Intelligent Monitoring Network Research Articles

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

A cross-domain state monitoring method for high-speed train brake pads based on data generation under small sample conditions

The proper functioning of the high-speed train brake system is of utmost importance to ensure the train comes to a halt at the intended distance. Among the key components, brake pads are susceptible to abnormal wear over prolonged usage, posing a safety risk to train operations. However, limited real-world data on brake pad damage poses challenges for effective monitoring. This paper proposes an effective transfer learning model that utilizes a small amount of data at different speeds, which can solve the small sample data problem on brake pad damage. Initially, a deep convolutional generative adversarial network (DCGAN) is employed to simulate and expand a limited dataset of vibration signals. The generated signals accurately preserve both the high-frequency and low-frequency characteristics observed in the actual signals. Subsequently, a domain-adversarial neural network (DANN) is utilized to map features from the source and target domains onto a shared feature space, enabling multi-domain feature extraction and adaptation. Finally, an intelligent monitoring network is employed to identify the health status of unlabeled brake pads under fluctuating speeds. Experimental results demonstrate the proposed approach outperforms other commonly used algorithms, providing evidence of the feasibility to monitor the small sample conditions of high-speed train brake pads.

An intelligent network monitoring approach for online classification of Darknet traffic

The Internet plays a crucial role in supporting global applications and businesses, but security remains a major challenge. Within the Internet, there exists a parallel network known as the Darknet, where malicious activities and traffic are present and require real-time classification. Many methods aim to classify this Darknet traffic in real-time due to its significant volume within Internet traffic. However, online Darknet traffic classification faces challenges, particularly in determining the optimal packet sampling amount for achieving a high classification rate in high-performance networks. To address this, our paper presents a novel approach that combines Convolutional Neural Network (CNN) and Reinforcement Learning (RL) techniques for intelligent and adaptive packet sampling rates in high-performance network interfaces. This method reduces overhead on monitored entities, especially in high-speed networks with a high bit rate. Our findings demonstrate a TOR traffic prediction accuracy of 99.84% and successful classification tasks in high-throughput networks using our method.

Recognition of Abnormal Individuals Based on Lightweight Deep Learning Using Aerial Images in Complex Forest Landscapes: A Case Study of Pine Wood Nematode

Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.

Intelligent Analysis Algorithm for Hidden Danger Identification of Intelligent Network Monitoring System from the Perspective of Big Data

Big data has become the mainstream of information dissemination today. Big data plays an important role in the field of intelligent network monitoring. How to improve the hidden danger identification of intelligent network monitoring system and improve its accuracy is a hot issue to be solved. Therefore, in the context of big data, this paper uses intelligent analysis algorithm to identify and analyze the hidden dangers of intelligent network monitoring system. This paper mainly applies the methods of experimental setup, fuzzy analysis and data comparison to carry out relevant research on intelligent analysis of hidden danger identification of intelligent network monitoring system. The experimental data shows that the accuracy of the data obtained by the proposed method in the hidden danger identification is more than 50%, but less than 80%. Therefore, the application of background difference method needs further improvement.

Retraction Note to: The stability of rainfall conditions based on sensor networks and the effect of psychological intervention for patients with urban anxiety disorder

As a new type of intelligent monitoring network, the wireless sensor network of the rapidly growing intelligent monitoring network, in order to study the wireless sensor network, many technical personnel of the relevant technical level analyze and think from multiple angles, combining the network world and the network application center. In the technical research stage, combined with the experience of former researchers, this article combines a variety of algorithms, including wireless sensor network (WSN) layer routing protocols, such as BP neural network and other new applications, and proposes a very novel algorithm, which is similar to the algorithm of BP neural network. It also combined many new calculation methods and found that psychological cues have a strong effect in the study of many patients with anxiety disorders in the city. This article mainly discusses the rainfall and soil quality in the rainfall environment, including saturated homogeneous soil, saturated heterogeneous soil, and unsaturated heterogeneous soil, whether the slope under different rainfall environments and the seepage field and stress field are combined smoothly. Under different rainfall environments, there is a force in the opposite direction at the top of the slope, and then, when the water pressure decreases, the direction of the slope top force is the positive direction; under the same rainfall environment and the same time environment, the force on the slope foot is greater than the force on the top of the slope; this paper combines the data algorithm with the line sensor network technology to study the stability of contemporary rainfall conditions and the psychological intervention of patients with urban anxiety.

