A Constructive Analysis on Machine Learning Integration in High Delay Tolerant Networking (HDTN)

This research provides a comprehensive investigation of the integration of Machine Learning (ML) in High-Rate Delay Tolerant Networking (HDTN), exploring the synergy between ML techniques and HDTN to enhance communication in environments with intermittent connectivity. A robust investigation of various ML strategies was applied to optimize routing, improve data reliability, and efficiently manage network resources. The proposed investigation spans multiple ML approaches, including decision trees, Bayesian classifiers, and neural networks, highlighting their impact on network adaptability and performance in challenging environments such as space communications and disaster zones. Comprehensive and critical review demonstrated by this research illustrates that ML-enhanced DTNs significantly outperform traditional routing methods, offering higher throughput and reduced error rates. Additionally, this research explored the potential of ML to anticipate and dynamically respond to network changes, thereby increasing the resilience and efficiency of communications. Additionally, this research illustrates future research directions, emphasizing the need for scalable ML models and enhanced security protocols for DTNs, aiming to prepare these networks for more complex operational scenarios. This study not only sheds light on significant contributions but also charts a course for ongoing advancements in ML-integrated networking solutions.