Real-time Monitoring Network Research Articles

Spatio-Temporal Correlation Between Radon Emissions and Seismic Activity: An Example Based on the Vrancea Region (Romania)

Radon gas anomalies have been investigated as potential earthquake precursors for many years. In this work, we have studied the possible correlations between radon emissions and the seismic activity rate for a given region to test if the existing correlation may be later used to forecast the occurrence of earthquakes larger than a given magnitude. The Vrancea region (Romania) was chosen as a study area since it is being surveilled by a multidisciplinary real-time monitoring network, and at least seven earthquakes with magnitudes greater than 4.5 Mw have occurred in this area in the period from 2016 to 2020. Our research followed several steps: First, the recorded radon signals were preprocessed (detrended, deseasoned and smoothed). Then, the station’s signals were correlated in order to check which stations are recording radon anomalies due to the same regional tectonic process. On the other hand, the seismic activity rate was computed using the earthquakes in the main catalogue of the region that are able to generate radon emissions and can be registered at several stations. The obtained results indicate a significant correlation between the seismic activity rate and the temporal series of radon anomalies. A temporal lag between the seismic activity rate and the radon anomalies was found, which can be related to the proximity to the epicentre of the main earthquake in each of the studied subperiods. Changes in the regional tectonic stress field could explain why the seismic activity rate and radon anomalies are correlated over time. Further research could focus on obtaining a function to forecast the seismic activity rate using the following as dependent variables: the radon anomalies recorded at several stations, the distance from the stations, and tectonic factors such as the fault system, azimuth, type of soil, etc.

A novel physical information neural network for real-time monitoring and sparse reconstruction of thermal environments with turbulent natural convection in nacelles

A novel physical information neural network for real-time monitoring and sparse reconstruction of thermal environments with turbulent natural convection in nacelles

Fuzzy-based driver monitoring system

This paper presents an all-around approach to enhancing road safety through a fuzzy driver tracking system integrated with VANET technology, IoT devices, and hardware wristbands. The increasing prevalence of road accidents is caused by factors such as driver fatigue, health disorders, or inattention to this system, which effectively detects and responds to such situations and provides real-time solutions. It comes with advanced sensors The hardware wristband continuously monitors the driver's pulse, like heart rate. Body temperature and movement Patterns for identifying signs of sleepiness abnormal health parameters or erratic behavior are processed using fuzzy logic algorithms. The in-car software interface gets real-time information from the wristband, processes it, and unlocks several safety protocols, which include visual notifications, buzzing alerts, and communication with other vehicles through VANET technology. The system also logs highly detailed records of incidents it detects, which will be analyzed later for long-term behavioral profiling. It will leverage the power of IoT-enabled cloud storage to optimize data management and analysis, thus fostering continuous improvement in traffic safety protocols. The integration of fuzzy logic ensures accurate and context-sensitive decision-making, addressing uncertainties inherent in dynamic driving environments. Meanwhile, the utilization of VANET enables seamless communication between vehicles, enhancing collective road safety. This paper provides an in-depth exploration of the system's architecture, design principles, algorithms, implementation strategies, and expected outcomes. It has several aspects, thereby changing the face of drivers' safety through reduced accidents, faster response mechanisms to accidents at a given time, and thus a safer ecosystem of transport for all other road users. This system represents a crucial step forward in intelligent transportation technologies. Combining the features of real-time monitoring, predictive analytics, and robust communication networks, the solution not only mitigates short-term risks but also works toward long-term safety improvement. The modular design adapts to different vehicle types and driving environments, making this a scalable solution for widespread adoption. This paper outlines the capacity of the system to transform traditional road safety mechanisms into proactive and intelligent ones. Index Terms—Internet of Things (IoT), secrecy enhancing, smart cities, space shift keying (SSK).

