Monitoring Nodes Research Articles

Sensor placement method for water distribution networks based on sampling of non-bandlimited graph signals

Monitoring water distribution networks (WDNs) requires careful consideration of sensor placement, which is essential for obtaining comprehensive information about the network. A natural graphical structure underlies WDN, making graph sampling theory advantageous for selecting monitoring nodes. However, graph sampling theory is only applied only to restrictive band-limited signals, while the pressure data of WDN is a restrictive non-band-limited signal. To address this issue, this paper presents an approximate conversion method for transforming non-band-limited signals into band-limited signals, accompanied by an optimal spectrum threshold formula. This formula is used to perform spectral screening in the graph frequency domain, effectively converting non-band-limited signals into band-limited signals that preserve the major frequency components while ignoring smaller-value frequency components. By sampling band-limited signal, we identify sampling nodes that perfectly recover the signal. These sampling nodes act as monitoring nodes that can perform a comprehensive inspection of the WDN and accurately locate leaks. The accuracy of our method in recovering the signal and locating the leak is demonstrated by comparing it with two existing sensor placement optimization methods.

DewROS2: A platform for informed Dew Robotics in ROS

With the shift from Cloud to Fog and Dew Robotics a lot of emphasis of the research community has been devoted to task offloading. Effective and efficient resource monitoring is however necessary for such offloading and it is also fundamental for other important safety and security tasks. Despite this, robot monitoring has received little attention in general and also for Robot Operating System (ROS) the most employed framework in robotics. In this paper DewROS2 is presented, a platform for Dew Robotics that comprises entities to monitor the system status and to share it with interested applications. The design and implementation of the platform is presented together with the monitoring entities created. DewROS2 has been deployed on different real devices, including an unmanned aerial vehicle and an industrial router, to move from theory to practice and to analyze the impact of monitoring on robot resources. DewROS2 has also been tested in a search and rescue use case where robots are used to collect and transmit videos to spot signs of humans in trouble. Results in controlled and uncontrolled conditions show that the monitoring nodes do not have a significant impact on the performance while providing important and measurable benefits to the applications. Accurately monitoring of robot resources, for example, allows the search and rescue application to almost double the utilization of the network, therefore collecting video at a much higher resolution.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

Low-Cost, Low-Power Edge Computing System for Structural Health Monitoring in an IoT Framework.

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

Cooperative task allocation method for air-sea heterogeneous unmanned system with an application to ocean environment information monitoring

Ocean environment information monitoring is crucial to maritime battlefield situation awareness, marine disaster prevention, and marine resources utilization. However, current monitoring means, including remote sensing satellites, scientific investigation ships, and ocean observation stations, have limitations in terms of spatial coverage, spatiotemporal resolution, and manpower resources. Therefore, this study proposed the Air-Sea heterogeneous unmanned system composed of UAVs and USVs to carry out ocean environment information monitoring tasks.) Firstly, an algorithm named FASO (Firefly Algorithm Based Particle Swarm Optimization) is proposed to obtain the deployment of monitoring nodes in the sea-surface and airspace. Secondly, a multi-objective optimization cooperative task allocation model is established, which contains complex constraints and takes the energy consumption cost of UAVs and USVs as optimization goals. Finally, a cooperative task allocation method based on the improved NSGA-II algorithm is presented to enhance the convergence speed and quality of the cooperative task allocation for air-sea heterogeneous unmanned system. Simulation results demonstrate that the proposed algorithm have higher generality and superiority compared to other algorithms. This study provides a specific approach for the application of heterogeneous unmanned systems in the field of ocean monitoring.

Development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements

The article continues to review the approach to designing the architecture of a distributed information monitoring system and quality management of communication services provided by the infrastructures of the Internet of Things and the industrial Internet of Things, based on solutions that support machine-to-machine and human-machine interaction. The development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements is proposed. The matrix structure of the LSTM network memory cell is proposed, which takes into account the spatio-temporal correlation of load parameters associated with the time lag of its propagation and is a matrix of connectivity of LSTM network hyperparameters and accumulated values of load parameters of monitoring nodes in the vicinity of a controlled monitoring node, taking into account the characteristics of the time series of propagation of load in stationarity moments.

Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

Malicious Code Propagation Model Based on Sleep Monitoring Mechanism in Wireless Sensor Networks

<p>Wireless sensor networks (WSNs) are characterized by high node density and finite energy storage. Each node exchanges information with all its neighboring nodes frequently, which makes it easy for nodes to run out of energy, leading to paralysis of the WSNs when facing network malicious code attacks. To address this problem, a malicious code propagation model based on sleep-monitoring technology for WSNs is proposed. As a Multi-compartment propagation, sleep nodes and monitoring nodes are introduced to the conventional SIR model. Sleep nodes can turn the infected node into a dormant state and stop the dissemination of information to save energy. Monitoring nodes can contain malicious codes spread in wireless sensor networks by sharing prevention information in real time. Additionally, by calculating the equilibrium point and propagation threshold of the new feedback model, the corresponding Lyapunov function is constructed to prove the local stability and global stability of the equilibrium point. Finally, the results of numerical simulation experiments show that when the sleep rate is 0.3 and feedback rate is 0.0000005, the number of infected nodes in the wireless sensor network decreases by 43.45%. Therefore, adding sleep-monitoring technology can effectively control the spread of malicious code in the networks.</p> <p> </p>

A New Embedded Condition Monitoring Node for the Idler Roller of Belt Conveyor

A New Embedded Condition Monitoring Node for the Idler Roller of Belt Conveyor

Stealing complex network attack detection method considering security situation awareness.

Tracking and detection have brought great challenges to network security. Therefore, this paper proposes a monitoring method of stealthy complex network attacks considering security situation awareness. By constructing a tracking model of invisible complex network attacks, public monitoring nodes are selected for monitoring. The cost of a single monitoring node is calculated by the algorithm, and the monitoring node is determined by the monitoring node algorithm, so as to reduce the resource occupancy rate of the monitoring node and improve the monitoring accuracy. The simulation results show that this method is stable in the range of 1000 to 4000 nodes, and can effectively monitor the complex network attacks of stealing secrets.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Cooperative-hydrophone-based underwater soundscape monitoring in a bustling harbor enhanced by coral ecosystem

Anthropogenic underwater noise is an emerging pollutant, urging a focus on studying the spatial and temporal dynamics of underwater soundscapes for effective environmental protection. This study centers on a bustling harbor enriched by coral ecosystems, employing 5–8 spatially distributed bottom-mounted passive acoustic monitoring (PAM) nodes strategically placed at locations of interest with diverse natural conditions and ship traffic intensities. Focused on the intricate interplay between anthropogenic and natural contributors, including shipping traffic and coral ecosystems, one-third octave band sound pressure levels SPLs were calculated and analyzed at different frequencies, revealing significant variations in levels across different times and locations. Diurnal variations in ambient noise levels highlight the complex relationship between human activities and marine ecosystems. Higher and more variable SPLs, primarily related to shipping traffic, were recorded during the daytime, while lower SPLs, accompanied by increased fish sounds, were observed at night. The collaborative deployment of multiple hydrophones enables an estimation of noise source direction, and multi-hydrophone based principal component analysis (PCA) for dimensionality reduction and subsequent clustering methods were used for noise source classification. This study significantly contributes to the field of underwater noise monitoring, offering a holistic perspective on busy harbor environments.

A conceptual CO2 fill-and-spill mega-fairway in the UK Southern North Sea: A new approach to identify and optimise large-scale underground carbon storage (CCS)

This work explores the subsurface CO2 storage potential in UKCS Quadrants 43–44, by integrating 3D seismic interpretation, regional wellbore data and CO2 migration modelling. We show 11 Bunter Sandstone domes linked together in a fill-and-spill “Bunter Mega-Fairway”. These traps host a theoretical storage capacity of 8.4 Gt CO2 (≈3–4 times the filled-to-spill theoretical capacity of the proposed Endurance storage site), while lessening CO2 leakage risks.Within the mega-fairway, the Triassic Bunter Sandstone reservoir shows a P10-P90 thickness of 78–219 m, along with moderate-high porosities (11–28 %) and permeabilities (9–669 mD). A regionally-connected reservoir is suggested from spatially consistent, near-hydrostatic aquifer pressure gradients (≈0.51 psi/ft). The top-seal is laterally effective and structural closures are lower than the CO2 columns necessary to breach the seal.At the mega-fairway storage depths, CO2 would inhabit the reservoir as a supercritical fluid, enabling it to naturally migrate buoyantly from one trap to another, away from strategically-placed injector sites. Through the integration of migration spill-point modelling, multi-physics seabed monitoring nodes and fill-and-spill injection management, the maximum storage capacity could be achieved, while alleviating CO2 leakage hazards and potentially decreasing the number of injector wells.Exploiting fill-and-spill fairways for CCS is a new concept with vast worldwide applicability.

