Optimizing Energy and Latency in 6G Smart Cities with Edge CyberTwins

2025-11-02

AI Summary

To address the coupled energy consumption–latency optimization challenge in heterogeneous network slicing for massive IoT deployments (>50,000 devices/km²) in 6G smart cities, this paper proposes CyberTwin, an edge-aware framework. It innovatively integrates centralized AI-driven scheduling with distributed hybrid federated learning, establishing a three-layer architecture—physical layer, digital twin layer, and decision layer. Key innovations include compressed-sensing-based digital twin modeling, renewable-energy-aware resource allocation, and PUF (Physically Unclonable Function)-enabled security authentication. NS-3 simulations demonstrate that CyberTwin reduces energy consumption by 52% for non-real-time slices, achieves ultra-reliable low-latency communication (URLLC) latency of 0.9 ms, attains 99.1% SLA compliance, and incurs CPU overhead below 25%. The framework delivers a scalable, secure, and low-overhead system-level solution for joint energy-efficiency and latency optimization in large-scale 6G network slicing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The proliferation of IoT devices in smart cities challenges 6G networks with conflicting energy-latency requirements across heterogeneous slices. Existing approaches struggle with the energy-latency trade-off, particularly for massive scale deployments exceeding 50,000 devices km. This paper proposes an edge-aware CyberTwin framework integrating hybrid federated learning for energy-latency co-optimization in 6G network slicing. Our approach combines centralized Artificial Intelligence scheduling for latency-sensitive slices with distributed federated learning for non-critical slices, enhanced by compressive sensing-based digital twins and renewable energy-aware resource allocation. The hybrid scheduler leverages a three-tier architecture with Physical Unclonable Function (PUF) based security attestation achieving 99.7% attack detection accuracy. Comprehensive simulations demonstrate 52% energy reduction for non-real-time slices compared to Diffusion-Reinforcement Learning baselines while maintaining 0.9ms latency for URLLC applications with 99.1% SLA compliance. The framework scales to 50,000 devices km with CPU overhead below 25%, validated through NS-3 hybrid simulations across realistic smart city scenarios.

Problem

• Optimizing energy-latency trade-offs in 6G smart city networks
• Managing conflicting requirements across heterogeneous network slices
• Scaling solutions for massive IoT deployments exceeding 50,000 devices

Innovation

• Edge CyberTwin framework integrates hybrid federated learning
• Combines centralized AI scheduling with distributed federated learning
• Uses compressive sensing digital twins and renewable energy allocation