Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications, including environmental monitoring, healthcare, industrial automation, and military operations. The primary challenge in WSNs is to ensure reliable data transmission while maintaining energy efficiency and network longevity. Multi-path data transmission has emerged as a promising technique to enhance the reliability of WSNs by mitigating data loss, reducing congestion, and improving fault tolerance. This paper presents a comprehensive study on multi-path data transmission mechanisms in WSNs, analyzing their impact on network performance, energy consumption, and data reliability. We explore various multi-path routing protocols, including Disjoint Path Routing, Braided Path Routing, and Hybrid Approaches, and assess their effectiveness in different network scenarios. Additionally, we discuss the challenges associated with multi-path data transmission, such as path redundancy, interference, and increased computational overhead. Through extensive simulations and comparative analysis, we demonstrate that multi-path data transmission significantly enhances network reliability while ensuring optimal resource utilization. The findings of this study provide valuable insights for designing robust and efficient WSNs, thereby contributing to advancements in the field of wireless communication.
Anatoly Kitov· International Journal of Dat...· 0 citations
The rapid expansion of scientific publications, experimental data, and digital repositories has made research increasingly data-intensive and computationally complex. Traditional research tools, such as search engines and digital libraries, require substantial manual effort and provide limited support for comprehensive research workflows. This paper proposes an Agentic AI Framework for Autonomous Scientific Research Assistance that integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and collaborative multi-agent systems to automate key research activities. The framework coordinates specialized agents for literature review, knowledge graph construction, hypothesis generation, experiment planning, data analysis, citation management, research validation, and manuscript generation. The architecture combines domain-specific knowledge bases, reinforcement learning-based task optimization, and explainable AI techniques to improve transparency, adaptability, and trustworthiness. It also promotes scientific integrity through citation verification, plagiarism prevention, ethical compliance monitoring, and continuous knowledge updating. Performance is evaluated using literature retrieval accuracy, hypothesis relevance, experiment planning efficiency, collaboration effectiveness, response latency, and manuscript quality. Experimental results indicate that coordinated autonomous agents significantly reduce research time, improve workflow consistency, enhance knowledge discovery, and increase scientific productivity compared with conventional AI-based research assistants. The modular framework supports scalable deployment across cloud, edge, and hybrid environments while enabling interdisciplinary collaboration, providing a reliable foundation for trustworthy AI-assisted scientific research.
Anatoly Kitov, M. Kartsev· International Journal of Eme...· 0 citations