over 5G Networks

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Intelligent technologies in traffic flow control: a review

With the acceleration of global urbanization, traffic congestion has become a major bottleneck in the development of modern cities, leading to serious economic losses and a decline in the quality of life. To address this challenge, traffic flow prediction techniques have received much attention. This paper reviews the application of three key technologies, including 5G, artificial intelligence (AI), and vehicular networking (V2X) in intelligent traffic management. 5G technology provides a communication foundation with high speed rates, low latency, and large-scale device connectivity, which effectively supports real-time data transmission and communication needs. AI technology, on the other hand, with its powerful data processing capability, is able to make high-precision traffic predictions in dynamic traffic environments. However, the effectiveness of AI models is highly dependent on the support of high-quality data.V2X technology greatly improves road safety and traffic mobility by realizing real-time information exchange between vehicles and infrastructure. However, relying on a single technology alone cannot comprehensively solve complex transportation problems. For this reason, this paper proposes a technology fusion scheme of 5G, AI, and V2X, aiming to optimize the intelligence level of the traffic management system through the complementary advantages of each technology. The study shows that the synergistic application of the technologies can not only effectively alleviate traffic congestion but also improve the overall safety of roads, providing a feasible solution for the future intelligent transportation system in cities.

Wireless microwave-to-optical conversion via programmable metasurface without DC supply

Microwave-optical interaction and its effective utilization are vital technologies at the frontier of classical and quantum sciences for communication, sensing, and imaging. Typically, state-of-the-art microwave-to-optical converters are realized by fiber and circuit approaches with multiple processing steps, and external powers are necessary, which leads to many limitations. Here, we propose a programmable metasurface that can achieve direct and high-speed free-space microwave-to-laser conversion. Moreover, it supports reverse conversion, achieving bidirectional operations. The programmable metasurface converter is realized by integrating subwavelength microwave resonant structures, MS junction and photoelectric PN junction components together, without connecting any direct-current supplies to provide driving bias. We further demonstrate the enormous potentials of the metasurface converter in cross-media links and develop a full-duplex air-water wireless communication system. Experimental results show that the bidirectional real-time data transmissions and exchanges are established through the air-water boundary. This work represents a decisive step towards microwave-optical interconversion on wireless and battery-free interfaces.

Perancangan dan Pengembangan Automatic Fish Feeder Menggunakan Aplikasi Mobile Blynk dan ESP32

This research presents the design and development of an Internet of Things (IoT)-based Automatic Fish Feeder system using the Blynk platform to address common challenges in fish feeding management. The primary goal is to ensure consistent feeding schedules, minimize manual intervention, and provide remote control and monitoring capabilities. The system utilizes the ESP32 microcontroller integrated with ultrasonic sensors and servo motors to dispense feed accurately. Real-time data transmission is enabled via the Blynk Cloud, allowing users to schedule feeding times, monitor feed levels, and receive notifications when the feed stock is low. The methodology involves a multi-layer communication architecture: perception, data processing, communication, and application layers. Ultrasonic sensors calculate the remaining feed stock, while servo motors execute precise feeding commands. Users can interact through a user-friendly Blynk interface that visualizes feed levels, logs feeding history, and supports customizable schedules. The system also sends real-time alerts to ensure proactive management. The implementation successfully reduces manual feeding tasks, mitigates overfeeding risks, and enhances feed efficiency. This innovative approach supports sustainable aquaculture practices, promoting healthier fish growth for hobbyists and commercial breeders alike. Results demonstrate high reliability and user satisfaction, paving the way for future smart aquaculture solutions.

Optimization of data transmission in sensor networks for enhanced control of ozonator efficiency

The main object of the research is the efficiency of real-time ozonator control based on sensor networks. The study addressed the issue of low efficiency in ozonator control systems and the lack of reliability and speed in real-time data transmission. The research revealed that changes in pressure and temperature have a direct impact on ozone concentration. This finding made it possible to increase the ozonator's productivity by 15 %, reduce energy consumption by 10 %, and improve system reliability by 20 %. The key features of the results include the ability to monitor ozone levels in real-time, maintaining the stability of the ozonator, and optimizing its performance. Additionally, sensor networks ensured fast and accurate data delivery, enhancing the energy efficiency and reliability of the system. These results were explained based on experimental data that demonstrated how changes in pressure and temperature affect ozone concentration. The use of sensor networks contributed to increased system stability, reduced energy consumption, and improved control accuracy. The obtained results can be applied to ozonator systems and other fields requiring real-time environmental monitoring and control. The methods proposed in the study provide opportunities for optimizing industrial processes, reducing costs, and achieving sustainable development goals

Система контроля концентрации антигенов в организме человека.

