Real-Time Monitoring of Network Devices: Its Effectiveness in Enhancing Network Security

The business world has been significantly affected by network intrusion leading to infringement of privacy and unprecedented economic losses. Therefore, real-time monitoring of network devices is important due to the enhanced and complex network systems in organizations and associated cyber threats. Real-time monitoring provides adequate alerts and updates regarding specific networks and their performance as soon as they occur. Constant monitoring of devices also makes it possible for organizations to detect any possible challenges that the networks may be encountering. This paper examines the effectiveness of real-time monitoring of network devices in a bid to enhance network security. The study was an empirical review of recently published research papers, journals, internet sites, and books with relevant content. The findings of this study revealed that Real-time device monitoring has many potential advantages to organizations by securing their systems thereby enhancing their overall performance.

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A Study on the Slope Failure Monitoring of a Model Slope by the Application of a Displacement Sensor

Journal of Sensors ◽

10.1155/2019/7570517 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Sungyong Park ◽

Hyuntaek Lim ◽

Bibek Tamang ◽

Jihuan Jin ◽

Seungjoo Lee ◽

...

Keyword(s):

Real Time ◽

Early Warning ◽

Slope Failure ◽

Early Warning Systems ◽

Displacement Sensor ◽

Scale Model ◽

Economic Losses ◽

Real Time Monitoring ◽

Landslide Occurrence ◽

Model Slope

Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This suggests that there is a need for an early warning system to mitigate casualties and economic losses. Most of the studies on early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient. In this study, a displacement sensor and an Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an inverse displacement analysis was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.

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Study on Real-Time Monitoring System of Mining Subsidence Area Based on LabVIEW

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.484-485.303 ◽

2014 ◽

Vol 484-485 ◽

pp. 303-306

Author(s):

Xing Qiang Guo ◽

Zhao Ming Qian ◽

Gao Feng Ren ◽

Zhi Gang Liu

Keyword(s):

Real Time ◽

Monitoring System ◽

Virtual Instrument ◽

Mining Subsidence ◽

Economic Losses ◽

Real Time Monitoring ◽

Time Data ◽

Computer Terminal ◽

Object A ◽

Transmission Equipment

Taking the mining subsidence as research object, a real-time monitoring scheme was designed. On this basis, a virtual instrument monitoring system based on LabVIEW was constructed. The data acquisition instrument collect real-time data through monitoring of subsidence and subsidence slope deformation by this system in range of mining influence, then control and transform it. After this, the data was transmitted to the computer terminal through wireless transmission equipment, and monitoring will be got after analysis and prediction by computer terminal. This result can provide mining of this area with real-time advice and guidance, to reduce the harms brought by mining subsidence and unnecessary economic losses.

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Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2016/7129376 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 7

Author(s):

Mehmet Şimşir ◽

Raif Bayır ◽

Yılmaz Uyaroğlu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Low Power ◽

Real Time ◽

Economic Losses ◽

Real Time Monitoring ◽

System Operation ◽

Compact Structure ◽

Neural Network Approach ◽

Data Set

Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

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