The development of digital technology has spawned the concept of the Internet of Things (IoT). The concept basis is the machine-to-machine interaction (M2M) technology, which allows devices to exchange information. The most effective data transmission medium for M2M devices is mobile communications. Rapid growth of machine-to-machine М2М traffic in mobile communication network defines the actuality of the research problem, its features and characteristics. Research outcomes are indispensable at the network modeling, planning, analyzing the М2М traffic impact at quality of service (QoS) of mobile network communication. The article analyzes the real traffic in the LoraWan network. Aggregated traffic coming to the network server from all devices is considered. To model the М2М batch traffic, apart from specifying the statistic characteristics it is necessary to assess its self-similarity. In order to define the traffic self-similarity there has been computed Hurst parameter. On the basis of STATISTICA programs batch we have conducted statistical analysis and short-term forecasting of real М2М traffic by method of exponential smoothing.

The rapid growth of internet of things (IoT) devices generate highly variable and self-similar traffic patterns, creating challenges for maintaining quality of service (QoS) in modern telecommunication networks. Accurate short-term forecasting of such traffic is essential for efficient resource allocation, yet its fractal characteristics and long-range dependence complicate prediction. This study investigates the use of simple exponential smoothing for short-term forecasting of self-similar IoT traffic by evaluating three smoothing coefficients (a=0.1, 0.5, and 0.9). The Hurst exponent (H=0.5) confirms the presence of self-similarity in the observed traffic. Experimental results show that a=0.1 provides the highest prediction accuracy, achieving a mean absolute percentage error (MAPE) of 25.82% when forecasting traffic values within a 32-minute horizon. The method effectively captures underlying trends while reducing noise sensitivity. These findings demonstrate that exponential smoothing offers a lightweight, interpretable, and practical solution for real-time IoT traffic forecasting, supporting dynamic network load management under highly variable traffic conditions.

This paper discusses some modes of signal transmission for corporate systems using the technology of virtual private networks VPN. A very important property of tunnels is the ability to differentiate different types of traffic and assign them the necessary service priorities. In this work, research has been carried out and comparative characteristics of signal transmission modes using virtual networks have been obtained to identify the effectiveness of the network in various modes of organizing a virtual network, and to optimize a virtual network in order to identify an effective method for organizing a VPN. Also, the work analyzes the specifics of the work of corporate information systems and networks intended for their maintenance, showed that for building a corporate network it is advisable to use virtual private network (VPN) technology, which makes it possible to ensure the fulfillment of the basic requirements for the security and quality of customer service and applications and the current state and direction of development of VPN technology when creating new generation corporate networks, while highlighting the main tasks that need to be addressed when creating a network. Some features of setting up an IPSec VPN server for corporate networks are considered.

This paper proposes a modified approximating function (MAF)-based analytical method for broadband impedance matching in radio-electronic systems. Unlike traditional Chebyshev and Butterworth approaches, which rely on fixed pole distributions and predefined amplitude responses, the proposed method analytically embeds load-specific transmission zeros directly into the approximation function. This modification enables more accurate reconstruction of frequency-dependent impedance behavior without increasing the network order or circuit complexity. The method establishes a unified analytical synthesis framework linking impedance modeling, ladder-network realization, and constrained optimization. Validation was performed over the 1–10 GHz band using numerical simulations, Monte Carlo tolerance analysis, and prototype measurements. Compared with classical Chebyshev and Butterworth designs, the MAF-based approach achieves a 15–25% reduction in maximum reflection coefficient, a 30–40% decrease in optimization iterations, and improved robustness, with reflection variations remaining within 2% under ±10% parameter deviations. The results confirm that the proposed method provides superior analytical flexibility, improved matching accuracy, and reduced computational effort, making it suitable for automated broadband radio frequency (RF) design applications.

Short-Wave Infrared (SWIR) remote sensing plays a significant role in environmental monitoring, agricultural analysis, and nanosatellite-based Earth observation applications. Existing remote sensing frameworks suffer from limitations such as inefficient spectral band selection, high computational complexity, and lack of intelligent optimization techniques for compact nanosatellite payload systems. This study aims to develop an intelligent filter selection system for a four-band SWIR optical payload using multispectral satellite imagery and deep learning (DL)-based optimization techniques. The proposed framework focuses on improving spectral feature extraction, environmental condition classification, and nanosatellite payload efficiency. A multispectral field image dataset was utilized for experimental analysis, where preprocessing techniques, including atmospheric correction and Z-score normalization, were applied. Mutual Information (MI)-based optimal band selection and SWIR filter mapping were performed to identify the significant spectral bands B05, B08, B11, and B12. Feature extraction was conducted using raw SWIR band values and spectral band ratios. A Vision Transformer (ViT) model was employed for environmental condition classification while the Whale Optimization Algorithm (WOA) was integrated to optimize model parameters and improve convergence performance. The proposed ViT–WOA framework achieved superior classification performance with 96.93% accuracy, 97.19% precision, 96.93% recall, 96.92% F1-score. and a Kappa coefficient of 0.9540. The framework effectively classified cloudy, rainy, and sunny environmental conditions with reduced classification loss and improved spectral feature learning efficiency. The proposed system demonstrated reliable SWIR spectral analysis and intelligent payload optimization for nanosatellite remote sensing applications. The integration of transformer-based learning and metaheuristic optimization provided an effective solution for efficient Earth observation and environmental monitoring systems.

Wildfires are significant ecological and environmental disasters, impacting forests, ecosystems, climate stability and human life. The visible-spectrum imagery-based traditional wildfire monitoring system can fail to perform well in the presence of smoke, haze and low lighting. A number of machine learning and deep learning techniques have been proposed, but most of the studies do not provide an integrated Short-Wave Infrared (SWIR) optical-electronic payload framework along with an intelligent optimization technique. The objective of this research is to design an intelligent SWIR-based optical-electronic payload architecture for accurate detection and remote sensing of wildfire and Earth applications via deep learning and optimization techniques. The proposed framework is based on Sentinel-2 SWIR satellite data layers with wildfire and non-wildfire samples. To enhance the quality of the images and the representation of their spectral domain, the following preprocessing operations are carried out: resizing, image normalization, SWIR band extraction, and data augmentation. The following spectral feature extraction techniques are then used: burn area analysis, vegetation stress analysis, and thermal anomaly detection. The framework also incorporates SWIR optical payload design, electronic subsystem development and SWIR InGaAs sensor modeling. Finally, a Hybrid Convolutional Neural Network (CNN)–Residual Network 50 (ResNet50) model optimized by Grey Wolf Optimization (GWO) is used for wildfire classification and hyperparameter tuning. The proposed framework achieved an accuracy of 91.03%, precision of 91.27%, recall of 91.03%, and F1-score of 91.01%. The wildfire detection capability, classification robustness, and convergence performance were enhanced through the integration of SWIR spectral analysis, hybrid deep learning and GWO. The proposed framework offers an effective and trustworthy solution for intelligent wildfire monitoring and Earth remote sensing applications with enhanced spectral sensing and classification performance.