Monitoring Network Research Articles

Transfer learning enhanced convolutional neural network for efficient monitoring of rate-adaptive coherent transmission system

Transfer learning enhanced convolutional neural network for efficient monitoring of rate-adaptive coherent transmission system

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

INTELLIGENT MONITORING OF THE PHYSIOLOGICAL STATE OF AGRICULTURAL PRODUCTS USING UAV

The article discusses the technology for automated neural network monitoring of the vineyard’s physiological condition. Images of leaves, obtained using an unmanned aerial vehicle (UAV), are the main indicator of the physiological vineyard’s condition. The proposed solution is based on the integrated use of convolutional neural network method (CNN) and machine vision technologies. To determine the optimal neural network (NN) model, a variant analysis was carried out. In accordance with its results, the YOLOv7 model was chosen, which satisfies the introduced time limit and provides the required detection quality. The training of the YOLOv7 neural network was implemented in the Python environment using the PyTorch framework and the OpenCV computer vision library. The dataset consisting of 6320 images of grape leaves (including healthy and diseased ones) has been used for neural network training. The obtained results showed that the detection accuracy is at least 91%. Visualization of monitoring results has been carried out using heatmap, allowing to obtain information about vineyard physiological condition in dynamics. The proposed mathematical model allows to calculate the monitored vineyard’s area made by one complex per day. The obtained results showed that effective monitoring area using one DJI Phantom 4 UAV per day is 2.5 hectares.

Three Gorges Seismological Network: Monitoring Seismicity at the Three Gorges Reservoir Forebay

Abstract In this study, we report on a nodal seismic deployment targeting the forebay area of the Three Gorges Reservoir. This deployment aimed to enhance the detection of microearthquakes and refine the imaging of subsurface geological structures. The network’s first phase of operation spanned from 2 September 2020 to 16 November 2020. It consisted of 78 short-period seismometers with an approximate interstation distance of 5 km. Using advanced deep-learning algorithms for phase detection and event location, we identified 1165 seismic events in this period, which exceeds the 623 events identified in the manual catalog using the same dataset. The catalog’s magnitude of completeness is ML ∼−0.3. The experiment from this deployment can offer guidelines for future microseismic monitoring networks, allowing for the strategic design of nodal seismometer spacing and other key parameters to meet specific observational requirements.

A Detailed Inspection of Machine Learning Based Intrusion Detection Systems for Software Defined Networks

The growing use of the Internet of Things (IoT) across a vast number of sectors in our daily life noticeably exposes IoT internet-connected devices, which generate, share, and store sensitive data, to a wide range of cyber threats. Software Defined Networks (SDNs) can play a significant role in enhancing the security of IoT networks against any potential attacks. The goal of the SDN approach to network administration is to enhance network performance and monitoring. This is achieved by allowing more dynamic and programmatically efficient network configuration; hence, simplifying networks through centralized management and control. There are many difficulties for manufacturers to manage the risks associated with evolving technology as the technology itself introduces a variety of vulnerabilities and dangers. Therefore, Intrusion Detection Systems (IDSs) are an essential component for keeping tabs on suspicious behaviors. While IDSs can be implemented with more simplicity due to the centralized view of an SDN, the effectiveness of modern detection methods, which are mainly based on machine learning (ML) or deep learning (DL), is dependent on the quality of the data used in their modeling. Anomaly-based detection systems employed in SDNs have a hard time getting started due to the lack of publicly available data, especially on the data layer. The large majority of existing literature relies on data from conventional networks. This study aims to generate multiple types of Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks over the data plane (Southbound) portion of an SDN implementation. The cutting-edge virtualization technology is used to simulate a real-world environment of Docker Orchestration as a distributed system. The collected dataset contains examples of both benign and suspicious forms of attacks on the data plane of an SDN infrastructure. We also conduct an experimental evaluation of our collected dataset with well-known machine learning-based techniques and statistical measures to prove their usefulness. Both resources we build in this work (the dataset we create and the baseline models we train on it) can be useful for researchers and practitioners working on improving the security of IoT networks by using SDN technologies.

