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Machine Learning for NTN-Assisted IoT:A Bibliometric-Assisted Survey of Optimization across Trajectory,Resource,Energy,and Security Aspects 认领 引用
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作者 Oluwatosin Ahmed Amodu Zurina Mohd Hanapi +5 位作者 Chedia Jarray Huda Althumali Faten A.Saif Raja Azlina Raja Mahmood Mohammed Sani Adam Nor Fadzilah Abdullah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期168-235,共68页
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 terre... 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 citationbased 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 multiagent 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. 展开更多
关键词 Machine learning deep learning reinforcement learning deep reinforcement learning satellites unmanned aerial vehicles drones altitude platforms Internet of Things wireless sensor networks
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Machine Learning-Enabled NTN-Assisted IoT:Mapping the Security Landscape 认领 引用
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作者 Oluwatosin Ahmed Amodu Zurina Mohd Hanapi +5 位作者 Raja Azlina Raja Mahmood Faten ASaif Huda Althumali Chedia Jarray Umar Ali Bukar Mohammed Sani Adam 《Computers, Materials & Continua》 SCIE EI 2026年第7期149-206,共58页
Non-terrestrial networks(NTNs),encompassing unmanned aerial vehicles(UAVs),low-/high-altitude platforms(LAPs/HAPs),and satellite systems,are increasingly enabling Internet of Things(IoT)applications beyond the limits ... Non-terrestrial networks(NTNs),encompassing unmanned aerial vehicles(UAVs),low-/high-altitude platforms(LAPs/HAPs),and satellite systems,are increasingly enabling Internet of Things(IoT)applications beyond the limits of terrestrial infrastructure.By combining UAV mobility with satellite and HAP coverage,NTN-assisted IoT supports diverse use cases,including remote sensing,smart cities,intelligent transportation,and emergency response.This paper presents a systematic mapping of machine learning(ML)research in NTN-assisted IoT with a focus on security-related aspects.A keyword co-occurrence analysis of over 2000 publications identifies twelve thematic clusters,including three clusters directly related to security,privacy,and trust.Cluster interconnections are analyzed to reveal dominant research trends and technological dependencies.The first security-focused cluster addresses access control,authentication,privacy preservation,and ML-based intrusion detection in Internet of Drones(IoD)and satellite-enabled systems,while also highlighting feature selection and energy-aware design.The second cluster centers on edge computing-enabled localization and privacy,linking technologies such as GPS,RSSI,LoRaWAN,and differential privacy for smart-city deployments.The third cluster emphasizes blockchain-enabled trust mechanisms,integrating blockchain with aerial image classification,intrusion detection,and secure coordination in IoD environments.Using a connectivity-driven analysis,anchor keywords with strong intra-cluster associations are identified and discussed alongside representative literature.Finally,emerging low-frequency themes are used to outline future directions,including AI-enabled security,trustworthy edge intelligence,autonomous and resilient robotic systems,predictive cyber resilience,and secure cognitive communication for next-generation NTN-assisted IoT. 展开更多
关键词 Non-terrestrial networks Internet of Things Internet of Drones satellites UAVs edge computing localization intrusion detection privacy federated learning blockchain
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