| 技術摘要en |
Many applications require large volumes of real-time information to function effectively. We have developed highly efficient algorithms — including online network scheduling, online coding design, and online incentive mechanism design — to ensure the timeliness of information within networks characterized by high uncertainty, particularly when such uncertainties cannot be accurately described by stable probabilistic models. We anticipate that our research will have significant impacts both in mathematical theory and practical applications, supporting the development of future network systems for real-time information updates.
Our research primarily supports applications that demand substantial real-time data. Examples include real-time route planning, which relies on up-to-date traffic and public transit information, and investment and financial applications, which require real-time stock market and exchange rate data. Additionally, our work can be applied to large-scale intelligent systems. For instance, in future intelligent transportation systems, massive networks of sensors will collect real-time data to enable the planning of safe driving services and the prevention of major incidents.
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