The detection of antigens plays a key role in many diagnostic tests, but current methods often prove to be complex and expensive. There is a pressing need for a simple, fast, reliable, and cost-effective system for quantitative determination of antigens, suitable for both users and laboratories (photometer). The system will analyze patient fluid samples for a group of viruses, using monoclonal antibodies and different wavelengths of light measured by the photometer. Today, many people actively monitor their health and purchase expensive "smartwatches" to keep track of key indicators. The research hypothesis is that by analyzing continuous physiological data collected through a wearable device and information provided by patients via a cloud analytical platform, it is possible to detect physiological changes and important clinical signals that may indicate the early stages of various diseases, including viral infections. The sensor can play a special role during pandemics. Bluetooth connectivity with a smartphone and «smartwatches» equipped with the appropriate application will allow real-time remote transmission of all physiological data. Socially, such an analytical system will help people monitor critical health indicators, ensure early disease detection, and study multiple antigens simultaneously. This will give individuals confidence in their health status and the ability to improve their quality of life.

Machine Learning-Based Reconnaissance Bee Colony with Custom AES & AE for Robust IoT Data Security and Transmission

Objectives: To propose a robust method to enhance data security and transmission in IoT to address the challenges in real-time data transmission and create a comprehensive security solution. The new method takes credit for the machine learning-based bioinspired method to minimize data loss and maximize end-to-end data transmission rate. Methods: Custom-AES with Avalanche Effect (AE) and Machine Learning-based Reconnaissance Bee Colony are employed to secure the data with enhanced encryption by creating a multi-layered IoT security solution. Key stretching is used to provide high resistance to cryptographic attacks. In order to optimize threat detection, Pareto-based selection and dynamic parameter tuning with ML-RBC are used. To improve attack resilience, rank, and tournament selection methods are employed to target DDoS and MMIT attacks with a minimized encryption time. The NSL-KDD IoT network dataset is used to test the proposed CAES-AE with the ML-RBC model using a network simulator. To assess the performance of the proposed work, the results are compared with prevailing IoT data transmission techniques such as ERSA, QoS-BFT, AES, Custom AES, and RBC. Findings: The suggested CAES-AE with ML-RBC data security and transmission method attains the promising results of 97% authentication success rate, 6 seconds response time to security actions, 96% vulnerability detection speed, 97% data encryption strength, 5 seconds incident response time, and 97.8% network performance (energy efficiency & lifetime), which is higher than earlier methods. Novelty: This research provides a secured data transmission method in an IoT environment to address the real-time data transmission challenges faced in sensor-based networks. This will function as a multi-layered IoT security model that optimizes real-time attack detection and resilience. The performance metrics are evaluated with multiple packet transfer rates to boost the network performance. Keywords: Machine Learning, Advanced Networking, Data Security, Secured Data Transmission, Bio­Inspired Algorithm

Optimizing conventional machining process parameters for A713 aluminum alloy using Taguchi method and passive optical network for data transmission

Abstract This study focuses on optimizing machining parameters for A713 aluminum alloy, renowned for its high strength-to-weight ratio but challenging to machine with precision. Using the Taguchi method, key parameters – cutting speed, feed rate, and depth of cut – are optimized to improve surface finish, tool life, and dimensional accuracy. An L9 orthogonal array is applied to enhance experimental efficiency. Furthermore, the integration of Passive Optical Network (PON) technology facilitates real-time data monitoring and transmission, enabling continuous feedback and timely adjustments. This hybrid approach of Taguchi optimization and PON technology achieves superior machining performance, boosting surface quality and operational efficiency. The research highlights how modern data transmission solutions like PON can enhance traditional optimization methods, providing a scalable framework for advanced machining processes in industrial applications.

Comprehensive Study of Ambulance Technology Integration and Advancing Emergency Care and Transportation

Introducing special technical equipment into ambulance services has led to the enhancement of emergency care and transport. Amid this process, this study attempts to uncover both the positive and negative effects, as well as the existing condition, of ambulance innovations on patient well-being, service organization, and EMS quality. GPS navigators and the use of real-time data transmission and telemedicine in moving intensive care units have contributed to relieving response times and better cooperation between emergency service personnel and medical facilities, as well as helping make urgent decisions at the scene. In this paper, an exploration of the technologies deployed in ambulance services, their advantages and disadvantages, and the future of EMS transportation has been done. This also provides an updated analysis of existing trends, emphasizing their consequences in the healthcare sphere worldwide.

