讲座名称：Adaptive Artificial Intelligence for Resource-Constrained Connected Vehicles in Cybertwin-Driven 6G Network
讲座人：Kuan Zhang 副教授
地点：腾讯会议直播（ID：666 672 475）
张宽博士现任美国内布拉斯加大学林肯分校电气与计算机工程系副教授。2017-2023年，他在该系任助理教授。他于2016年，在加拿大滑铁卢大学获得电气与计算机工程系博士学位。2016-2017年，他在滑铁卢大学进行博士后研究工作。他在国际期刊和会议上发表超过100篇文章。他的研究方向包括网络安全，大数据，以及云计算边缘计算等。张博士获得过IEEE可扩展计算技术委员会的杰出博士论文奖。他还获得了多次国际会议的最佳论文奖，包括IEEE WCNC 2013，Securecome 2016和IEEE ICC 2020。他担任多个期刊的副编辑，包括IEEE Transactions on Wireless Communications，IEEE Communications Surveys & Tutorials，IEEE Internet-of-Things Journal，以及Peer-to-Peer Network and Applications。
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 can adjust 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. To this end, we propose an adaptive AI framework based on efficient feature selection to cooperate with cybertwin’s resource allocation. The proposed framework can adaptively customize 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.