Engineering design of electronic fence system based on intelligent monitoring and wireless local area network

With the establishment of the economic New Area, preliminary results have been achieved in the construction of the business environment, and some experience has been accumulated, but there are still many problems. The most important of these is the issue of business safety. The security system in commercial areas generally adopts manual shifts. This management method is increasingly unsuitable for urban development. Therefore, the unattended electronic fence system emerges from time to time, but its security has become an urgent problem to be solved. This article designs the electronic fence system project based on intelligent video surveillance and wireless local area network technology. This article designs the electronic fence system based on intelligent video surveillance and wireless local area network technology. First, design a pulsebased electronic fence system. Under the control of the FPGA, by sending pulses to the electronic fence, the frontend detection fence subsystem can detect the pulse signals, and implement the pulse signals to the FPGA through wireless local area network technology. After feature extraction and pulse signal recognition, determine whether an abnormal situation has occurred. Secondly, an intelligent video surveillance system based on wireless local area network is designed to record the surrounding video information 24 h, and use related target detection technology to locate and record abnormal conditions. The experimental results show that the design system canwell judge the occurrence of abnormal conditions and record the abnormal conditions in realtime, which can ensure the safety.

RETRACTED ARTICLE: The stability of rainfall conditions based on sensor networks and the effect of psychological intervention for patients with urban anxiety disorder

As a new type of intelligent monitoring network, the wireless sensor network of the rapidly growing intelligent monitoring network, in order to study the wireless sensor network, many technical personnel of the relevant technical level analyze and think from multiple angles, combining the network world and the network application center. In the technical research stage, combined with the experience of former researchers, this article combines a variety of algorithms, including wireless sensor network (WSN) layer routing protocols, such as BP neural network and other new applications, and proposes a very novel algorithm, which is similar to the algorithm of BP neural network. It also combined many new calculation methods and found that psychological cues have a strong effect in the study of many patients with anxiety disorders in the city. This article mainly discusses the rainfall and soil quality in the rainfall environment, including saturated homogeneous soil, saturated heterogeneous soil, and unsaturated heterogeneous soil, whether the slope under different rainfall environments and the seepage field and stress field are combined smoothly. Under different rainfall environments, there is a force in the opposite direction at the top of the slope, and then, when the water pressure decreases, the direction of the slope top force is the positive direction; under the same rainfall environment and the same time environment, the force on the slope foot is greater than the force on the top of the slope; this paper combines the data algorithm with the line sensor network technology to study the stability of contemporary rainfall conditions and the psychological intervention of patients with urban anxiety.

Deep anomaly detection in expressway based on edge computing and deep learning

In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network of expressway based on edge computing and deep learning is studied. The video data collected by the camera equipment in the intelligent monitoring network structure of the expressway is transmitted to the edge processing server for screening and then sent to the convolutional neural network. The convolutional neural network uses the multi-scale optical flow histogram method to preprocess the video data after the edge calculation to generate the training sample set and send it to the AlexNet model for feature extraction. SVM classifier model is used to train the feature data set and input the features of the test samples into the trained SVM classifier model to realize the anomaly detection in the intelligent monitoring network of expressway. The research method is used to detect the anomaly in an intelligent monitoring network of an expressway. The experimental results show that the method has better detection effect. The miss rate has reduced by 20.34% and 40.76% on average compared with machine learning method and small block learning method, respectively. The false positive rate has reduced by 27.67% and 21.77%, and the detection time is greatly shortened.