IOT Based Air Quality Monitoring Device

The air quality in the past years has fallen dramatically, creating serious global environmental and health concerns. This project talks about the development of an Internet of Things-based air quality monitoring device that aims to continuously measure and report on certain air quality parameters. This system utilises an Arduino microcontroller as a base for several sensors, including humidity and temperature sensors, particulate matter monitoring sensors, and gas sensors like MQ135. The data uploaded is to a cloud server in real-time on the basis of the present modules of wireless connectivity for easy and remote accessibility to monitor. Apart from that, this technology may be used in the widescale environmental monitoring network while making information easily accessible to the respective user. With the inclusion of IoT, real-time alerts concerning pollutant level can go a long way in enhancing awareness and can help in taking informed decisions that will reduce air pollution. This research solution addresses the critical need in air quality monitoring since it is cheap, scalable, and efficient. Keywords - IoT (Internet of Things), Air Quality Monitoring, Arduino, Sensors, Pollution Detection, Real-time Data, Environmental Monitoring

Improving the sustainability and quality of kidney health care through life cycle assessments, quality improvement, education and technical innovations: the KitNewCare approach.

The European Union (EU)-funded KitNewCare consortium aims to create and manage a comprehensive EU-wide programme focusing on sustainability in Kidney Care. Around 850million people have chronic kidney disease (CKD) worldwide and by 2030, 6million will need kidney replacement therapy, mainly haemodialysis. As the world population gets older, projections for the end of the century worsen. From a sustainability perspective, healthcare systems contribute around 5-11% of total carbon emissions. Kidney care is one of the most resource-intensive specialties. In addition to energy, haemodialysis and peritoneal dialysis require transportation of patients and personnel to and from facilities, use large volumes of water and generate significant plastic waste. Overall, current dialysis is not sustainable in the medium term. Primary prevention, early diagnosis and treatment of CKD and transplantation will decrease the need for dialysis, but this will take time and will not prevent the need for dialysis in millions of persons. There is a need to improve knowledge around the environmental and financial cost of kidney care and social and health outcomes of each patient pathway including using holistic tools such as life cycle assessment. This knowledge will allow workflow optimisations, organisational transformations and technological innovations across Europe, learning from different clinical sites. KitNewCare will build a European-wide knowledge base for sustainability in kidney care, develop and introduce a novel 4-factor database for comprehensive impact analysis, implement optimised processes and organisational transformations in four European clinical sites. It will also pilot innovations from small- and medium-sized high-tech enterprises with a focus on kidney care, and establish a network for continuous monitoring, benchmarking, and implementation of sustainable solutions across healthcare sectors. This paper presents the rationale behind selecting kidney disease as a focal point, summarises the current state of knowledge, and outlines the foundational statement underlying KitNewCare's operational framework.

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

Abstract. We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

A machine learning approach to site groundwater contamination monitoring wells

Effective monitoring of groundwater contamination is crucial to protect human livelihoods and ecosystems. This paper presents a machine learning-based approach to improve groundwater monitoring networks by providing predictions of groundwater contamination in space. The method is demonstrated through a practical application in Central Spain, where nitrate was used as a proxy for groundwater contamination. Predictive mapping identifies the spatial markers for groundwater contamination based on twenty-four predictor variables and a dataset of 213 existing monitoring boreholes. Tree-based algorithms found meaningful associations between the explanatory variables and known nitrate concentrations. Comparing the outcomes of the algorithms with the areas officially delineated as vulnerable to nitrate suggests that machine learning algorithms are able to predict groundwater contamination. The extra trees algorithm outperformed decision trees, random forest, gradient boosting, and AdaBoost classifiers, with an area under the curve score in excess of 0.88. Major predictors for groundwater contamination were depth to the water table, lithology, distance to rivers, and distance to livestock farms. Predictive mapping suggests that there are unmonitored regions to the northeast and to the southwest of Madrid’s metropolitan area that present similar markers to monitored regions known to be contaminated. These unmonitored areas should be prioritized in future attempts to improve the network. From a research perspective, the main conclusion of this work is that machine learning techniques can be used as a technique to automate the siting of monitoring boreholes. Practical applications should nevertheless be overseen by an expert eye to guarantee the quality of the outcomes.

