Adaptive Artificial Intelligence for Resource-Constrained Connected Vehicles in Cybertwin-Driven 6G Network

• Published in: IEEE internet of things journal 2021-11, Vol.8 (22), p.16269-16278
• Main Authors: Shen, Shuaiqi, Yu, Chong, Zhang, Kuan, Ci, Song
• Format: Article
• Language: English
• Subjects: 6G mobile communication; Adaptation models; Adaptive algorithms; Algorithms; Artificial intelligence; Artificial intelligence (AI); Asset management; Complexity; Complexity theory; Computational modeling; Connected vehicles; Customization; cybertwin; dimension reduction; Dimensionality reduction; Feature selection; Model accuracy; New technology; nonadditive measure theory; Resource allocation; User experience; Vehicles; Wireless networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2021.3101231

The emerging technology of cybertwin is expected to bring revolutionary benefits to the sixth-generation (6G) network in respect of communication, resources allocation, and digital asset management. Empowered by ubiquitous artificial intelligence (AI), cybertwin is capable of adjusting the requests for computing resources to support network services by analyzing user's demands for quality of experience and resource scarcity in the market. For resource-constrained applications, such as connected vehicles in the 6G network, cybertwin can intelligently determine the time-varying requests of computing resources for various vehicles at different times. However, the current service architecture executes AI algorithms with universal configurations for all vehicles. This causes the difficulty of customizing the complexity of AI algorithms to maintain adaptive to cybertwin's decisions on dynamic resources allocation. In this article, we propose an adaptive AI framework based on efficient feature selection to cooperate with cybertwin's resource allocation. This proposed framework can adaptively customizing AI model complexity with available computing resources. Specifically, we systematically characterize the aggregated impacts of all feature combinations on the modeling outcomes of AI algorithms. By utilizing nonadditive measures, the interactions among features can be quantified to indicate their contributions to the modeling process. Then, we propose an efficient algorithm to obtain accurate interaction measures for adaptive feature selection to balance the tradeoff between modeling accuracy and computational overhead. Finally, extensive simulations are conducted to validate that our proposed framework substantially reduces the overhead of AI algorithms while guaranteeing desired modeling accuracy for cybertwin-driven connected vehicles in 6G.