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

CNS 核心：小型：面向应用的调度，用于优化无线网络中的信息新鲜度

• 批准号: 2008092
• 负责人: Yu Cheng
• 金额: $ 42.05万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008092&HistoricalAwards=false
• 关键词: CNS; Core; Small; Application; Oriented

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.

无线网络技术以及移动设备的扩散的重大进步已经实现了以信息为中心的物联网（IoT）系统，在这些系统中，通常需要或更新用户的信息更新。最近引入了信息时代（AOI），以量化控制器对远程信息源的知识的新鲜度。尽管AOI是以信息为中心的，但在目前的最新状态下，新鲜度的评估主要是以以传输为中心进行的。该项目旨在通过揭示涉及的基本研究问题，开发通用建模工具并在某些重要的实践场景中生成有效的网络协议来弥合传输级信息新鲜度和应用程序级决策之间的差距。该提出的研究将理论研究纳入了无线网络，优化理论，控制理论，信息理论和机器学习领域。这样的跨学科项目不仅将为本科生和研究生提供各种培训项目，而且还可以激发学生以创造性和开放的观点从事高质量的研究。基于信息新鲜的无线调度算法对于下一代无线网络和移动应用程序至关重要，具有良好的潜力，可以转变为实用解决方案。拟议的研究有望促进创新信息的新鲜度优化技术，并具有一些基本的知识分子。这项研究是为了表明，从面向应用程序的角度（即，包括用户查询模式，不同信息源之间的相关性或分布式药物之间的合作决策）优化信息新鲜度需要全新的建模/分析技术。有效的分解技术和低复杂性算法以通过预测用户查询模式并在应用级别结合延迟耐受性响应来开发新鲜度优化的信息。当存在多个相关的信息源时，信息新鲜优化的基本障碍是缺乏定义明确的应用程序级新鲜度指标；这个问题将被解决。此外，要证明，相关性提出的复杂性可以通过整合域知识和深度强化学习技术有效地解决。随着最近的研究证实了基于新鲜度优化的基于CSMA的调度的不足，将开发并通过数学分析基于TDMA的媒介访问控制（MAC）方案。这种新的MAC协议有望在多代理决策的情况下提供定量信息新鲜保证；希望这一部分的结果可以为系统级别的新鲜型网络设计做出重要组成部分。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来提供支持。

项目成果

A Self-Supervised Learning Approach for Accelerating Wireless Network Optimization

• DOI: 10.1109/tvt.2023.3244043
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Shuai Zhang;O. Ajayi;Yu-long Cheng

Age of Local Information for Fusion Freshness in Internet of Things

• DOI: 10.1109/iccc57788.2023.10233592
• 发表时间: 2023-08
• 期刊: 2023 IEEE/CIC International Conference on Communications in China (ICCC)
• 作者: Taige Chang;Xianghui Cao;Y. Cheng

Machine Learning Assisted Capacity Optimization for B5G/6G Integrated Access and Backhaul Networks

• DOI: 10.1109/infocomwkshps57453.2023.10225946
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: O. Ajayi;Shuai Zhang;Yu-long Cheng

Adaptive Messaging based on the Age of Information in VANETs

VANET 中基于信息时代的自适应消息传递

• DOI: 10.1109/globecom48099.2022.10000671
• 发表时间: 2022
• 期刊: 2022 IEEE Global Communications Conference
• 作者: I Parella, Jordi Marias;Ajayi, Oluwaseun T.;Cheng, Yu
• 通讯作者: Cheng, Yu

Deep Reinforcement Learning for Scheduling in Multi-Hop Wireless Networks : Invited Paper

• DOI: 10.1109/mass52906.2021.00010
• 发表时间: 2021-10
• 期刊: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Shuai Zhang;Bo Yin;Yu Cheng