Free-Space to SMF Integration and Green to C-Band Conversion Based on PPLN.

In this study, we experimentally demonstrate a PPLN-based free-space to SMF (single-mode fiber) conversion system capable of efficient long-wavelength down-conversion from 518 nm, optimized for minimal loss in highly turbid water, to 1540 nm, which is ideal for low-loss transmission in standard SMF. Leveraging the nonlinear optical properties of periodically poled lithium niobate (PPLN), we achieve a wavelength conversion efficiency of 1.6% through difference frequency generation while maintaining a received optical signal-to-noise ratio of 10.4 dB. Our findings underscore the potential of integrating PPLN-based wavelength conversion with fiber optic networks, offering a viable solution for next-generation optical sensor systems that demand real-time, low-latency, and reliable data transmission. This work represents a significant advancement in developing robust and efficient optical sensor technologies, addressing the challenges associated with long-distance transmission and broad-linewidth light sources in optical remote sensing applications.

Models and Methods of Intrusion Detection in Wireless Sensor Networks of the Tactical Command and Control of Troops

Wireless sensor networks are an important element of modern military operations, providing real-time monitoring and data transmission. However, these networks are vulnerable to both physical and cyber attacks due to limited resources, lack of physical control over the sensors, and challenges associated with using wireless communication channels. The aim of the article is to conduct a comparative analysis of models and methods for intrusion detection in tactical command-level wireless sensor networks. The analysis covers centralized and decentralized security management approaches with a focus on detection models based on signatures, anomalies, and specifications. The article also explores the potential of using hybrid methods that combine the advantages of the aforementioned approaches. Publicly available datasets (KDD, NSL-KDD, WSN-DS) and synthetic datasets generated using network simulators were used to compare the effectiveness of the models. The results show that centralized models are more effective for small networks but create a load on the base station, which can cause delays in attack detection. Decentralized models reduce the load and improve the speed of response to attacks, but they also have their drawbacks. The article notes that none of the existing methods provide complete protection, so a combination of approaches is the most effective solution. Anomaly-based intrusion detection models and methods are classified according to their functional capabilities: statistics-based, data mining-based, machine learning-based, and artificial intelligence-based. The use of artificial neural networks and machine learning significantly improves the accuracy of anomaly detection, but such systems require large computational resources and are complex to configure. The main analytical conclusion of the article is the need to create a hybrid intrusion detection system using artificial neural networks and machine learning, which combines centralized and decentralized methods while considering specific threats to tactical command-level wireless sensor networks. Future research should focus on developing a functional model of an intrusion detection system for the security subsystem in tactical command-level wireless sensor networks.

Mobile all-light communication network

In reality, both mobile and fixed nodes are required in wireless light communication networks. Dynamic maintenance of light alignment plays a key role in mobile full-duplex light communication. Here, we merge an image identification module and a light communication system on a three-axis gimbal stabilizer. A real-time image of the other light communication system obtained by the image identification module is used as a feedback signal to control the three-axis gimbal stabilizer. Therefore, the other light communication system is automatically tracked, and the optical path between the two light communication ends is dynamically maintained, leading to mobile full-duplex light communication under the transmission control protocol/internet protocol (TCP/IP) scheme. Two green light communication apparatuses are separately deployed on two moving vehicles to establish bidirectional light transmission between moving network nodes with a maximum modulation bandwidth of 4 Mbps. Video communication across air and underwater environments is demonstrated, and internet access is illustrated via a Wi-Fi modem. To overcome environmental barriers, we combine mobile green light communication with blue laser communication, deep-ultraviolet light communication, and 850 nm laser diode communication to develop a mobile all-light communication network that enables seamless connectivity across air, land, and underwater environments. Since this network architecture is based on full-duplex communication, all communication nodes have equal and complete mapping characteristics and can facilitate bidirectional real-time data transmission between arbitrary nodes within the network, offering a promising route toward seamless mobile connectivity using light regardless of the environment.

