Multi-Agent DRL for Multi-Objective Twin Migration Routing with Workload Prediction in 6G-enabled IoV

2025-05-12

AI Summary

In 6G-enabled Internet of Vehicles (IoV), high vehicle mobility frequently causes communication disruptions with edge servers, severely compromising the continuity of Vehicle Twins (VTs). To address this challenge, this paper proposes an efficient VT migration routing optimization framework. We innovatively design an LSTM-Transformer hybrid time-series load prediction model and develop a Dynamic Masking Multi-Agent Proximal Policy Optimization (DM-MAPPO) algorithm, enabling multi-objective co-optimization of migration decisions under a 6G heterogeneous edge network model. Experimental results demonstrate that our approach reduces migration latency by 20.82% and packet loss rate by 75.07% compared to conventional deep reinforcement learning baselines. Validated on a real-data-driven platform, the method proves both effective and practically deployable for sustaining VT service continuity in dynamic vehicular environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Sixth Generation (6G)-enabled Internet of Vehicles (IoV) facilitates efficient data synchronization through ultra-fast bandwidth and high-density connectivity, enabling the emergence of Vehicle Twins (VTs). As highly accurate replicas of vehicles, VTs can support intelligent vehicular applications for occupants in 6G-enabled IoV. Thanks to the full coverage capability of 6G, resource-constrained vehicles can offload VTs to edge servers, such as roadside units, unmanned aerial vehicles, and satellites, utilizing their computing and storage resources for VT construction and updates. However, communication between vehicles and edge servers with limited coverage is prone to interruptions due to the dynamic mobility of vehicles. Consequently, VTs must be migrated among edge servers to maintain uninterrupted and high-quality services for users. In this paper, we introduce a VT migration framework in 6G-enabled IoV. Specifically, we first propose a Long Short-Term Memory (LSTM)-based Transformer model to accurately predict long-term workloads of edge servers for migration decision-making. Then, we propose a Dynamic Mask Multi-Agent Proximal Policy Optimization (DM-MAPPO) algorithm to identify optimal migration routes in the highly complex environment of 6G-enabled IoV. Finally, we develop a practical platform to validate the effectiveness of the proposed scheme using real datasets. Simulation results demonstrate that the proposed DM-MAPPO algorithm significantly reduces migration latency by 20.82% and packet loss by 75.07% compared with traditional deep reinforcement learning algorithms.

Problem

• Optimizing Vehicle Twin migration in 6G-IoV for seamless service continuity
• Predicting edge server workloads using LSTM-Transformer for migration decisions
• Reducing migration latency and packet loss via DM-MAPPO algorithm

Innovation

• LSTM-based Transformer for workload prediction
• DM-MAPPO algorithm for optimal migration routes
• Practical platform with real dataset validation