EAGER: Collaborative Research: Fundamental Limits on Information Freshness

EAGER：协作研究：信息新鲜度的基本限制

• 批准号: 1836695
• 负责人: Andrew Klein
• 金额: $ 9.56万
• 依托单位: Western Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836695&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Fundamental; Limits

Information freshness is of critical importance in a variety of networked monitoring and control systems such as Internet of Things (IoT), intelligent vehicular systems, feedback in wireless communication systems, environmental monitoring, robotic networks, and real-time monitoring and control in cyber-physical systems. In addition it is important in several information-update and data analytics applications including financial trading, social networks, crowdsourcing, consensus systems, and online learning. In all these applications, stale information can lead to incorrect decisions, unstable control loops, and even compromises in safety and security. Since timely updates are critical in these and many other contemporary applications, an important problem is to understand the fundamental limits of information freshness in networked status update systems. This project will further our understanding of the fundamental limits of information freshness in general multi-source/multi-monitor settings, and will develop strategies to achieve these limits. The project also includes engaging research experiences for both undergraduate and graduate students.The approach in this project differs from prior work in age of information (AoI) by considering general multi-source/multi-monitor networks with explicit contention and using techniques from graph theory to study information freshness under the AoI metric in general network topologies with interference constraints. This approach is expected to lead to new insights in realistic networks with general topologies. The research plan is organized into three related tasks to better understand the theoretical foundations of AoI in the explicit contention framework: (i) simultaneous transmission and network coding, (ii) generalized information mappings and age weightings, and (iii) large-network limits. The focus in all of these tasks is on characterizing fundamental limits of information freshness and developing strategies to approach or achieve these limits in general classes of wireless and wired networks. The proposed work will generalize the explicit contention framework for AoI to a rich, realistic class of problem settings including settings where nodes can simultaneously broadcast information updates, settings where nodes have access to common or correlated information, and settings where certain information is more critical than other information.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.

信息新鲜度在各种联网的监测和控制系统中至关重要，例如物联网（IoT）、智能车辆系统、无线通信系统中的反馈、环境监测、机器人网络以及网络物理系统中的实时监测和控制。此外，它在一些信息更新和数据分析应用中也很重要，包括金融交易、社交网络、众包、共识系统和在线学习。在所有这些应用程序中，陈旧的信息可能导致错误的决策，不稳定的控制回路，甚至在安全性和安全性方面的妥协。由于及时更新是至关重要的，在这些和许多其他当代应用程序，一个重要的问题是要了解的基本限制，信息的新鲜度在网络状态更新系统。 这个项目将进一步我们的信息新鲜度在一般多源/多显示器设置的基本限制的理解，并将制定策略，以实现这些限制。该项目还包括从事本科生和研究生的研究经验，在这个项目中的方法不同于以往的工作，在信息时代（AoI）考虑一般的多源/多监视器网络与明确的竞争和使用技术从图论研究信息新鲜度下的AoI指标在一般网络拓扑干扰约束。这种方法预计将导致新的见解，在现实的网络与一般的拓扑结构。研究计划被组织成三个相关的任务，以更好地理解AoI的理论基础，在显式的竞争框架：（i）同时传输和网络编码，（ii）广义信息映射和年龄加权，（iii）大型网络限制。所有这些任务的重点是表征信息新鲜度的基本限制，并制定策略，以接近或实现这些限制在一般类别的无线和有线网络。所提出的工作将把AoI的显式争用框架推广到丰富的、现实的一类问题设置，包括节点可以同时广播信息更新的设置，节点可以访问公共或相关信息的设置，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Fundamental bounds on the age of information in multi-hop global status update networks

• DOI: 10.1109/jcn.2019.000038
• 发表时间: 2019-06
• 期刊: Journal of Communications and Networks
• 影响因子: 3.6
• 作者: S. Farazi;A. G. Klein;D. Brown

Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity

• DOI: 10.1109/ieeeconf51394.2020.9443454
• 发表时间: 2020-11
• 期刊: 2020 54th Asilomar Conference on Signals, Systems, and Computers
• 作者: Leah Lackey;Arick Grootveld;Andrew G. Klein

Assessing Paper Texture Similarity in Matisse Lithographs Using a Triplet Neural Network

使用三元组神经网络评估马蒂斯石版画中的纸张纹理相似性

• DOI: 10.1109/urtc54388.2021.9740159
• 发表时间: 2021
• 期刊: 2021 IEEE MIT Undergraduate Research Technology Conference (URTC
• 作者: Aguilar, Karen M.;Powers, Shelby;Lackey, Leah;Grootveld, Arick;Klein, Andrew G.
• 通讯作者: Klein, Andrew G.

Age of Information with Unreliable Transmissions in Multi-Source Multi-Hop Status Update Systems

多源多跳状态更新系统中传输不可靠的信息时代

• DOI: 10.1109/ieeeconf44664.2019.9048736
• 发表时间: 2019
• 期刊: and Computers
• 作者: Farazi, Shahab;Klein, Andrew G.;Brown, D. Richard
• 通讯作者: Brown, D. Richard

Fundamental Bounds on the Age of Information in General Multi-Hop Interference Networks

• DOI: 10.1109/infcomw.2019.8845150
• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: S. Farazi;A. G. Klein;D. Brown