Aug 2026· International Journal of AI Electronics and Nexus Energy· 0 citations· 29 references
TL;DR
A Cognitive TN–NTN Service Orchestration framework that combines Digital Twins and Agentic Artificial Intelligence to achieve resilient network management and enables uninterrupted communication for smart cities, autonomous transportation, disaster recovery, industrial automation, maritime networks, and remote healthcare services is presented.
Abstract
Sixth-generation (6G) communication requires seamless integration of terrestrial networks (TN) and non-terrestrial networks (NTN) to deliver reliable, intelligent, and ubiquitous connectivity. This paper presents a Cognitive TN–NTN Service Orchestration framework that combines Digital Twins and Agentic Artificial Intelligence (AI) to achieve resilient network management. Digital Twins create real-time virtual replicas of terrestrial infrastructure, satellites, UAVs, and high-altitude platforms for continuous monitoring and prediction. Agentic AI employs autonomous agents to analyze network conditions, optimize routing, perform proactive fault detection, and coordinate service migration during failures or congestion. Reinforcement Learning and Graph Neural Networks support adaptive decision-making across heterogeneous 6G environments. Experimental evaluation indicates improved latency, packet delivery ratio, service availability, orchestration efficiency, and resilience compared with conventional orchestration methods. The proposed framework enables uninterrupted communication for smart cities, autonomous transportation, disaster recovery, industrial automation, maritime networks, and remote healthcare services.
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N. J., Jeeva Jothi· International Journal of Com...· 0 citations
The 6G networks require autonomous and smart management of resources to support ultra-dense devices, dynamic traffic and low-latency services. The classic methods of Network Function Virtualization (NFV) orchestration utilize primarily the use of either a static or heuristics algorithm, which constrains their capacity...
Kishore Golla, M. Ramkumar· 2026 4th International Confe...· 0 citations
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M. Bampi, P. H. M. Pereira, E. P. de Freitas· International Conference on...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods...
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Embodied Artificial Intelligence (AI) integrates multimodal large models into Embodied Agents (EAs), driving the evolution of Embodied Edge Intelligence Networks (EEINs) to handle the heterogeneous requests generated by EAs. To guarantee service performance for heterogeneous requests, Service Function Chain (SFC) orche...
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Future 6G services will require strict performance guarantees, especially in terms of delay, end-to-end (e2e) across multiple network domains including packet and radio segments. While deterministic transport and slice-based capacity allocation can improve segment-level performance, ensuring e2e Network Service (NS) pe...
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