Jul 2026· International Conference on Future Internet of Things and Cloud· pp. 421-427· 0 citations· 22 references
Abstract
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.
An agentic OP framework autonomously reconciles stringent Service Level Agreements (SLAs) while enhancing infrastructure energy efficiency and establishing a scalable blueprint for cross-domain Network-as-a-Service (NaaS) models that align standardised exposure with 6G autonomous requirements.
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
High penetrations of distributed energy resources require energy regulation that combines cloud-level global optimization with edge-level fast response. This paper proposes EC-HDT, a device-edge-cloud hierarchical digital twin in which a lightweight graph-attention-temporal-convolution estimator reconstructs local states under asynchronous, noisy, and missing measurements, while a cloud predictor and model predictive controller perform rolling economic optimization. A five-factor decision weight based on communication latency, information freshness, estimation confidence, operational risk, and edge computational load continuously allocates control authority between edge and cloud, and a quadratic-programming safety layer enforces physical constraints. On the IEEE 33-bus system, EC-HDT achieves a nodal-voltage MAE of 0.0076 p.u., mean/P95 end-to-end latencies of 56.4/89.4 ms, and a 99.2% control success rate; the daily operating cost is 3.51% lower than that of the fixed-fusion scheme. The results indicate that state-aware edge-cloud coordination can improve the latency-economy-safety trade-off in distribution-system regulation.
A Federated Cognitive Digital Twin (FCDT) architecture that combines federation and cognition within a unified approach improves scalability, responsiveness, and decision-making in complex distributed CPSs.
This work introduces KAT-MAPPO, a Kolmogorov–Arnold Transformer-enhanced Multi-Agent Proximal Policy Optimization scheme that significantly outperforms existing baselines and highlights the promise of reinforcement learning considering temporal dynamics for efficient offloading in heterogeneous CEG systems.
Chenyang Wang, Qifeng Han· ACM Transactions on Internet...· 0 citations
The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns.
Fabio Orazio Mirto, Giuseppe Tricomi, L. D’Agati et al.· arXiv.org· 0 citations
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