Research on internal network data security monitoring method based on NB-IOT

2021 ◽  
pp. 1-12
Author(s):  
Yuanyuan Li ◽  
Jidong Sha ◽  
Rongna Geng
Keyword(s):  
Internet Of Things ◽  
Data Security ◽  
Narrow Band ◽  
Network Data ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Security Incident ◽  
Network Attack ◽  
Security Monitoring ◽  
Quantity Index

In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node’s optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation.

scholarly journals Online Monitoring of Transmission Line Operation Based on Power Internet of Things

2021 ◽  
Vol 2108 (1) ◽  
pp. 012053
Author(s):  
Xiaohua Zhang ◽  
Wenxiang Xue ◽  
Shuyuan Wang ◽  
Yi Lu ◽  
Hui Wang
Keyword(s):  
Internet Of Things ◽  
Transmission Line ◽  
Real Time ◽  
Edge Computing ◽  
Monitoring Methods ◽  
Time Data ◽  
Monitoring Method ◽  
Computing Model ◽  
Real Time Data ◽  
On Line

Abstract Current monitoring methods of transmission line operation status can not obtain real-time data of distributed distribution transmission line, which leads to a large error in monitoring results. Therefore, a multi-state on-line monitoring method based on power Internet of Things is proposed. Using the gateway of power internet of things to set up network control access mode, build edge computing model, and using AD chip of ADS8365W5300 to obtain the real-time data of massive distributed distribution network, then make a decision on the fault after processing and analyzing the data. This paper constructs an edge computing model which can complete the data processing and analysis in the edge node, and designs the deployment of the edge computing model. By evaluating the faults in the dynamic incremental fault set, the risk state of transmission line in the danger control area is obtained, and a multi-state on-line monitoring method is designed. The experimental results show that the proposed method can monitor the transmission line running state accuratel

Applied-Information Technology in Research of Security Processing Based on IOT Data

2014 ◽  
Vol 908 ◽  
pp. 509-512
Author(s):  
Wei Jiang ◽  
Feng Yang
Keyword(s):  
Information Technology ◽  
Internet Of Things ◽  
Monitoring System ◽  
Data Storage ◽  
Data Security ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Network Data ◽  
Lake Water Quality ◽  
Important Trend

Internet of things (IOT) has become an important trend in the development of information technology. How to deal with huge amounts of internet data is becoming more and more important. In this paper, we have a further research in the technology of applied-information about mass content of network data security processing model. This model is mainly composed of massive internet of data acquisition, data storage, based on the rules of mass data processing and data security management, etc. The model can be applied to all kinds of massive internet data monitoring system based on rules, such as: the lake water quality monitoring system based on Internet of things, PM2.5 monitoring system, and so on.

scholarly journals Dynamic Monitoring of Low Voltage Distribution Network Leakage Detection Device Based on Internet of Things Technology

2021 ◽  
Vol 252 ◽  
pp. 01006
Author(s):  
Yang Wang ◽  
Shiqing Wang
Keyword(s):  
Internet Of Things ◽  
Low Voltage ◽  
Distribution Network ◽  
The State ◽  
Dynamic Monitoring ◽  
Leakage Detection ◽  
Voltage Distribution ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Internet Of Things Technology

The existing dynamic monitoring methods of low-voltage distribution network leakage detection device have been unable to meet the needs of the distribution network in China. Therefore, a dynamic monitoring method of low-voltage distribution network leakage detection device based on Internet of Things technology is proposed. Based on the introduction of the Internet of Things technology sensor sensing technology, the sensor is installed on the leakage detection device to obtain the operation data of the leakage detection device and preprocess it with noise reduction and normalization. At the same time, the statistical analysis of partial discharge signal is carried out to extract the characteristics of fast waveform signal (energy parameters, sample entropy and modal components). Based on the operation data features of the leakage detection device extracted above, the state diagnosis framework of the leakage detection device is built to diagnose the state of the leakage detection device, and the dynamic monitoring of the leakage detection device in low-voltage distribution network is realized. The experimental results show that: compared with the existing methods, the proposed method has stronger anti-interference ability and smaller monitoring error, which fully proves that the proposed method has better application effect.

scholarly journals Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis

2021 ◽  
Vol 193 (7) ◽  
Author(s):  
Heini Hyvärinen ◽  
Annaliina Skyttä ◽  
Susanna Jernberg ◽  
Kristian Meissner ◽  
Harri Kuosa ◽  
...  
Keyword(s):  
Comparative Studies ◽  
Cost Efficiency ◽  
Cost Benefit ◽  
Marine Ecosystems ◽  
Actual Cost ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Monitoring ◽  
The Status ◽  
The Cost

AbstractGlobal deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term “cost-efficiency.” We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost–benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost–benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.

Reduction of Cyclic Prefix Overhead in Narrow-Band Internet of Things (NB-IoT) Systems

2020 ◽  
pp. 1-1
Author(s):  
Dejin Kong ◽  
Pei Liu ◽  
Yaru Fu ◽  
Jie Ding ◽  
Tony Q. S. Quek
Keyword(s):  
Internet Of Things ◽  
Narrow Band ◽  
Cyclic Prefix

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

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