This study investigates the development of network simulation applications using visual programming languages, aiming to map current trends, tool preferences, and key implementation challenges in educational and experimental contexts. Through a structured literature review of 28 academic sources, the research identifies the increasing adoption of visual-based interfaces, such as VB.NET, C# WinForms, and tools like Cisco Packet Tracer, as essential platforms to support interactive learning in computer networking. Visual simulation environments are found to enhance conceptual understanding, reduce cognitive load, and improve student engagement, particularly among non-technical users. Despite these advantages, the study reveals several limitations inherent in visual simulation tools. These include lack of modularity, limited protocol validation, and constrained interoperability across different vendors and platforms. Moreover, most existing tools operate in standalone environments and have not yet adapted to cloud-based or scalable architecture. The findings suggest a pressing need for hybrid simulation models that combine the intuitive user experience of GUI-based applications with the technical depth and flexibility of CLI or open API backends. This research contributes a systematic synthesis of the strengths and gaps within the current ecosystem of visual network simulators. It provides practical recommendations for educators, developers, and researchers aiming to build more adaptive, extensible, and context-aware simulation environments that align with modern educational and technological needs.

The rapid proliferation of Internet of Things (IoT) devices has introduced significant challenges, particularly in managing electronic noise, which can degrade system performance and data accuracy. This study explores the integration of IoT with edge computing as a novel approach to mitigate electronic noise. By leveraging edge computing, data processing is performed locally, reducing transmission distances and enhancing real-time noise reduction. The research employs a combination of field experiments and computational simulations to evaluate the effectiveness of this integration. Key metrics such as noise reduction level, system latency, energy efficiency, and signal quality were measured before and after the implementation of noise mitigation technologies. Results indicate a significant reduction in electronic noise, with noise levels decreasing from 75 dB to 55 dB (a 26.67% reduction). System latency improved by 66.67%, dropping from 120 ms to 40 ms, while energy efficiency increased by 30%. Signal quality, measured by Signal-to-Noise Ratio (SNR), improved by 100%, from 10 dB to 20 dB. These findings demonstrate that the integration of IoT and edge computing effectively mitigates electronic noise, enhances system performance, and optimizes energy consumption. The study concludes with recommendations for further optimization of signal processing algorithms and testing in more complex industrial environments.