Research and development of intelligent network monitoring and warning system for deep foundation pit

Research and development of intelligent network monitoring and warning system for deep foundation pit

High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network

As one of the source sensors of maritime intelligent traffic network, radar plays an important role in maritime monitoring and early warning. The number of features extracted by the traditional maritime radar target detection method in feature domain is small, and the sea clutter and target echo features are often not linearly separable, so the radar detection performance is seriously affected by sea clutter. To solve this problem, this paper combines the time–frequency domain processing method with the residual neural network, and uses the time–frequency transform method to improve the signal-to-clutter ratio (SCR) and the degree of difference between sea clutter and target echo. On this basis, the high-dimensional time–frequency spectrum features are extracted by using the residual neural network to improve the utilization rate of radar echo information, form a high-dimensional feature space of sea clutter and target echo that are non-linearly separable, and realize the binary classification of sea clutter and target echo. It is verified by the measured data of X-band radar that the proposed target detection method can extract the deep features of the time–frequency spectrum of radar echo, has a high classification accuracy even in the case of low SCR, and has the potential to detect weak targets in the background of strong sea clutter. In addition, the influence of different time–frequency transform methods and polarization modes on target detection performance is further analyzed. The comparative study shows that different time–frequency transform methods and polarization modes have little influence on the classification accuracy of the proposed method. In comparison, under the conditions of fractional Fourier transform and cross-polarization, the proposed method has higher classification accuracy.

An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

The intelligent environment monitoring network, as the foundation of ecosystem research, has rapidly developed with the ever-growing Internet of Things (IoT). IoT-networked sensors deployed to monitor ecosystems generate copious sensor data characterized by nonstationarity and nonlinearity such that outlier detection remains a source of concern. Most outlier detection models involve hypothesis tests based on setting outlier threshold values. However, signal decomposition describes stationary and nonstationary relationships sensor data. Therefore, this paper proposes a three-level hybrid model based on the median filter (MF), empirical mode decomposition (EMD), classification and regression tree (CART), autoregression (AR) and exponential weighted moving average (EWMA) methods called MF-EMD-CART-AR-EWMA to detect outliers in sensor data. The first-level performance is compared to that of the Butterworth filter, FIR filter, moving average filter, wavelet filter and Wiener filter. The second-level prediction performance is compared to support vector regression (SVR), K-nearest neighbor (KNN), CART, complementary ensemble EEMD with CART and AR (EEMD-CART-AR) and ensemble CEEMD with CART and AR (CEEMD-CART-AR) methods. Finally, EWMA is compared to Cumulative Sum Control Chart (CUSUM) and Shewhart control charts. The proposed hybrid model was evaluated with a real dataset from the hydrometeorological observation network in the Heihe River Basin, yielding experimental results with better generalization ability and higher accuracy than the compared models, and providing extremely effective detection of minor outliers in predicted values. This paper provides valuable insight and a promising reference for outlier detection involving sensor data and presents a new perspective for detecting outliers.

Intelligent Monitoring Network Construction based on the utilization of the Internet of things (IoT) in the Metallurgical Coking Process

Abstract With the development of the Internet of Things (IoT), a new and important research direction is possible using IoT to solve the problems of information and intelligence in the metallurgical industry. This paper proposes an intelligent monitoring network based on networking technology and uses the coking process as the research object. The construction of a coking process intelligence monitoring network should focus on the formation of a perception layer network and build on a ZigBee mesh clustered network. Moreover, it also puts forward a network routing establishment and data transmission mechanism. This study provides an effective reference for the wide application of the IoT in the intelligent management and monitoring of the metallurgical process.

A highly conductive and stretchable wearable liquid metal electronic skin for long-term conformable health monitoring

Conventional rigid electronics are usually unconformable with soft skins and tend to fail in accurate physiological monitoring and precise treatment. Electronic skins (e-Skins) made by conductive and stretchable materials offer mechanical compliance for fabricating flexible and conformable wearables. Compared to common organic or inorganic conductive materials, gallium-based liquid metals alone own superior conductivity and compliance. Here, we demonstrate a highly conductive and stretchable electronic skin with liquid metal circuits (LMCs) embedded in silicone rubber film, which are functionalized for physiological signals monitoring. Through the designs of serpentine structure, LMCs maintained good electrical conductivity and functionality under over 100% strain. Also, a wearable electrocardiogram (ECG) recording device was fabricated and tested. The device was able to acquire steady signals during real-time measurement of physical activities. The proposed liquid metal e-Skin can be further extended to conformable bio-integrated healthcare devices and intelligent health monitoring networks.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

Design and implementation of an automated network monitoring and reporting back system

