An article was published in the Applied Sciences journal (current Polish ministerial score: 70 points, Impact Factor: 2,474): as part of the research work carried out in the field of applications of traffic analysis methods in computer networks, IoE, and in the diagnosis, supervision and control of processes in Industry 4.0
Dymora, P.; Mazurek, M. Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks. Appl. Sci. 2021, 11, 190.
https://www.mdpi.com/2076-3417/11/1/190
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.
