AI Enhanced IoT Framework

• AI Enhanced IoT Framework is a multi-layered system that integrates distributed AI and real-time analytics to improve scalability and security across diverse network segments.
• It employs federated learning and zero-trust principles to automate threat detection, policy enforcement, and dynamic network slicing in complex environments.
• Implementation leverages cloud-native orchestration and edge-AI deployment to ensure robust, reliable IoT connectivity in 5G Advanced/6G TN-NTN ecosystems.

An AI Enhanced IoT Framework integrates artificial intelligence methodologies within heterogeneous, multi-layered Internet of Things systems to address scalability, security, reliability, and automation challenges. This approach orchestrates distributed AI, real-time analytics, and automation across space, aerial, terrestrial, edge, and cloud layers—frequently underpinned by zero-trust and federated learning paradigms. An illustrative realization is the AI-enabled security architecture for 5G Advanced/6G IoT-integrated Terrestrial and Non-Terrestrial Networks (TN-NTN), incorporating satellites, high-altitude platforms, unmanned aerial vehicles, and terrestrial components, with AI-native cloud security and edge AI modules (Maric et al., 7 Aug 2025).

1. Architectural Stratification and Data Flows

The system architecture employs a hierarchical, end-to-end layering model encompassing:

• Space Segment: LEO constellations (321 km–1,500 km, ~30 ms RTT) with phased-array beamforming and optical inter-satellite links for backhaul; MEO (2,000 km–20,000 km) for navigation and load-balancing; GEO (~35,786 km) for high-latency broadcast.
• Aerial Segment: High-Altitude Platform Systems (HAPS) provide persistent cells at stratospheric altitudes, and UAVs act as mobile edge relays/data collectors where terrestrial coverage is absent.
• Terrestrial Segment: Disaggregated 5G Advanced/6G RAN with distributed RUs, DUs, and CUs—deployed on fixed/mobile ground assets and ships/aircraft—and IoT end-devices (sensors, actuators, vehicles, wearables), all characterized by constrained compute, memory, and power profiles.
• Edge and Cloud Layers: Edge nodes (co-located with gNBs, satellite gateways, UAV hubs) run lightweight AI inference and federated learning clients; central AI-native cloud hosts Kubernetes-orchestrated microservices for model aggregation, threat analytics, and policy enforcement.

Data flows traverse: Device ↔ RAN ↔ (satellite/terrestrial) backhaul ↔ edge node ↔ AI cloud, supporting heterogeneous routing over RF/FSO, with dynamic mesh reconfiguration via ISLs. The control/user plane is split, with user traffic steered over NTN in terrestrial failure scenarios. Real-time telemetry (spectrum, packets, device health) is streamed into AI-security modules for continuous assessment.

2. AI Methodologies and Threat Detection Mechanisms

AI-driven modules provide multiple layers of cognitive capability:

• Threat Detection: Streaming deep packet inspection pipelines are augmented with ML/DL, employing features such as packet rate and entropy, alongside neural autoencoders, one-class SVMs for anomaly scoring ($s(x)=\|x-\operatorname{Recon}(x)\|^2$), and ensemble models (e.g., XGBoost, transformers referenced from Jiang2023), optimized for low-rate DDoS and stealth MitM.
• Security Automation: Incident type is classified by ML; orchestration scripts (playbooks) then automate network quarantine or traffic rerouting. Reinforcement-learned policies dynamically adjust slice QoS, firewall rules, or perform beamforming null-steering.
• Policy Enforcement: Dynamic risk-aware policy engines (e.g., Kubernetes Admission Controller) use graph-based risk metrics for micro-segmentation. A zero-trust architecture is enforced using PDP/PIP/PAP modules, wherein the Policy Decision Point evaluates access requests with contextual attributes (device posture, location, slice ID) feeding back into policy refinement loops (Maric et al., 7 Aug 2025).

3. Security Posture: Zero-Trust, Network Slicing, and Adversarial Resilience

The security model combines multiple robust approaches:

• Zero-Trust Principles: All entities are continuously authenticated and authorized, using mutual TLS and hardware attestation. Micro-segmentation confines VNFs/pods to dedicated slices. Fresh session tokens are issued via AI-informed risk scoring at each transaction.
• Network Slicing and Segmentation: Dedicated E2E slices (URLLC, eMBB, mMTC) are mapped to RN/NTN resources, with explicit trust boundaries and inter-slice firewalling. Slice isolation extends across compute, storage, and spectrum budget enforcement.
• Resilience and Adversarial Defenses: Adversarial training injects perturbed samples during federated learning to harden anomaly detectors; redundancy is engineered via multi-path routing across LEO/MEO/GEO layers, and critical URLLC fallback on UAV relays. Software supply-chain security is achieved through signed container images, SBOM tracking, and host OS drift detection (Maric et al., 7 Aug 2025).

