@Article{cmes.2026.077054,
AUTHOR = {Oluwatosin Ahmed Amodu, Zurina Mohd Hanapi, Chedia Jarray, Huda Althumali, Faten A. Saif , Raja Azlina Raja Mahmood, Mohammed Sani Adam, Nor Fadzilah Abdullah},
TITLE = {Machine Learning for NTN-Assisted IoT: A Bibliometric-Assisted Survey of Optimization across Trajectory, Resource, Energy, and Security Aspects},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/26799},
ISSN = {1526-1506},
ABSTRACT = {Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection, we analyze representative and influential works across four core optimization themes: trajectory planning, resource allocation, energy utilization, and security. We develop a taxonomy that captures problem types, learning approaches, architectural configurations, and cross-layer constraints, and discuss insights, complemented by a focused review of top-cited contributions in each theme as well as discussions relating to their complexities and practicality. Our analysis reveals clear methodological trends, including the growing use of deep and multi-agent reinforcement learning, the emergence of distributed intelligence through federated learning, and the increasing interplay among mobility, computation, communication, resource allocation, energy optimization and security. Finally, we highlight key lessons and future research opportunities related to scalable cooperative learning, energy-efficient operation, secure distributed intelligence, and multi-tier optimization across space–air–ground integrated networks, offering a roadmap toward resilient and intelligent 6G-era connectivity.},
DOI = {10.32604/cmes.2026.077054}
}