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

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.