Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks
Source abstract
Abstract
This study aimed to determine the applicability of using selected libraries of computing environment R to establish the coefficient of self-similarity. R environment is an analytical environment with rich functionality that is used in many research and practical works concerning data analysis and knowledge discovery. Such an issue is significant in the context of contemporary wide area computer networks and the emerging type of network infrastructure IoT. This originates directly from the new nature of IoT traffic, which also has a substantial impact on Industry 4.0. It provides built-in operations facilitating data processing. The Hurst coefficient is used to evaluate traffic behavior and analyze its character. The study analyzed two cases of IoT network traffic: high and low intensity. For different sizes of time windows, we dermined the statistical Hurst exponent and compared it with standard, smoothed, and Robinson methods. The accuracy of the methods used was evaluated. Spectral regression graphs were additionally generated for selected motion variants. The obtained results were verified by Higuchi and Aggvar methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Complex Network Analysis Techniques · Network Traffic and Congestion Control · Complex Systems and Time Series Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
Funding
Minister of Science and Higher Education of the Republic of Poland
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