The queue model P/M/1/K with the Hurst parameter (self-similarity parameter) estimator in the Simulink environment is described in the present paper using wavelet transformation. The estimation error of the parameter is analyzed depending on the parameter’s value, as well as the number of measurements based on which the parameter is estimated. This is an experimental paper aimed at accumulating knowledge on the practicability of estimating the Hurst parameter and on the estimation accuracy in the case of using wavelet analysis.

Real-Time Estimation of Traffic Self-Similarity Parameter in Simulink with Wavelet Transform

Wireless technologies are essential for modern people to maintain uninterrupted connection to the Internet. The most popular standards for wireless technologies are standards of the IEEE 802.11 family. Currently, data transmission rate achievable by IEEE 802.11ac or 802.11ax standards can reach up to 10 Gbit/s. Different IEEE standards have specific data transmission rates. For example, the IEEE 802.11ah standard or Wi-Fi HaLow (code name) operates in the 900 MHz band, which is an unlicensed frequency band below 1 GHz, and is called the “Sub-1-GHz” range. In theory, this standard can provide coverage range of up to 543 meters indoors and data transfer rate of up to 347 Mbit/s (using a maximum of four spatial streams and 16 MHz channel bandwidth). The great benefit of the 802.11ah standard is low energy consumption, which enables communication between devices from the Internet of Things (IoT) over long distances without using a lot of energy. The Wi-Fi HaLow standard is being studied by the authors of the presented article in the ns-3 network simulation program with the 802.11ah module installed and implemented in Docker containers, VirtualBox Virtual Machines (VMs) with a running Linux operating system. During the simulations, results were obtained for the Docker containers simulation with a limited number of stations over different simulation times. These results have been studied in different scenarios. In the scenarios, the results of the Wi-Fi HaLow network simulation were converted into another simulation time, and thus were compared with each other.

Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.

WLAN IEEE 802.11ac is one of the wireless network technologies that can be used for ITS (Intelligent Transport Systems) needs, in particular for providing vehicle passengers with access to the data transmission network. To evaluate the performance of such automotive wireless access networks, it is necessary to perform experimental measurements. By conducting the throughput measurements in WLAN-based automotive communications testbed, it has been observed that the throughput of the communication channel decreases and the received signal becomes weaker as the vehicle moves away from the wireless access point. The aim of the research is to verify theoretically whether there is a correlation between the received signal power level and the throughput of the communication channel depending on the distance to the transmitter. To calculate the received signal power depending on the distance to the transmitter, a log-normal signal propagation model can be used, which takes into account random signal fluctuations that are described by the Nakagami distribution. Further, based on the obtained results, Shannon’s theorem can be used to calculate the maximum theoretical throughput of the communication channel. The analysis of the obtained results shows that a correlation exists between the received signal power level and the throughput of the communication channel depending on the distance to the transmitter. The performed theoretical calculations justify the experimentally obtained results.