Optimizing Agricultural Production Through the Internet of Things: Innovative Solutions to Reduce Food Loss and Waste

Significant food loss and waste along agricultural supply chains is a global challenge that threatens food security and environmental sustainability. This research aims to explore the potential of Internet of Things (IoT) technology as an innovative solution to overcome these problems. Through an interdisciplinary approach that combines agriculture, information technology and sustainability, this research develops an integrated Internet of Things (IoT) system that can efficiently monitor, analyze and manage the entire agricultural supply chain process. The proposed Internet of Things (IoT) solution includes a wireless sensor network for real-time monitoring of environmental conditions, crop growth, and activity on farmland, as well as a cloud-based analytics platform to process data and provide optimal recommendations. In addition, this system also allows food product traceability and information transparency along the supply chain. Respondents who are agricultural managers have a positive perception of the use of Internet of Things (IoT) technology in their agricultural activities. The research instrument (questionnaire) used was also proven to be valid and reliable in measuring related variables. Thus, this research concludes that the application of Internet of Things (IoT) technology in smart farming has great potential to optimize input use, increase crop productivity, and significantly reduce food loss and waste through better monitoring and control along the agricultural supply chain.

Learning to Sketch: A Neural Approach to Item Frequency Estimation in Streaming Data.

Recently, there has been a trend of designing neural data structures to go beyond handcrafted data structures by leveraging patterns of data distributions for better accuracy and adaptivity. Sketches are widely used data structures in real-time web analysis, network monitoring, and self-driving to estimate item frequencies of data streams within limited space. However, existing sketches have not fully exploited the patterns of the data stream distributions, making it challenging to tightly couple them with neural networks that excel at memorizing pattern information. Starting from the premise, we envision a pure neural data structure as a base sketch, which we term the meta-sketch, to reinvent the base structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase and can be quickly adapted to real (skewed) distributions in the adaption phase. The meta-sketch not only surpasses its competitors in sketching conventional data streams but also holds good potential in supporting more complex streaming data, such as multimedia and graph stream scenarios. Extensive experiments demonstrate the superiority of the meta-sketch and offer insights into its working mechanism.

Assessing 7-year heat-stress exposures and adaptation strategies for children using a real-time monitoring network in Taiwan

Wet-bulb globe temperature (WBGT) serves as a suitable heat-stress indicator not only for outdoor workers but also for the general public. However, studies on WBGT exposure among the general population are scarce. This research represents the first attempt to assess WBGT exposure of school-aged children. Utilizing a real-time monitoring network in Taiwan, WBGT exposure of school-aged children (7–15 years) were estimated during May to October from 2016 to 2022. Important determinants and spatiotemporal variability of WBGT levels were explored, with hot spots and peak hours of WBGT identified. Macro- and micro-scale adaptation strategies applicable at schools were also evaluated for their effectiveness in reducing heat stress for students. Results showed that the mean daily maximum WBGT (WBGTmax) was 33.1 ± 3.8 °C at 20 stations across Taiwan but could reach/exceed 36 °C (threshold of the dangerous category) at certain hot spots for 42.3–52.0 % of days between May and October. Local geographic features sometimes outweigh the latitude in explaining the spatial variations. Contrary to temperature, WBGT peaked during 10 am to noon rather than from noon to 1:59 pm in most schools, due to clouds blocking solar radiation in the afternoon. This finding has significant implications for scheduling outdoor physical classes/activities to reduce children's heat-health risks. Setting up on-site WBGT monitoring on surfaces that children mostly encounter at schools or utilizing data from nearby weather stations could provide a near real-time heat-health warning. Moreover, providing shades outdoors, relocating outdoor classes indoors, and using air-conditioning would reduce WBGT by 2.1–5.8, 3.7–7.3, and 2.5–5.9 °C, respectively; and would significantly decrease the percentages of WBGT ≥34 °C, which is associated with increased heat-related emergency visits among children in Taiwan. The methodology applied serves as a useful reference for assessing WBGT exposure and adaptation strategies, providing the scientific foundation for heat-health adaptation measures.

Pipeline leakage identification method based on DPR-net and distributed optical fiber acoustic sensing technology

Real-time monitoring of natural gas pipeline network can ensure the life and health of human beings and property safety in the surrounding area of the pipeline. Therefore, how to realize the rapid identification of pipeline leakage signal when leakage occurs is extremely important. Distributed acoustic sensing (DAS) technology is widely used in the field of pipeline leakage monitoring because of its advantages of wide coverage and high sensitivity. In this paper, a pipeline leak identification method based on DAS and one dimensional DPR-net (DAS pipeline leakage recognition network) are proposed. Pipeline leakage signal samples are collected under different working conditions (leakage aperture, leakage pressure, leakage direction). In test, the recognition accuracy of this method reached over 99%. There are reasons to believe that this method will provide an important technical means for DAS to detect pipeline leakage.

