講座名稱：Adaptive Artificial Intelligence for Resource-Constrained Connected Vehicles in Cybertwin-Driven 6G Network

講座人：Kuan Zhang 副教授

講座時間：11月26日9:00

地點(diǎn)：騰訊會議直播（ID：666 672 475）

講座人介紹：

張寬博士現(xiàn)任美國內(nèi)布拉斯加大學(xué)林肯分校電氣與計(jì)算機(jī)工程系副教授。2017-2023年，他在該系任助理教授。他于2016年，在加拿大滑鐵盧大學(xué)獲得電氣與計(jì)算機(jī)工程系博士學(xué)位。2016-2017年，他在滑鐵盧大學(xué)進(jìn)行博士后研究工作。他在國際期刊和會議上發(fā)表超過100篇文章。他的研究方向包括網(wǎng)絡(luò)安全，大數(shù)據(jù)，以及云計(jì)算邊緣計(jì)算等。張博士獲得過IEEE可擴(kuò)展計(jì)算技術(shù)委員會的杰出博士論文獎。他還獲得了多次國際會議的最佳論文獎，包括IEEE WCNC 2013，Securecome 2016和IEEE ICC 2020。他擔(dān)任多個期刊的副編輯，包括IEEE Transactions on Wireless Communications，IEEE Communications Surveys & Tutorials，IEEE Internet-of-Things Journal，以及Peer-to-Peer Network and Applications。

講座內(nèi)容：

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.

主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院