CNS Core: Small: Application-Oriented Scheduling for Optimizing Information Freshness in Wireless Networks

无线网络技术的重大进步以及移动设备的激增使以信息为中心的物联网（IoT）系统成为可能，在这些系统中，用户通常需要或更喜欢及时更新信息。最近引入了信息时代（AoI）来量化控制器对远程信息源的知识的新鲜度。虽然AoI是以信息为中心的，但在目前的技术水平上，信息新鲜度的评估主要是以传播为中心的方式进行的。本项目旨在通过揭示相关的基础研究问题，开发通用建模工具，并在一些重要的实际场景中生成有效的网络协议，弥合传输级信息新鲜度与应用级决策之间的差距。本研究整合无线网路、优化理论、控制理论、资讯理论及机器学习等领域的理论研究。这样一个跨学科的项目不仅可以为本科生和研究生提供各种各样的训练项目，还可以激发学生以创新和开放的视角追求高质量的研究。基于信息新鲜度的无线调度算法对下一代无线网络和移动应用至关重要，具有很好的转化为实际解决方案的潜力。所提出的研究有望为具有一些基本智力优势的创新信息新鲜度优化技术做出贡献。这项研究表明，从面向应用程序的角度（即合并用户查询模式、不同信息源之间的相关性或分布式代理之间的协作决策）优化信息新鲜度需要全新的建模/分析技术。通过预测用户查询模式和在应用程序级别合并延迟容忍响应，将开发用于信息新鲜度优化的有效分解技术和低复杂度算法。当存在多个相关信息源时，信息新鲜度优化的一个基本障碍是缺乏定义良好的应用级新鲜度度量；这个问题将得到解决。此外，通过整合领域知识和深度强化学习技术，可以有效地解决由相关性引起的复杂性。由于最近的研究证实了基于csma的调度在新鲜度优化方面的不足，本文提出了一种基于分散tdma的介质访问控制（MAC）协议，并对其进行了数学分析。这种新的MAC协议有望在多智能体决策场景下提供定量的信息新鲜度保证；本部分的研究成果有望在系统层面为面向新鲜度的网络设计提供重要的组成部分。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The significant advancement in wireless networking technologies as well as the proliferation of mobile devices have enabled information-centric Internet of Things (IoT) systems, where timely information updating is normally required or preferred by users. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. While AoI is information-centric, in the present state of the art, the evaluation of information freshness has been conducted mainly in a transmission-centric manner. This project aims to bridge the gap between transmission-level information freshness and application-level decision making by revealing involved fundamental research issues, developing generic modeling tools, and generating effective network protocols in some important practical scenarios. This proposed research integrates theoretical studies in the areas of wireless networking, optimization theory, control theory, information theory, and machine learning. Such an interdisciplinary project will not only provide various training projects to undergraduate and graduate students, but also inspire students to pursue high-quality research with a creative and open-minded perspective. Information freshness based wireless scheduling algorithms are of critical importance to next generation wireless networks and mobile applications, with a good potential to be transformed into practical solutions.The proposed research is expected to contribute to innovative information freshness optimization techniques with some fundamental intellectual merits. This study is to show that optimizing information freshness from an application-oriented angle (that is, incorporating user query patterns, correlations across different information sources, or cooperative decision-making among distributed agents) requires brand-new modeling/analysis techniques. Effective decomposition techniques and low-complexity algorithms for information freshness optimization are to be developed through predicting user query patterns and incorporating delay-tolerant responses at the application level. When multiple correlated information sources present, a fundamental obstacle to information freshness optimization is the lack of a well-defined application-level freshness metric; this issue will be tackled. Moreover, it is to be demonstrated that the intricacies raised by the correlations can be addressed efficiently through integrating domain knowledge and deep reinforcement learning techniques. With recent studies confirming the inadequacy of CSMA-based scheduling in terms of freshness optimization, a decentralized TDMA-based medium access control (MAC) protocol is to be developed and mathematically analyzed. Such a new MAC protocol is expected to provision quantitative information freshness guarantee in the scenario of multi-agent decision making; the outcome in this part hopefully can contribute an important component for freshness-oriented network design in the system level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.