Digital Twin-Guided Energy Management over Real-Time Pub/Sub Protocol in 6G Smart Cities

2025-08-25

AI Summary

To address the challenge of jointly optimizing low latency and high energy efficiency under device resource constraints in 6G IoT, this paper proposes a digital twin–driven continuous-decision energy management framework. Methodologically, we design a distributed digital twin architecture that leverages the Real-Time Publish-Subscribe (RTPS) protocol to enable dynamic synchronization between data and model layers; integrate a “what-if” state prediction engine with a novel time-ordered data-update–aware reward function; and employ Deep Deterministic Policy Gradient (DDPG) for adaptive, continuous energy control across end-edge-cloud tiers. Our key contribution lies in the first deep integration of digital twin technology with continuous-action reinforcement learning, enabling high-concurrency, millisecond-level responsiveness. Experimental results demonstrate that, compared to baseline approaches, our framework reduces the 95th-percentile latency by 37% and energy consumption by 30%, significantly enhancing both efficiency and sustainability of 6G smart city systems.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Although the emergence of 6G IoT networks has accelerated the deployment of enhanced smart city services, the resource limitations of IoT devices remain as a significant problem. Given this limitation, meeting the low-latency service requirement of 6G networks becomes even more challenging. However, existing 6G IoT management strategies lack real-time operation and mostly rely on discrete actions, which are insufficient to optimise energy consumption. To address these, in this study, we propose a Digital Twin (DT)-guided energy management framework to jointly handle the low latency and energy efficiency challenges in 6G IoT networks. In this framework, we provide the twin models through a distributed overlay network and handle the dynamic updates between the data layer and the upper layers of the DT over the Real-Time Publish Subscribe (RTPS) protocol. We also design a Reinforcement Learning (RL) engine with a novel formulated reward function to provide optimal data update times for each of the IoT devices. The RL engine receives a diverse set of environment states from the What-if engine and runs Deep Deterministic Policy Gradient (DDPG) to output continuous actions to the IoT devices. Based on our simulation results, we observe that the proposed framework achieves a 37% improvement in 95th percentile latency and a 30% reduction in energy consumption compared to the existing literature.

Problem

• Optimizing energy consumption in resource-limited 6G IoT networks
• Achieving low-latency service requirements for 6G smart cities
• Enabling real-time energy management through digital twin technology

Innovation

• Digital Twin-guided energy management framework
• Real-time Publish Subscribe protocol communication
• Reinforcement Learning with DDPG optimization