SNW YOLOv8: improving the YOLOv8 network for real-time monitoring of lump coal

Abstract Due to the large size of the coal and the high mining output, lump coal is one of the hidden risks of mining conveyor damage. Typically, lump coal can cause jamming and even damage to the conveyor belt during the coal mining and transportation process. This study proposes a novel real-time detection method for lump coal on a conveyor belt. The Space-to-Depth Conv (SPD-Conv) module is introduced into the feature extraction network to extract the features of the mine's low-resolution lump coal. To enhance the feature extraction capability of the model, the Normalization-based Attention Module (NAM) is combined to adjust weight sparsity. After loss function optimization using the Wise-IoU v3 (WIoU v3) module, the SPD-Conv-NAM-WIoU v3 YOLOv8 (SNW YOLO v8) model is proposed. The experimental results show that the SNW YOLOv8 model outperforms the widely used model (YOLOv8) in terms of precision and recall by 15.82% and 11.71%, respectively. Significantly, the real-time detection speed of the SNW YOLOv8 model is increased to 192.93 f/s. Compared to normal models, the SNW YOLO v8 model overcomes the disadvantages of normal models, such as being overweight, and the parameters of SNW YOLO v8 are reduced to only 6.04 million with a small model volume of 12.3 MB. Meanwhile, the floating point of SNW YOLOv8 is significantly reduced. Consequently, it demonstrates excellent lump coal detection performance, which may open up a new window for coal mining optimization.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from 0.33 to 33.77%, and most of them were 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

HxPINN: A hypernetwork-based physics-informed neural network for real-time monitoring of an industrial heat exchanger

The importance of heat exchangers in achieving sustainability goals has significantly increased across industries, given their crucial role in maintaining energy efficiency. However, lack of effective monitoring hardware for industrial heat exchangers often leads to suboptimal performance and unplanned stoppages. Physics-based numerical solvers, while accurate, are often computationally intensive and require frequent adjustments for monitoring. On the other hand, machine learning or data-driven models face a stiff challenge as they rely exclusively on data which is scarce due to lack of sensors. We present HxPINN, a hypernetwork-based physics-informed neural network (PINN) model for real-time monitoring of an industrial heat exchanger. Our approach incorporates a hypernetwork-based framework, enhancing a domain-decomposed PINN to learn the heat exchanger’s thermal behavior under dynamic operating conditions. HxPINN minimizes the need for retraining PINNs for different operating conditions and significantly reduces the inference time compared to existing approaches, without significant loss of accuracy vis-à-vis physics-based simulations. The methodology was tested on an Air Preheater (APH), a regenerative heat exchanger used in thermal power plants. The mean absolute error in APH internal temperatures with HxPINN when compared with benchmark physics-based simulation was found to be well below 5 °C across all the operating conditions. HxPINN has also accelerated the inference by up to 35–40x compared to existing approaches including transfer learnt PINNs and physics-based numerical solution. The methodology holds great potential for predictive maintenance of industrial equipment using digital twin systems providing efficient, adaptable, and real-time monitoring capabilities.

An adaptive RFID anti-collision algorithm for network intrusion detection

Radio frequency identification (RFID) provides real-time network monitoring capabilities for threat identification. However, accurate detection is impeded by tag interference. This paper presents an adaptive collision tree algorithm that selects optimal binary or octal splits based on collision counts to handle interference. Experiments demonstrate an integrated RFID intrusion detection framework that achieves 8.98% higher throughput and 99.82% detection accuracy compared to other protocols. The method enables efficient real-time threat identification as networks proliferate. However, there are limitations to the approach, such as assumptions of fixed tag populations rather than dynamic tags and a lack of field testing. To strengthen the approach, further research on fluctuating tags and validation in real-world network deployments is necessary. This work presents an adaptive method for leveraging RFID to achieve scalable and accurate network intrusion detection.

Analysing Smart Power Grid against different Cyber Attacks on SCADA System

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system. A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid and also includes two different cyber-attack scenarios. Index Terms- co-simulation; smart power grid; SCADA system; cyber-attack scenarios.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor.

