Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Study on an Audio and Video Network Monitoring System for Weather Modification Operation

An audio and video network monitoring system for weather modification operation transmitting information by 3G, ADSL and Internet has been developed and applied in weather modification operation of Tai＇an City. The all-in-one machine of 3G audio and video network highly integrates all front-end devices used for audio and video collection, communication, power supply and information storage, and has advantages of wireless video transmission, clear two-way voice intercom with the command center, waterproof and dustproof function, simple operation, good portability, and long working hours. Compression code of the system is transmitted by dynamic bandwidth, and compression rate varies from 32 kbps to 4 Mbps under different network conditions. This system has forwarding mode, that is, monitoring information from each front-end monitoring point is trans- mitted to the server of the command center by 3G/ADSL, and the server codes＇and decodes again, then beck-end users call images from the serv- er, which can address 3G network stoppage caused by many users calling front-end video at the same time. In addition, the system has been ap- plied in surface weather modification operation of Tai＇an City, and has made a great contribution to transmitting operation orders in real time, monitoring, standardizing and recording operating process, and improving operating safety.

Application of the Keyword Recognition in the Network Monitoring

In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network monitoring and keywords spotting. In the part of network monitoring, this paper adopts a method which is based on ARP spoofing technology to monitor the users＇ data, and to obtain the original audio streams. In the part of keywords spotting, the extraction methods of PLP （one of the main characteristic arameters） is studied, and improved feature parameters- PMCC are put forward. Meanwhile, in order to accurately detect syllable, the paper the double-threshold method with variance of frequency band method, and use the latter to carry out endpoint detection. Finally, keywords recognition module is built by HMM, and identification results are contrasted under Matlab environment. From the experiment results, a better solution for the application of key words recognition technology in network monitoring is found.

Develop and Implement of Real-time Network Monitoring Software based on C/S

This article mainly introduces the multi-layer distributed C/S architecture of system design scheme. Its working principle is the client program runs automatically after the computer starts, and establish communication with the application server. The network administrator can monitor and intelligent management of the client computer through the server program, the computer will execute the corresponding operation according to the server to send command instructions. The system realize the main module of the whole system framework, network monitoring data initialization module, network data transmission module, image coding and decoding module, the advantages of system make full use of existing LAN resources, timely delivery and manaRement information.

Well-Drilling and Groundwater Monitoring Network Construction:Taking Changde City as an Example

Entrusted by the Environmental Protection Bureau of Changde City,we conducted drilling,sampling survey and constructed a monitoring network for groundwater in several counties and districts of Changde City.This article introduces the drilling technology,detection method and detection network layout plan adopted in the project,and expounds the problems that occurred while executing the project,in order to provide reference for similar groundwater capacity supervision and construction projects.

Trident: Efficient and Practical Software Network Monitoring

Network monitoring is receiving more attention than ever with the need for a self-driving network to tackle increasingly severe network management challenges. Advanced management applications rely on traffic data analyses, which require network monitoring to flexibly provide comprehensive traffic characteristics. Moreover, in virtualized environments, software network monitoring is constrained by available resources and requirements of cloud operators. In this paper, Trident, a policy-based network monitoring system at the host, is proposed. Trident is a novel monitoring approach, off-path configurable streaming, which offers remote analyzers a fine-grained holistic view of the network traffic. A novel fast path packet classification algorithm and a corresponding cached flow form are also proposed to improve monitoring efficiency. Evaluated in a practical deployment, Trident demonstrates negligible interference with forwarding and requires no additional software dependencies. Trident has been deployed in production networks of several Tier-IV datacenters.

Current situation and trend of marine data buoy and monitoring network technology of China

Marine data buoy can provide a long-term, continuous, real-time, reliable data of ocean observation in a variety of complex marine environment. It is one of the most reliable, most effective and important means of ocean monitoring technology. In this paper, the classification, main theory and technology system of marine data buoy are summarized. The typical technological breakthrough of the development of marine data buoy in recent years is summarized. The composition and application of marine monitoring network in China was introduced, and the gap between the technology of China＇s marine data buoy and the international advanced countries is compared.Combined on the situation and demand of China＇s current situation and needs, the development trend of marine data buoy and buoy monitoring network are expected.

Upgrading a regional groundwater level monitoring network for Beijing Plain,China

Monitoring of regional groundwater levels provides important information for quantifying groundwater depletion and assessing impacts on the environment. Historically, groundwater level monitoring wells in Beijing Plain, China, were installed for assessing groundwater resources and for monitoring the cone of depression. Monitoring wells are clustered around well fields and urban areas. There is urgent need to upgrade the existing monitoring wells to a regional groundwater level monitoring network to acquire information for integrated water resources management. A new method was proposed for designing a regional groundwater level monitoring network. The method is based on groundwater regime zone mapping. Groundwater regime zone map delineates distinct areas of possible different groundwater level variations and is useful for locating groundwater monitoring wells. This method was applied to Beijing Plain to upgrade a regional groundwater level monitoring network.

