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.

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network’s topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks.

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.

Wireless Mesh Networks (WMNs) remain pivotal in various domains due to their versatility and scalability, offering robust and adaptable connectivity in areas where traditional wired networks are impractical. They find extensive use in diverse scenarios like smart cities, disaster recovery, industrial automation, and rural connectivity, underscoring their continued relevance in modern networking applications. Recent research in WMNs has increasingly concentrated on optimizing gateway placement and selection to enhance network performance and ensure efficient data transmission. This paper introduces a novel approach to maximize average throughput by strategically positioning gateways within the mesh topology. Leveraging Coulomb's law, previously employed in network analysis by Zhang et al., this method aims to enhance network performance through optimized gateway placement. Through thorough simulations and analysis, the effectiveness of this approach in improving both throughput and network efficiency is demonstrated. This study contributes to the ongoing evolution of WMN optimization strategies, emphasizing the indispensable role of gateway placement in establishing robust and efficient wireless communication infrastructures. By leveraging physics-based models like Coulomb's law, the proposed approach provides a framework for objectively optimizing gateway placement, thereby addressing a critical aspect of WMN design. These findings offer valuable insights for network designers and operators, guiding informed decision-making processes for gateway deployment in diverse WMN deployments.

Object detection is a fundamental task in computer vision and remote sensing, aimed at recognizing and categorizing different types of objects within images. Unmanned aerial vehicle - based thermal infrared remote sensing provides crucial multi-scenario images and videos, serving as key data sources in public applications. However, object detection in these images remains challenging due to complex scene information, lower resolution compared to visible-spectrum videos, and a shortage of publicly available labeled datasets and trained models. This article introduces a Unmanned aerial vehicle - based thermal infrared object detection framework for analyzing images and videos in public applications and evaluates the performance of YOLOv8n/v8s, YOLOv11n/v11s, and YOLOv12n/v12s models in extracting features from ground-based thermal infrared images and videos captured by Forward-Looking Infrared cameras, as well as from unmanned aerial vehicle - recorded thermal infrared videos taken from various angles. The YOLOv8n/v8s, YOLOv11n/v11s, and the latest YOLOv12n/v12s models were deployed on a Raspberry Pi 5 using the OpenVINO framework. The successful deployment of these models, including the most recent version, demonstrates their feasibility for unmanned aerial vehicle-based thermal infrared object detection. The results show that YOLOv8 and YOLOv11 achieved high accuracy and recall rates of 93% and 92%, respectively, while the YOLOv12 model demonstrated good precision but comparatively lower performance in accuracy and recall, suggesting the possibility for further improvement.

Unmanned Aerial Vehicle Communication Networks (UAVCNs) have emerged as a transformative solution to enable resilient, scalable, and infrastructure-independent wireless communication in urban and remote environments. A key challenge in UAVCNs is the optimal placement of Unmanned Aerial Vehicle (UAV) nodes to maximize coverage, connectivity, and overall network performance while minimizing latency, energy consumption, and packet loss. As this node placement problem is NP-hard, numerous meta-heuristic algorithms (MHAs) have been proposed to find near-optimal solutions efficiently. Although research in this area has produced a wide range of meta-heuristic algorithmic solutions, most existing review articles focus on MANETs with terrestrial nodes, while comprehensive reviews dedicated to node placement in UAV communication networks are relatively scarce. This article presents a critical and comprehensive review of meta-heuristic algorithms for UAVCN node placement. Beyond surveying existing methods, it systematically analyzes algorithmic strengths, vulnerabilities, and future research directions, offering actionable insights for selecting effective strategies in diverse UAVCN deployment scenarios. To demonstrate practical applicability, selected hybrid algorithms are evaluated in a reproducible Python framework using computational time and coverage metrics, highlighting their ability to optimize multiple objectives and providing guidance for future UAVCN optimization studies.