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

TL;DR

This paper introduces an edge-aware CyberTwin framework that optimizes energy consumption and latency in 6G smart city networks with massive IoT deployments, using hybrid federated learning and digital twins.

Contribution

It presents a novel hybrid federated learning approach combined with digital twins and security measures for energy-latency optimization in large-scale 6G smart city networks.

Findings

52% energy reduction for non-real-time slices

Maintains 0.9ms latency for URLLC applications

Scales to 50,000 devices km with low CPU overhead

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.