A Reliable Service Chain Option for Global Migration of Intelligent Twins in Vehicular Metaverses

Objective As an emerging paradigm that integrates metaverses with intelligent transportation systems, vehicular metaverses are becoming a driving force in the transformation of the automotive industry. Within this context, intelligent twins act as digital counterparts of vehicles, covering their entire lifecycle and managing vehicular applications to provide immersive services. However, seamless migration of intelligent twins across RoadSide Units (RSUs) faces challenges such as excessive transmission delays and data leakage, particularly under cybersecurity threats like Distributed Denial of Service (DDoS) attacks. To address these issues, this paper proposes a globally optimized scheme for secure and dynamic intelligent twin migration based on RSU chains. The proposed approach mitigates transmission latency and enhances network security, ensuring that intelligent twins can be migrated reliably and securely through RSU chains even in the presence of multiple types of DDoS attacks. Methods A set of reliable RSU chains is first constructed using a communication interruption-free mechanism, which enables the rational deployment of intelligent twins for seamless RSU connectivity. This mechanism ensures continuous communication by dynamically reconfiguring RSU chains according to real-time network conditions and vehicle mobility. The secure migration of intelligent twins along these RSU chains is then formulated as a Partially Observable Markov Decision Process (POMDP). The POMDP framework incorporates dynamic network state variables, including RSU load, available bandwidth, computational capacity, and attack type. These variables are continuously monitored to support decision-making. Migration efficiency and security are evaluated based on total migration delay and the number of DDoS attacks encountered; these metrics serve as reward functions for optimization. Deep Reinforcement Learning (DRL) agents iteratively learn from their interactions with the environment, refining RSU chain selection strategies to maximize both security and efficiency. Through this algorithm, the proposed scheme mitigates excessive transmission delays caused by network attacks in vehicular metaverses, ensuring reliable and secure intelligent twin migration even under diverse DDoS attack scenarios. Results and Discussions The proposed secure dynamic intelligent twin migration scheme employs the MADRL framework to select efficient and secure RSU chains within the POMDP. By defining a suitable reward function, the efficiency and security of intelligent twin migration are evaluated under varying RSU chain lengths and different attack scenarios. Simulation results confirm that the scheme enhances migration security in vehicular metaverses. Shorter RSU chains yield lower migration delays than longer ones, owing to reduced handovers and lower communication overhead (Fig. 2). Additionally, the total reward reaches its maximum when the RSU chain length is 6 (Fig. 3). The MADQN approach exhibits strong defense capabilities against DDoS attacks. Under direct attacks, MADQN achieves final rewards that are 65.3% and 51.8% higher than those obtained by random and greedy strategies, respectively. Against indirect attacks, MADQN improves performance by 9.3%. Under hybrid attack conditions, MADQN increases the final reward by 29% and 30.9% compared with the random and greedy strategies, respectively (Fig. 4), demonstrating the effectiveness of the DRL-based defense strategy in handling complex and dynamic threats. Experimental comparisons with other DRL algorithms, including PPO, A2C, and QR-DQN, further highlight the superiority of MADQN under direct, indirect, and hybrid DDoS attacks (Figs. 5～7). Overall, the proposed scheme ensures reliable and efficient intelligent twin migration across RSUs even under diverse security threats, thereby supporting high-quality interactions in vehicular metaverses. Conclusions This study addresses the challenge of secure and efficient global migration of intelligent twins in vehicular metaverses by integrating RSU chains with a POMDP-based optimization framework. Using the MADQN algorithm, the proposed scheme improves both the efficiency and security of intelligent twin migration under diverse network conditions and attack scenarios. Simulation results confirm significant gains in performance. Along identical driving routes, shorter RSU chains provide higher migration efficiency and stronger defense capabilities. Under various types of DDoS attacks, MADQN consistently outperforms baseline strategies, achieving higher final rewards than random and greedy approaches across all scenarios. Compared with other DRL algorithms, MADQN increases the final reward by up to 50.1%, demonstrating superior adaptability in complex attack environments. Future work will focus on enhancing the communication security of RSU chains, including the development of authentication mechanisms to ensure that only authorized vehicles can access RSU edge communication networks.