Nowadays, Wireless Mesh Networks (WMNs) are widely deployed in communication areas due to their ease of implementation, dynamic self-organization, and cost-effectiveness. The design of routing protocols is critical for ensuring the performance and reliability of WMNs. Although there have been numerous experimental works on WMNs in the past decade, only a few of them have been tested in real-world scenarios. This article presents a comparative analysis of three proactive routing protocols, OLSR, BATMAN, and Babel, using Raspberry Pi 4 devices. The evaluation, conducted at Al-Farabi Kazakh National University, covers both indoor and outdoor scenarios, focusing on key metrics such as bandwidth, Packet Delivery Ratio (PDR), and jitter. In outdoor scenarios, OLSR achieved the highest bandwidth at 2.9 Mbps, while BATMAN and Babel lagged. Indoor tests revealed that Babel initially outperformed with the highest bandwidth of 57.19 Mb/s but suffered from scalability issues, while BATMAN and OLSR exhibited significant declines in performance as network size increased. For PDR, BATMAN performed best with a decline from 100% to 42.8%, followed by OLSR with a moderate drop, and Babel with the greatest decrease. For jitter, OLSR showed the most stable performance, increasing from 0.281 ms to 2.58 ms at eleven nodes, BATMAN exhibited moderate increases, and Babel experienced the highest rise.

Automatic Modulation Classification (AMC) is an essential technology that is widely applied into various communications scenarios. In recent years, many Machine Learning and Deep-Learning methods have been introduced into AMC, and a lot of them apply different approaches to eliminate interference in complex Multiple-Input and Multiple-Output (MIMO) signals and improve classification performance. However, in practical communication systems, the perfect elimination of MIMO signal interference is impossible, and therefore classification performance suffers. In this paper, we propose a new AMC algorithm for MIMO system based on mutual information (MI) features extraction, which does not require a large amount of training data and the elimination of MIMO signal interference. In this approach, features based on mutual information are extracted using In-Phase and Quadrature (IQ) constellation diagrams of MIMO signals, which have not been explored previously. Our method can be effective since mutual information considers the interdependencies among variables and measures how much information about one variable reduces uncertainty about another, providing a valuable perspective for extracting higher-level and interesting features from the data. The effectiveness of our method is evaluated on several model and real-world datasets, and its applicability is proven.

The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hole-neutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron star-black hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

A routing algorithm is the most fundamental problem in complex network communication. In complex networks, the amount of computation increases as the number of nodes increases which reduces routing performance. In this paper, we propose a routing algorithm for software-defined networking (SDN) based on a box-covering (BC) algorithm. It is known that using the BC algorithm it is possible to increase performance in complex SDN. We partition the entire SDN network into subnets using three existing box-covering methods such as MEMB, GC and CIEA, then we use Dijkstra’s algorithm to find the shortest path between subnets and within each subnet. We compared all box-covering algorithms and found that the GC algorithm has the highest performance for SDN routing.

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.