Design Science Approach Comparing Ensemble Learning and Artificial Neural Networks for Uncertainty-Aware Helicopter Turbine Engines Health Monitoring

This work presents the development of an uncertainty-aware health monitoring system for helicopter turbine engines, focusing on improving operational availability and reducing maintenance costs. We address the critical issue of uncertainty quantification in data-driven fault detection and prognostics, essential for increasing system reliability. The project follows an iterative development cycle, incorporating multiple techniques for data processing, such as polynomial feature generation and data cleansing, and model development, including ensemble learning and artificial neural networks. Evaluation is performed using K-fold and group-fold cross-validation. The final solution consists of a cascaded ensemble learning model combining bagged linear regression built on polynomial features and random forest. This model demonstrates robust performance, achieving a test score of 0.955719 and a validation score of 0.886953, showcasing the effectiveness of uncertainty-aware machine learning methods in health monitoring systems.

Multidimensional patterns of bird diversity and its driving forces in the Yangtze river basin of China

Biodiversity is fundamental to human well-being and economic development. The Yangtze River, the largest river in China, faces biodiversity loss due to habitat degradation, climate change, and other anthropogenic threats. However, the long-term changes in the region's biodiversity remain poorly understood. Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. Our findings indicate that a large-scale ecological restoration program has contributed to increases in terrestrial and aquatic bird diversity in the Yangtze River Basin. In contrast, bird diversity in the downstream area has decreased by 2.83 ​%, largely due to a rapid decline in wetland birds. The degradation of wetland habitats and insufficient conservation measures have negatively impacted bird diversity in the downstream region. This suggests that although there have been significant improvements in terrestrial bird diversity, more effective wetland restoration is necessary for biodiversity conservation. We recommend optimizing the national large-scale biodiversity monitoring network and increasing the number of upstream monitoring sites.

Pregnancy, Childbirth, and Postpartum Experiences of Racialised Brazilian Women in Portugal: An Analysis of Obstetric Violence as Gender-Based Violence

As gender-based violence, obstetric violence is a complex phenomenon that represents a matter for debate worldwide. The main objective of this exploratory study is to understand the experiences of obstetric violence during pregnancy, childbirth, and the postpartum period of racialised Brazilian women in the Portuguese National Health System. Using a qualitative case study research approach, semi-structured individual interviews were conducted with ten racialised Brazilian women who gave birth in Portugal. A thematic qualitative analysis was used. The findings reveal significant cases of obstetric violence, which include the complexities and lack of support, assistance and monitoring networks, structural inequalities, neglect, and intersectional discriminatory practices based on racial and cultural prejudices, as well as reflection. These experiences not only affect the physical health of mothers but also have profound psychological and emotional consequences. This study highlights the urgent need for culturally sensitive care and the implementation of policies to prevent obstetric violence, ensuring that the rights and dignity of migrant mothers are upheld. By highlighting these critical issues, this study aims to contribute to the broader discourse on maternal health care and promote social justice for these historically marginalised groups.

Variational spatial-temporal graph attention network for state monitoring and forecasting

With the popularity of smart devices, there has been extensive collection of data structured in graphs. Spatial graphs, in particular, have garnered significant interest owing to their diverse range of applications. In this paper, we focus on applying spatial graph model for state monitoring and forecasting, with special attention to transportation systems. In current spatial modeling approaches, understanding the structure of a spatial graph usually relies on the analysis of gathered spatial–temporal data. However, spatial graph data in real world often contains temporary factors that are not easily detected. In this paper, we propose a novel variational inference-based model VISTG, which integrates such dynamics into spatial–temporal learning of spatial graph modeling. Specifically, VISTG is primarily composed of several spatial–temporal learning blocks, each encompassing both temporal and spatial learning layers. The temporal learning layer is crafted to characterize the distributions of latent factors using a variational inference-based model, aiming to capture the dynamics within the data. Subsequently, the spatial learning layer utilizes graph attention networks to describe the correlation among nodes. Additionally, an adaptive fusion module is implemented to equalize the impact of diverse temporal patterns. Finally, comprehensive experiments are carried out on two real-world datasets. The results affirm the efficacy of our model.