Preliminary Hydrogeologic Modeling and Optimal Monitoring Network Design for a Contaminated Abandoned Mine Site Area: Application of Developed Monitoring Network Design Software

In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error;2) locate the plume boundary;3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells.

Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

Groundwater level monitoring-importance global groundwater monitoring network

The paper illustrates the concept, methodology, essential components and importance of groundwater level monitoring in terms of various aquifers such as multiple aquifer, karst aquifer and other aquifers. The groundwater resources in Mekong countries including Cambodia, Laos PDR, Myanmar, Thailand and Vietnam have also been reviewed. Finally, the author briefly presents Global Groundwater Monitoring Network.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

Prospects for the Development and Application of the Earthquake Monitoring Network

With the rapid development of the economy in China, the seismic network has been changing rapidly, in that the capability of instruments, technological systems and network density are approaching those of developed countries and a large quantity of observation data has been accumulated. How to apply these resources to economic construction and public safety has become an important issue worth studying. In order to improve earthquake prediction and earthquake emergency response, it is suggested in this paper that extracting valuable precursor information, improving earthquake rapid reporting ability and extending rapid intensity reporting function are key issues. Integrating network resources, building unified standards and a multifunction seismic monitoring network are preconditions of establishing a public safety service platform and earthquake observation resources will contribute significantly to the fields of engineering, ocean, meteorology, and environmental protection. Thus, the future directions of the development of the seismic network are exploring monitoring resources, enhancing independent innovation, constructing a technological platform and enlarging the service field.

Seismic Monitoring Network and Seismic Activities in Thailand

The details of seismic monitoring network and seismological work in Thailand, including some information on felt earthquakes that occurred from 2000 to August 2001, are reported here.

Detection Situation and Improvement of Lightning Monitoring Network in Guizhou Province

Based on lightning location data of lightning monitoring network in Guizhou Province in recent eight years,the effective detection radius of a station and the effective detection range of lightning monitoring network in Guizhou Province were analyzed. The results show that the effective detection radius of a lightning monitoring sub-station in Guizhou Province is 160 km; some counties in the southwest,northwest and northeast of Guizhou were not detected. To improve the detector efficiency of lightning monitoring network in Guizhou Province,it is suggested that nine sub-stations should be built in Weining,Shuicheng,Qinglong,Pingtang,Rongjiang,Yuping,Songtao,Tongren and Renhuai,so that the effective detection efficiency will reach more than 95%.

Monitoring Efficiency Evaluation of the Well-water-level Observation Network in China

In order to understand the monitoring efficiency status of the well-water-level observation network in China after the completion of the 10 th "Five-year Plan " digital network project,and to provide a basis for the future network optimization and equipment updating, the monitoring efficiency of the well-water-level observation network was evaluated. On the whole,61. 8% observing stations have good monitoring effectiveness,the observation environment of 73. 5% of observing stations meets the monitoring requirements of well-water-level. The operation status of the network is as a whole getting better,operation rates of 75% observing instruments are above 95%. Most well water levels can monitor crustal stress changes and seismic activities. However,some observation stations,due to inherent deficiency in wells,environmental disturbance,instrument aging,and low-level operation and maintenance,need to improve the monitoring efficiency level by taking measures such as observation environment improvement,equipment updating,and management training. About 6. 5% of the stations need to stop observation due to the unqualified observational environment.

Network Based Real Time Condition Monitoring of Rotating Machinery

This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server) and intranet. The client serves as a field data collector and processor that samples the vibration signals and process parameters of a machine monitored in the net and processes the sampled data. The data collected by the client are transmitted to the server that processes the data further and provides the results of the diagnosis of each machine to any distant terminals through intranet or internet. Such a structure of the monitoring system is advantageous in safety, reliability and reasonably shares the existing net resources. In order to ensure real time transmission of the data, two procedures of data transmission, virtual channel and data pool, are developed and applied in the monitoring system. The experimental results show that the monitoring system works well and is suitable to monitor a large group of rotating machines.

DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

The evolution and expansion of IoT devices reduced human efforts,increased resource utilization, and saved time;however, IoT devices createsignificant challenges such as lack of security and privacy, making them morevulnerable to IoT-based botnet attacks. There is a need to develop efficientand faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, Deep Neural Network(DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-knownIoT botnet attacks such as Mirai and BASHLITE from nine compromisedindustrial-grade IoT devices. The utilization of PCA was made to featureextraction and improve effectual and accurate Botnet classification in IoTenvironments. The models were designed based on rigorous hyperparameterstuning with GridsearchCV. Early stopping was utilized to avoid the effects ofoverfitting and underfitting for both DNN models. The in-depth assessmentand evaluation of the developed models demonstrated that accuracy andefficiency are some of the best-performed models. The novelty of the presentinvestigation, with developed models, bridge the gaps by using a real datasetwith high accuracy and a significantly lower false alarm rate. The results wereevaluated based on earlier studies and deemed efficient at detecting botnetattacks using the real dataset.

FPGA-Based Network Traffic Security: Design and Implementation Using C5.0 Decision Tree Classifier

In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs： hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services （DOS）, leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array （FPGA）, giving a more advantages to the system. A C5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.