scholarly journals Modeling and analysis of the states of objects and subjects of monitoring by means of secure sensor networks and IoT networks with a supercomputer

2021 ◽  
pp. 191-195
Author(s):  
Bohdan Shevchuk
Keyword(s):  
Information Technology ◽  
Monitoring Networks ◽  
Modeling And Analysis ◽  
Mobile Objects ◽  
Zone Control ◽  
Information Models ◽  
Long Term Monitoring ◽  
Informative Signals ◽  
Term Monitoring

The paper proposes an information technology for evidence-based monitoring of the states of remote and mobile objects and subjects. The proposed method for the effective implementation of long-term monitoring of a large number of objects based on modeling information states of objects by means of aperture or zone control of changes in selected indicators and calculated signal characteristics. Taking into account the minimization of computations with performance-limited processor facilities of the object systems of secure wireless monitoring networks at the places of introduction of monitoring signals, it is proposed to form logical and statistical information models of the behavior of objects that correspond to the current functional and operating states of objects of long-term monitoring. To identify the most informative signals and characteristics of the states of objects, it is proposed to calculate and analyze the relative and normalized indicators and characteristics of signals. Information technology is focused on long-term monitoring of objects and subjects in various spheres of human activity.

Fault tree analysis for data-loss in long-term monitoring networks

2009 ◽  
Vol 60 (4) ◽  
pp. 909-915 ◽  
Author(s):  
J. Dirksen ◽  
J. A. E. ten Veldhuis ◽  
R. P. S. Schilperoort
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Monitoring Network ◽  
Limited Attention ◽  
Monitoring Networks ◽  
Data Loss ◽  
Long Term Monitoring ◽  
Data Yield ◽  
Term Monitoring

Prevention of data-loss is an important aspect in the design as well as the operational phase of monitoring networks since data-loss can seriously limit intended information yield. In the literature limited attention has been paid to the origin of unreliable or doubtful data from monitoring networks. Better understanding of causes of data-loss points out effective solutions to increase data yield. This paper introduces FTA as a diagnostic tool to systematically deduce causes of data-loss in long-term monitoring networks in urban drainage systems. In order to illustrate the effectiveness of FTA, a fault tree is developed for a monitoring network and FTA is applied to analyze the data yield of a UV/VIS submersible spectrophotometer. Although some of the causes of data-loss cannot be recovered because the historical database of metadata has been updated infrequently, the example points out that FTA still is a powerful tool to analyze the causes of data-loss and provides useful information on effective data-loss prevention.

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