Age and energy aware data collection scheme for urban flood monitoring in uav-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become pivotal in numerous applications, including environmental monitoring, precision agriculture, and disaster response. In the context of urban flood monitoring, utilizing unmanned aerial vehicles (UAVs) presents unique challenges due to the dynamic and unpredictable nature of the environment. The primary challenges involve designing strategies that maximize data collection while minimizing the Age of Information (AoI) to ensure timely and accurate decision-making. Efficient data collection is crucial to capturing all relevant information and providing a comprehensive understanding of flood dynamics. Simultaneously, reducing AoI is essential, as outdated data can lead to delayed or incorrect responses, potentially worsening the situation. Addressing these challenges is critical for the effective use of WSNs in urban flood monitoring. Initially, we formulate the problem as a mixed integer non-linear programming (MINLP) problem. Further, it is solved using a Lagrangian-based branch and bound technique by converting it into an unconstrained problem. Then, for large-scale WSN, we propose a hybrid optimization technique which combines a genetic algorithm with a particle swarm optimization technique to simultaneously maximize the data collection and reduce the AoI of the collected data with the constraint of energy consumption of the UAVs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of both data collection and AoI.

Rain event detection and magnitude estimation during Indian summer monsoon: Comprehensive assessment of gridded precipitation datasets across hydroclimatically diverse regions

Accurate precipitation estimates are quintessential for hydrologic modeling and climate studies. Different gridded precipitation products are available in any region, and selecting the best one is essential for hydroclimatic modeling and analysis. In the current study, observation- (APHRODITE), reanalysis- (IMDAA, ERA5-Land, PGF), satellite-based (IMERG, CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) gridded precipitation products with different spatial and temporal resolutions are evaluated using several continuous, categorical, graphical, and interval-based performance measures towards detecting Indian Summer Monsoon Rainfall (ISMR) events and estimating their magnitudes for the subcontinent of India, considering IMD gauge-based gridded as reference product. We confine our analysis to the monsoon season (i.e., June to September), the principal rainy season in the Indian sub-continent. The dearth of data and limited rain gauge-based observations from non-uniform sparse monitoring networks across India necessitated grid-to-grid comparative evaluations instead of point-to-grid assessments. We propose a new ranking framework to determine the suitability of precipitation datasets for twenty-seven hydroclimatic regions comprising homogeneous rainfall zones, Köppen-Geiger climate zones, and major river basins. Results from the comprehensive evaluation suggest that (1) APHRODITE, MSWEP, and ERA5-Land best approximate precipitation event occurrences across India, (2) MSWEP and ERA5-Land are most suitable (highest rank) alternatives at the regional level, while APHRODITE is found to be next suitable dataset owing to its persistent dry bias, (3) performances of CHIRPS and IMERG have reasonably lower rank score across India, (4) close agreement of examined datasets is noted over semi-arid and sub-humid regions (e.g., peninsular and central India), whereas ERA5-Land, IMDAA, and APHRODITE fail to detect and reproduce the intensity of the events along the west coast and northeastern India, (5) PGF and PERSIANN-CDR are the least situated datasets. Moreover, the present study provides a unique and innovative perspective to characterise the precipitation over a vast topographic, ecologic, and climatic gradient region like India.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Information-theoretic sensor placement for large sewer networks

Utility operators face a challenging task in managing wastewater networks to proactively enhance network monitoring. To address this issue, this paper develops a framework for optimized placing of sensors in sewer networks with the aim of maximizing the information obtained about the state of the network. To that end, mutual information is proposed as a measure of the evidence acquired about the state of the network by the placed sensors. The problem formulation leverages a stochastic description of the network states to analytically characterize the mutual information in the system and pose the sensor placement problem. To circumvent the combinatorial problem that arises in the placement configurations, we propose a new algorithm coined the one-step modified greedy algorithm, which employs the greedy heuristic for all possible initial sensor placements. This algorithm enables further exploration of solutions outside the initial greedy solution within a computationally tractable time. The algorithm is applied to two real sewer networks, the first is a sewer network in the South of England with 479 nodes and 567 links, and the second is the sewer network in Bellinge, a village in Denmark that contains 1020 nodes and 1015 links. Sensor placements from the modified greedy algorithm are validated by comparing their performance in estimating unmonitored locations against other heuristic placements using linear and neural network models. Results show the one-step modified greedy placements outperform others in most cases and tend to cluster sensors for efficiently monitoring parts of the network. The proposed framework and modified greedy algorithm provide wastewater utility operators with a sensor placement method that enables them to design the data acquisition and monitoring infrastructure for large networks.