Abstract The task of network monitoring becomes tedious with increase in the network size, heterogeneity and complexity. The available network monitoring and management solutions are not only expensive but also difficult to use, configure and maintain. Manually pin pointing a faulty device in the large complex network is very tricky and time consuming for network administrators. Thus, an automated system is needed that immediately reports to network administrator about fault type and location as soon as it arises. This paper presents design and implementation of an intelligent network monitoring and reporting back system for large size organizations/industries. It is based on programming open-source tools (such as Nagios and RT) and intelligently integrating them to monitor network devices such as switches and routers. To monitor end-user devices in the network, a software package has been developed using Universal Plug & Play (UPnP) technology. The developed monitoring system immediately notifies network administrator as soon as a network problem arises. The notification message clearly pin points the fault location in network topology, its type and impact on rest of the network. If the network problem is not resolved within the pre-specified deadline, a second notification is sent out to the next responsible person. Thus, all people in the priority list are notified one by one with pre-specified deadlines until the problem is resolved.

Optimizing stations location for urban noise continuous intelligent monitoring

In most of urban noise monitoring systems, the optimization of number and locations of autonomous monitoring stations is a Non-deterministic Polynomial Complete (NPC) problem. It is also important for the implementation of intelligent measurement networks. This paper investigates an optimization method to achieve minimum stations for urban noise intelligent monitoring. First a mathematical model for monitoring stations selection has been developed. Next, a novel hybrid Immune PSO K-means (IPKM) clustering algorithm is proposed to solve the mathematical model. The IPKM algorithm can overcome the shortcomings (e.g. slow convergence speed) of the Particle Swarm Optimization (PSO) algorithm, and help K-means clustering algorithm escaping from local optima. Finally, the methodology has been applied to QingDao urban noise intelligent monitoring networks. For comparison, the K-means algorithm and IPKM algorithm are applied to the noise grid survey datasets of 1998–2014years. The final optimized results illustrate the proposed method could perform relevant monitoring tasks with fewer monitoring stations. In addition, the importance of the proposed method is that it would be applicable for noise monitoring and noise control management problems.

Control of framed structures using intelligent monitoring networks

The paper proposes the integration of structural monitoring with Building Management Systems for electricity and gas distributions. To assess the state of damage of existing buildings the technics of Structural Health Monitoring (SHM) is adopted. SHM as well as to record the occurrence of sudden structural damage resulting from exceptional events (earthquakes, explosions, shocks and collisions with vehicles, etc.), allows the monitoring of the progressive damage and structural performance under operating conditions through the extraction of the modal parameters of the structure. This approach requires time to process acquired data that, depending on the size of the building and the number of monitored points, varies from minutes to hours. In this paper, an intelligent system is proposed to immediately communicate during an earthquake the overrun of a certain ground shaking threshold so that gas delivery and selected power loads are interrupted, as suggested by current national regulations on structures. The use of low-cost and reduced size accelerometric sensors integrated with Energy Monitoring Systems is proposed in both highrisk earthquake centers and in all “strategic” buildings that must ensure their operation use immediately after the earthquake. The procedure for calibrating the horizontal and vertical acceleration threshold is also sketched.

Analysis of Energy Efficiency of Cooperative Communication in Wireless Sensor Networks

Wireless sensor network (WSNs) is composed of a large number of cheap micro sensor nodes with wireless communication and computing capabilities, which will deploy in unattended monitoring area through the cooperation of autonomous monitoring tasks assigned, and sending the information of monitoring object collection to intelligent monitoring network system for remote users.

Crucial elements and technical implementation of intelligent monitoring networks.

Growing complexity of water monitoring instrumentation leads to specialized solutions in respect to sensor integration across several measurement device suppliers. Despite efforts of standardization for data interfaces and protocols, problems regarding the combination of several devices to gain the complete picture in terms of water quality remain. This assessment, especially accomplished from the perspective of a catchment area, requires a transition from sole use of data collectors toward an implementation of intelligent measurement networks. Several challenges and bottlenecks concerning distributed data collection are discussed starting with data acquisition up to the user-scope of utilizing data processing software. Finally, experiences using automated data inspection and export tools are discussed and a brief overview of expectable long-term data availability is given.