Cascading Propagation Path of Vinyl Chloride Process Risk Based on Complex Network

In the production of vinyl chloride, small parameter fluctuations may lead to large-scale cascade fluctuations, causing serious economic losses and casualties, in order to ensure the safety and stability of production, it is necessary to study the cascade fluctuation propagation path of vinyl chloride production process. In this paper, a complex network model of vinyl chloride production process is constructed based on complex network theory, and the concept of comprehensive degree considering network direction is introduced to identify important nodes in the network: secondly, the fluctuation overload propagation probability and material hazard degree of the edge are used to define the fluctuation overload propagation intensity of the edge; finally, according to the fluctuation overload propagation intensity, the risk propagation path of cascading fluctuations under different overload modes is obtained by using ant colony algorithm, it provides a basis for the prevention of cascade overload and the selection and protection of key monitoring nodes.

Internet of Things (IoT)-Driven on Evaporative Cooling System for Tropical Greenhouse Environmental Control

The impact of climate change on tropical agriculture can be mitigated by using controlled environmental conditions in tropical greenhouse buildings with evaporative cooling. Precision agriculture can be applied by adopting technology based on the Internet of Things (IoT) with easy access and real-time monitoring. A study has been conducted to design and manufacture an IoT-based evaporative cooling control system for cultivating horticultural crops in tropical greenhouses. The system consists of an environmental monitoring node and an air cooler actuator control node. Data temperature, relative humidity, and the response of the control actuator can be monitored in real-time via a cloud server. The study also discusses Vapor Pressure Deficit (VPD) as an important factor that needs to be considered in controlling greenhouse environmental conditions. The study used a linear regression test, validation test, analysis of VPD, calculation of the accuracy of the evaporative cooler, as well as evaluation of packet loss.

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Abstract For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

Study on the Mechanism of Safety Risk Propagation in Subway Construction Projects

Under the development trend of complexity and systematization of metro construction, there is an increasing number of risk factors potentially affecting construction safety, which has led to frequent accidents in metro construction projects, and the road to high-quality and sustainable development of metro construction is full of challenges. One of the essential reasons is that the propagation mechanism of safety risk factors in metro construction under hidden and delayed effects is not yet clear. This paper combines the theory of complex network and propagation dynamics and constructs a subway construction safety risk propagation model based on considering the hidden and delayed characteristics of construction safety risk propagation, which reveals the dynamic propagation law of subway construction safety risk and puts forward feasible coping strategies. The findings evince that the delay time T significantly affects the propagation behavior of risk and the achievement of the equilibrium state in the network. The transmissibility of the risk factor within the hidden state holds a pivotal sway over the entirety of risk propagation, and the latency in transmission significantly expedites the propagation of risk throughout the network. It is recommended that project managers monitor and warn safety state nodes and hidden state nodes to block the spread of risk in the network and control the delay time of risk in the network in time to reduce the probability of risk occurrence. This study significantly promotes the resilient management of safety risks in metro construction.

Cyber Physical System For Autometed Weather Station And Agriculture Node In Smart Farming

The Cyber-Physical System is a key system in the implementation of the 4th generation industrial revolution. This system combines automation systems, electronics, internet networks and machine learning. The implementation of physical cyber systems in agriculture is one of the long-awaited applications because it is the backbone of the implementation of sustainable agribusiness systems. With this system precision agriculture and pervasive computing in agriculture can help stakeholders in agribusiness to enjoy various benefits optimally. One example of its application is a smart farming system that is conditioned to provide measurable and maximum yields without having to sacrifice soil nutrients because it is well monitored according to weather conditions. In previous studies, several LoRa communication-based systems for monitoring local weather in a place and measurements of soil nutrient conditions have been carried out and displayed through the internet network. Monitoring and control systems on other agricultural models based on hydroponics and aquaponics have also been carried out for some types of crops. The results provide greater potential for wider application and in actual conditions in the agricultural industry. In this study, the integration of physical cyber systems with lora communication-based agricultural monitoring and control nodes will be carried out more broadly by considering the local conditions under which the system is tested. In this case, the research will be carried out at the Research and Recreation Park, Telkom University and agrifarming industry partners as a model of actual application. The research findings show that the application of advanced sensor technology has improved the accuracy of weather measurements by up to 95%. Quantitative data collected from the new weather station showed a significant improvement in weather monitoring. Qualitatively, positive responses from stakeholders such as disaster management authorities and farmers were also noted. In conclusion, the development of this modern weather station supports the community's need for reliable weather information and is a step forward in addressing the challenges of climate change.

A fuzzy based Q – learning approach to prevent sinkhole attacks in MANET (FQ – SPM)

Mobile Ad-Hoc Networks (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack. We try to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning-based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach.

Design of agricultural wireless sensor network node optimization method based on improved data fusion algorithm.

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.