Discrete Event Modeling and Simulation Approaches for IIoT

The industry has experienced significant advancements in recent years, primarily focusing on smart manufacturing, culminating in the Industry 4.0 (I4.0) revolution I4.0 emphasizes interconnectivity, real time data capture and transmission among machines, autonomy, and machine learning, providing manufacturing companies numerous growth opportunities. The Industrial Internet of Things (IIoT) is a core component of this revolution, becoming integral to each system and increasing complexity due to the vast number of interconnected devices and diverse physical components. The variety of virtual services distributed across the architectural layers of industrial systems (cloud, fog, edge) and the various connection types between IIoT devices introduce security and privacy challenges, which are critical issues for any system incorporating IIoT. To fully leverage IIoT’s potential, addressing these security and privacy concerns is essential. Research and design in this domain are challenging, particularly when creating a simulation environment to study a system’s behavior over time. Despite the extensive research in IoT and the significant benefits of simulation based approaches, there remains a challenge in creating detailed representations from the underlying IoT nodes to the application layer in the cloud, along with the underlying networking infrastructure. To assist researchers and practitioners in overcoming these challenges, we propose the Discrete Event System Specification (DEVS) formalism. DEVS provides a mathematical framework for modeling systems, whether discrete or continuous events, allowing for the simulation of these systems within the DEVS environment. Every system, whether real or conceptual, has a time base, inputs, outputs, and functions to determine the next state, as well as outputs that reflect the current state and inputs. Simulating the system within the DEVS environment allows one to study its behavior to predict and optimize performance patterns.

Beyond the Pain Management Clinic: The Role of AI-Integrated Remote Patient Monitoring in Chronic Disease Management - A Narrative Review.

Remote Patient Monitoring (RPM) stands as a pivotal advancement in patient-centered care, offering substantial improvements in the diagnosis, management, and outcomes of chronic conditions. Through the utilization of advanced digital technologies, RPM facilitates the real-time collection and transmission of critical health data, enabling clinicians to make prompt, informed decisions that enhance patient safety and care, particularly within home environments. This narrative review synthesizes evidence from peer-reviewed studies to evaluate the transformative role of RPM, particularly its integration with Artificial Intelligence (AI), in managing chronic conditions such as heart failure, diabetes, and chronic pain. By highlighting advancements in disease-specific RPM applications, the review underscores RPM's versatility and its ability to empower patients through education, shared decision-making, and adherence to therapeutic regimens. The COVID-19 pandemic further emphasized the importance of RPM in ensuring healthcare continuity during systemic disruptions. The integration of AI with RPM has refined these capabilities, enabling personalized, real-time data collection and analysis. While chronic pain management serves as a focal area, the review also examines AI-enhanced RPM applications in cardiology and diabetes. AI-driven systems, such as the NXTSTIM EcoAI™, are highlighted for their potential to revolutionize treatment approaches through continuous monitoring, timely interventions, and improved patient outcomes. This progression from basic wearable devices to sophisticated, AI-driven systems underscores RPM's ability to redefine healthcare delivery, reduce system burdens, and enhance quality of life across multiple chronic conditions. Looking forward, AI-integrated RPM is expected to further refine disease management strategies by offering more personalized and effective treatments. The broader implications, including its applicability to cardiology, diabetes, and pain management, showcase RPM's capacity to deliver automated, data-driven care, thereby reducing healthcare burdens while enhancing patient outcomes and quality of life.

Fingerprint-Authenticated Voting System with Real- Time Data Sharing via Android Application

Abstract: This system presents a novel electronic voting solution tailored for college-level elections, integrating biometric authentication with real-time data transmission. The system is built around an ESP32 microcontroller, a fingerprint sensor for secure user authentication, and an OLED display for interaction. Upon successful authentication, the ESP32 activates Bluetooth to connect seamlessly with an Android application, providing users with a simple interface for voting, polling, or submitting feedback. This system improves security and transparency by ensuring only authorized individuals can vote, while real-time data transmission fosters immediate result processing and accountability. By combining biometric security, ease of use, and instantaneous communication, the system promotes an inclusive and participatory governance environment within educational institutions

Web-Based Environmental Monitoring System Design and Dust Concentration Modeling for Dairy Barns

This study focuses on designing and evaluating a web-based environmental monitoring system for dairy barns, targeting dust concentration and temperature/humidity levels. A prototype device integrating Arduino technology with GSM module was developed to enable real-time data transmission to a web-based platform. The system was tested in a small-scale dairy barn, where it effectively monitored dust, temperature, and humidity while calculating the Temperature Humidity Index (THI). Statistical analysis using multiple regression (MR) and artificial neural networks (ANN) revealed that temperature and humidity significantly influence dust concentrations, with both models achieving strong predictive performance (MR: R² = 0.7, ANN: R² = 0.71). These results indicate that both methods can accurately predict dust levels based on environmental conditions. This low-cost solution not only improves air quality monitoring but also offers a viable tool for controlling heat stress in dairy barns, enhancing animal welfare and productivity. The system's affordability makes it accessible for small family farms and adaptable for broader agricultural applications.