Monitoring the edges of product networks using distances

Foucaud et al. recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. Let G be a graph with vertex set V(G) and edge set E(G). For any subset M in V(G) and an edge e in E(G), let P(M,e) be the set of pairs (x,y) such that dG(x,y)≠dG−e(x,y) where x∈M and y∈V(G). M is called a distance-edge-monitoring set if every edge e of G is monitored by some vertex of M, that is, the set P(M,e) is nonempty. The distance-edge-monitoring number of G, denoted by dem(G), is defined as the smallest size of distance-edge-monitoring sets of G. For two graphs G,H of order m,n, respectively, in this paper, we prove that max⁡{mdem(H),ndem(G)}≤dem(G□H)≤mdem(H)+ndem(G)−dem(G)dem(H), where □ is the Cartesian product operation. Moreover, we characterize the networks attaining the upper and lower bounds and show their applications on some known networks. We also obtain the distance-edge-monitoring numbers of join, corona, cluster, and some specific networks.

Disentangling hydrodynamic drivers of the Southern Venice (Italy) coastal aquifer via frequency decomposition analysis: Insights, challenges, and limitations

Study regionShallow coastal aquifer located in the southern part of the Venice lagoon (Italy). Study focusThis study aims to improve the understanding of coastal aquifers’ hydrodynamics by implementing systematic time-series analyses of data. A collection of non-intrinsically consistent time series from hydrological (surface water and groundwater) and meteo-mareographic monitoring networks was obtained from different institutions. Each signal was broken down through a frequency decomposition analysis, isolating the main driving forces to focus on phenomena that occur at different time and spatial scales. New hydrological insights for the regionResults highlighted that the aquifer is highly connected with the Venice lagoon, with a clear fluctuation of piezometric heads induced by tidal major constituents, decreasing landward. Besides, the effects exerted by reclamation canals and pumping stations were also determined and found to increase landward. Despite the relatively simple behaviour of piezometric heads, the groundwater salinity is influenced by additional local factors, like probe depth, wells’ screen length, and vertical salinity distribution along the aquifer. These findings suggested how to make use of limited and sparse data to enhance the conceptual model of coastal aquifer hydrodynamics, while highlighting the limitations of existing monitoring networks. This outcome justified the need for an intrinsically-consistent network of dedicated multi-level samplers to avoid intra-borehole mixing and reliably characterize the groundwater salinity distribution.

DeepWhaleNet: Climate Change-Aware FFT-Based Deep Neural Network for Passive Acoustic Monitoring

Climate change poses severe risks to the survival of many whale populations, whose habitats and migration patterns are affected by environmental changes. To detect these whales effectively, especially in deep-sea environments, we need to use AI-based techniques to handle the acoustic diversity and variability of different species. However, current methods for whale detection are based on pre- and post-processing steps that reduce their efficiency and generalizability. To address this issue, we present DeepWhaleNet, a novel deep-learning model that automates whale detection in Underwater Passive Acoustic Monitoring datasets. DeepWhaleNet simplifies the detection process by extracting relevant features from raw log-power spectrograms and helps protect these threatened species by supporting conservation efforts. Our model uses a larger short-time Fourier transform as input and a custom ResNet-18 architecture for classification, which enables it to separate whale sounds from noise and capture their temporal and spectral characteristics. We evaluate the performance of DeepWhaleNet and show that it surpasses state-of-the-art methods, achieving an 8.3% improvement in the F-1 score and 21% higher average precision of binary relevance than the baseline method. Moreover, our model demonstrates its versatility and suitability for species-specific retrieval problems through an ablation study on multi-label retrieval problems and a 99.1% recall for Blue Whales.