4. Federated Learning and Mathematical Formalism

The framework formalizes distributed learning and security constraints as follows:

• Federated Learning Objective:

$$F(w) = \sum_{k=1}^K \frac{n_k}{N} F_k(w), \quad F_k(w) = \mathbb{E}_{x\sim D_k}[\ell(w;x)]$$

with $K$ nodes (gNBs, satellites, IoT gateways) and communication constraints $\sum_k C_k\cdot \Delta w_k \leq B$ (satellite bandwidth), and privacy via secure aggregated local updates.

• Anomaly Scoring Model:

$$s(x) = \left\| f_\theta.\text{encoder}(x) - f_\theta.\text{decoder}(\hat{x}) \right\|_2$$

with flows flagged anomalous if $s(x) > \tau$.

• Network Slicing Constraints:

$\sum_{s=1}^S r_s \leq R_\text{total} \ \text{latency}_s \leq L_\text{max}_s \ \text{throughput}_s \geq T_\text{min}_s$

and the slice configuration $\{r_s\}$ is optimized to maximize weighted utility $\sum_s \alpha_s U_s(r_s)$ under these constraints (Maric et al., 7 Aug 2025).

5. Implementation Strategies and Integration

Implementation leverages modern cloud-native approaches:

• Cloud Orchestration: Microservices based on Kubernetes, with controllers for AI model training, policy enforcement, and FL management. Helm charts deploy AI-security sidecars in cloud-native RAN functions.
• Edge-AI Deployment: TinyML models such as LSTM autoencoders are embedded on edge servers and gNBs for immediate local inference. Model binaries are versioned and distributed via OCI registries; GPU clusters aggregate central models.
• 5G/6G RAN-Core Integration: Network repository functions (3GPP SA-based NRF) are extended with security PDPs for enforcing zero-trust. The RAN Intelligent Controller (RIC) executes AI xApps, enabling real-time slicing adjustments and anti-jamming measures. Service-Based Architecture (SBA) in the core exposes AI-driven policy endpoints for cross-domain queries.
• Edge-Cloud Continuum: Mobile Edge Computing (MEC) servers at sites run federated learning clients; satellite gateways conduct localized FL rounds. Model offloading adapts to current threat severity, with heavy models quantized for resource-constrained environments (Maric et al., 7 Aug 2025).

6. Role within the 5G Advanced/6G IoT TN-NTN Landscape

AI-enhanced IoT frameworks such as described here are foundational for realizing global, robust, and secure 5G Advanced/6G TN-NTN infrastructure:

• AI enables automated threat recognition, closed-loop remediation, and dynamic, risk-based access control in exceptionally heterogeneous, distributed, and intermittently connected environments.
• The architectural layering addresses both latency-sensitive (e.g., URLLC) and throughput-intensive (eMBB) IoT applications, ensuring service availability and security—even across satellite and aerial relays.
• The inclusion of adversarial training, federated intelligence, and zero-trust activities positions the framework as a future-proof blueprint for resilient, large-scale IoT deployments within 5G/6G-integrated TN-NTN (Maric et al., 7 Aug 2025).

7. Summary Table: Key Components and Technical Realization

Layer	Core Functionality	AI/ML Role
Space (LEO/MEO/GEO)	Global backhaul, broadcast, navigation	Edge FL client, telemetry, security relay
Aerial (HAPS/UAV)	Mobile relays, persistent coverage cells	Edge inference, local federated rounds
Terrestrial (RAN/IoT)	Local access, diverse end-devices	TinyML inference, secure onboarding
Edge Nodes	Local inference, pre-aggregation, security	FL clients, anomaly detection, playbooks
AI Cloud	Orchestration, threat analytics, policy	Model aggregation, RL policies, token auth
Security Processes	Zero-trust, micro-segmentation, adversarial	Risk scoring, identity, adversarial FL

The AI Enhanced IoT Framework for TN-NTN realizes a unified, AI-native, zero-trust, federated security posture, leveraging distributed analytics and automation to support robust, globally distributed IoT connectivity in highly heterogeneous, mission- and safety-critical networks (Maric et al., 7 Aug 2025).