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.
This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems and investigates the application of advanced machine learning paradigms specifically deep reinforcement learning, federated learning, and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement.
N. P 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 to adjust to the dynamically evolving network conditions. Additionally, the current digital twin and distributed learning systems are characterized by a high level of synchronization overhead and poor automation abilities. To cope with these issues, this paper suggests a Federated Intelligence-based Digital Twin-Assisted Intent-Directed Autonomous Virtual Network Function (VNF) Orchestration. The proposed methodology combines the idea of digital twins to represent a real-time network with the idea of federated learning so that one can train models using a set of distributed edge nodes and maintain data privacy. In Python, the machine learning model is applied to interpret the network traffic data and forecast the demands of resources to be efficient in the orchestration of VNF. The experimental findings indicate that the suggested framework with such a high prediction precision of about 98, a higher usage of resources and a lower latency rate. The paper concluded that incorporating digital twins and federated intelligence could be useful in future 6G networks management to achieve high degrees of automation, scalability, and resilience.
Kishore Golla, M. Ramkumar· 2026 4th International Confe...· 0 citations
Heterogeneous multi-UAV fleets act as highly dynamic mobile Internet of Things (IoT) nodes, but they often integrate platforms with incompatible telemetry and control semantics, hindering safe and scalable coordination. This paper presents an Asset Administration Shell (AAS)-driven digital twin architecture for the cloud continuum that decouples protocol translation from mission orchestration, pushing compute power closer to the edge. The first contribution of this work is a four-layer communication model mapped across the IoT and edge continuum, spanning physical-digital synchronization at the network edge, inter-digital-twin interaction, digitaltwin/GCS supervision in the fog/cloud layer, and a safety-critical physical/GCS bypass. The second contribution is a UAV AAS submodel that standardizes runtime state, energy, payload, and wireless QoS descriptors. The third contribution is an analytical validation framework based on bounded digital-twin staleness $\left(\Delta S_{\max }=30 ~\text{ms}\right)$ to evaluate semantic task reallocation and QoS-aware telemetry adaptation. At a representative cruise speed of 15 m/s, the worst-case position uncertainty induced by semantic latency is 0.45 m, which is acceptable for highlevel mission handoff. For degraded links, stability conditions are derived for queue-bounded latest-state forwarding and hysteresis-based telemetry control. The analysis indicates that AAS enables protocol-agnostic interoperability while preserving orchestration timeliness.
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 is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
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) orchestration has emerged as a critical solution, involving the sequential deployment of Network Functions (NFs) to satisfy customized service requirements. However, realizing SFC orchestration in EEINs presents several challenges, including limited forwarding performance, dynamic environment evolution, and high-dimensional decision spaces. To tackle these issues, we present A2ProSFC, an agentic AI-enabled SFC orchestration system that facilitates perception–reasoning–action loops by leveraging programmable switches. Specifically, we formulate a long-term SFC orchestration problem aimed at maximizing served SFC throughput while ensuring system load balancing. Subsequently, we employ Lyapunov optimization to decouple the long-term orchestration into a sequence of online optimization subproblems and design DiffOrch, a diffusion-based SFC orchestration algorithm. By leveraging In-band Network Telemetry (INT), DiffOrch perceives network state information and adaptively generates orchestration decisions. Furthermore, we design a pipeline integrating INT perception and SFC orchestration to validate the system’s effectiveness. Experimental results demonstrate that A2ProSFC improves throughput by 40.53% and enhances load balancing efficiency by 36.97% compared to existing baselines.
Tianhao Ouyang, Yichi Zhang, Xiaoxu Ren et al.· IEEE Transactions on Cogniti...· 0 citations
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) performance remains challenging as it requires making decisions Near–Real-Time (Near-RT) on a per-service basis, which does not fit well within the typical centralized control and orchestration hierarchy. Multi-agent systems (MAS), where a number of distributed agents collaborate, has demonstrated its capabilities for such Near-RT control. Agents equipped with Deep Reinforcement Learning (DRL) engines autonomously made traffic routing decisions based on e2e telemetry measurements. In this paper, we extend such MAS solutions for NS traffic routing focused on covering several issues that appear under frequent NS reconfiguration, e.g., caused by end device mobility. In addition, we define a lifecycle for NS operation that includes the initial MAS deployment, model reconfiguration during operation, and NS reconfiguration. The proposed lifecycle requires the definition of DRL training and validation procedures to produce models ready to be deployed with guaranteed performance under certain network conditions. In addition, model selection algorithms are defined for the lifecycle scenarios. In case of NS reconfiguration, a procedure for probe testing the actual network conditions is proposed to improve model selection. Evaluation across a meaningful set of network and traffic scenarios shows that the MAS is able to maintain e2e delay guarantees under all the lifecycle scenarios.
H. Shakespear-Miles, S. Barzegar, M. Ruiz et al.· IEEE Transactions on Network...· 0 citations