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Rancang Bangun Alat Pengendali Suhu pada Proses Pasteurisasi Susu Murni Menggunakan Arduino Berbasis IoT

This research aims to design and develop a temperature control device for the pasteurization process of pure milk using Arduino based on the Internet of Things (IoT). The device is designed to monitor and regulate the temperature automatically during the pasteurization process to ensure its quality and safety. Arduino, as the main platform, is used to collect temperature data through temperature sensors that are directly connected to the system. The temperature data is then sent to a server through the internet network for real-time processing and monitoring. Intelligent temperature control algorithms are implemented in Arduino to adjust heating or cooling during the pasteurization process according to the set temperature point. The results of this research are expected to provide an efficient and accurate solution for temperature control in the pasteurization process of pure milk, as well as improve the overall safety and quality of dairy products.

Development and Deployment of a Virtual Water Gauge System Utilizing the ResNet-50 Convolutional Neural Network for Real-Time River Water Level Monitoring: A Case Study of the Keelung River in Taiwan

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies.

Recent radiation protection activities related to nuclear facilities on the Iberian Peninsula

Part of the radiation protection in Spain and Portugal is focused on the protection of workers, public and environment due to the influence of nuclear facilities present in their territory (Spanish case) or nearby. Radiation protection is in a process of continuous improvement, with the participation of nuclear facilities, companies and researchers in numerous research projects funded in Spain by the Nuclear Safety Council or other international organizations. This article presents some of the projects in which Spanish working groups have participated. In addition, specific examples of activities related to radiation protection and environmental radiological monitoring carried out in Spain in recent years are shown. Among them are: the development of methods of analysis of environmental samples in case of emergency, real-time monitoring networks for measuring radiation, environmental dosimetry in the environment of nuclear facilities, programs and models of radiation dispersion in case of accident, and the treatment and management of radioactive waste for its clearance.

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods.

SEADETECT: developing an automated detection system to reduce whale-vessel collision risk

With the continuous intensification of marine traffic worldwide, whale-vessel collisions at sea (or “ship strikes”) have become one of the primary causes of mortality for cetaceans and a widely recognised cause of concern for human safety and economic losses. The Mediterranean Sea is a global hotspot for whale-vessel collisions, with one of the highest rates involving large cetaceans, especially the endangered fin whales (Balaenoptera physalus) and sperm whales (Physeter macrocephalus). Evidence indicates that both species are experiencing higher chances of a fatal collision than what predictions have estimated so far, with ship strikes being the main human-induced threat in the area. Regional and international organisations have stressed the need to address the issue by investigating the projected impacts of ship strikes on whale populations and by identifying possible mitigation measures to reduce chances of collision. Amongst the most popular and feasible options, there is the improvement of animal detection during navigation. Here, we present SEADETECT, a LIFE project that aims at developing an automated detection system to reduce vessel collision risk with marine mammals and unidentified floating objects (UFOs), combining state-of-the-art and novel technologies with existing approaches in the study of large whale ecology. This detection system consists of three elements; an automated onboard detection system composed of several sensors, a real-time passive acoustic monitoring (PAM) network at sea and a real-time detection-sharing and alert system (REPCET®). In this paper, we propose the development of a mitigation measure framework tailored for the issue of collision with fin and sperm whales in the north-western Mediterranean Sea, but that has the transferability features necessary for its application in other high-risk areas for ship strikes worldwide.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Bayesian Regularization (BR) Optimization Based DDoS Detection for SDN and Next Generation Communication Network

The rapid evolution of communication networks, particularly Software-Defined Networking (SDN) and next-generation communication infrastructures, has introduced new challenges in securing these dynamic and complex environments. Among the most persistent threats are Distributed Denial of Service (DDoS) attacks, which can disrupt critical services and inflict severe economic and operational damages. To combat these threats, novel and adaptive DDoS detection mechanisms are crucial. This paper proposes a Bayesian Regularization (BR) optimization-based approach for DDoS detection in SDN and next-generation communication networks. Bayesian Regularization is a statistical technique that combines the strength of Bayesian analysis with optimization methodologies, enabling the model to adapt to changing network conditions and attack strategies. This approach leverages the inherent advantages of SDN, such as centralized control and real-time network monitoring, to enhance the accuracy and timeliness of DDoS detection.