CRII: CIF: Models, Theories and Algorithms for Timeliness Optimization in Information-update Systems

CRII:CIF:信息更新系统时效性优化的模型、理论和算法

基本信息

  • 批准号:
    1657162
  • 负责人:
  • 金额:
    $ 17.26万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-02-15 至 2021-01-31
  • 项目状态:
    已结题

项目摘要

The last two decades have witnessed significant advances in the development of theoretical foundations and control mechanisms for network resource allocation. These newly developed theories and mechanisms have substantially improved network performance in terms of throughput and delay. However, optimizing throughput and delay is insufficient for networked systems that require real-time information update. The state-of-the-art theoretical foundations need to be largely expanded to integrate timeliness of information into the design of network control mechanisms. The research on timeliness optimization is still at its nascent stage. New theoretical results and practical solutions coming out of this project are expected to have a significant impact not only on information theory and networking community, but also on databases and machine learning community. This project will focus on providing research experiences to undergraduate and K-12 students, recruiting and advising underrepresented students, and engaging in curriculum development activities. The goal of this research is to develop new models, theories, and algorithms for optimizing timeliness performance in information-update systems. A recently proposed metric called age-of-information or simply "age", will be employed as a key metric to study timeliness performance. First, this research investigates the impact of channel coding on timeliness of information transmitted over a lossy channel. Second, this research studies the problem of age minimization under a bounded staleness constraint in a new setting where information can be partitioned into multiple disjoint units with partial updates. Finally, this research introduces a new Pull model where the destination sends queries to the sources to pull information of interest and proposes using replication schemes to optimize timeliness performance.
近二十年来,网络资源配置的理论基础和控制机制的发展取得了重大进展。这些新发展的理论和机制在吞吐量和延迟方面大大提高了网络性能。然而,对于需要实时信息更新的网络系统,优化吞吐量和延迟是不够的。将信息的时效性融入网络控制机制的设计,需要大量扩展最新的理论基础。时效性优化的研究还处于起步阶段。预计该项目产生的新的理论成果和实际解决方案不仅对信息论和网络社区,而且对数据库和机器学习社区产生重大影响。该项目将侧重于为本科生和K-12学生提供研究经验,招募和建议代表性不足的学生,并参与课程开发活动。本研究的目标是开发新的模型、理论和算法来优化信息更新系统的时效性。最近提出的一项指标被称为信息年龄或简称为“年龄”,将被用作研究及时性绩效的关键指标。首先,本研究探讨了信道编码对在有损信道上传输的信息时效性的影响。其次,本文研究了有界过时约束下的新设置下的年龄最小化问题,其中信息可以被划分为多个不相交的部分更新单元。最后,本研究引入了一种新的Pull模型,其中目标向源发送查询以提取感兴趣的信息,并提出使用复制方案来优化时效性性能。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Anti-Aging Scheduling in Single-Server Queues: A Systematic and Comparative Study
单服务器队列抗老化调度:系统比较研究
  • DOI:
    10.23919/jcn.2021.000005
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Zhongdong Liu, Liang Huang
  • 通讯作者:
    Zhongdong Liu, Liang Huang
Combinatorial Sleeping Bandits With Fairness Constraints
The Power of Waiting for More Than One Response in Minimizing the Age-of-Information
等待多个响应在最小化信息时代的力量
Implementing Grover’s Algorithm on the IBM Quantum Computers
在 IBM 量子计算机上实施 Grover 算法
Efficient and low-overhead uplink scheduling for large-scale wireless Internet-of-Things
大规模无线物联网的高效、低开销上行链路调度
  • DOI:
    10.23919/wiopt.2018.8362813
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Li, Bin;Ji, Bo;Liu, Jia
  • 通讯作者:
    Liu, Jia
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Bo Ji其他文献

A moving weak and small target detection algorithm for multispectral image sequences
多光谱图像序列的运动弱小目标检测算法
Algal Toxins in Water
水中的藻类毒素
  • DOI:
    10.1002/047147844x.wq23
  • 发表时间:
    2005
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bo Ji;M. Wong;R. Wong;Yu’e Jiang
  • 通讯作者:
    Yu’e Jiang
Deep Learning Models for Biomedical Image Analysis
用于生物医学图像分析的深度学习模型
Securing Bystander Privacy in Mixed Reality While Protecting the User Experience
保护混合现实中的旁观者隐私,同时保护用户体验
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Matthew Corbett;Brendan David;Jiacheng Shang;Y. C. Hu;Bo Ji
  • 通讯作者:
    Bo Ji
Diagnosis Expert System for Oesophagus Cancer in Early Stage
食管癌早期诊断专家系统

Bo Ji的其他文献

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{{ truncateString('Bo Ji', 18)}}的其他基金

Collaborative Research: NeTS: Medium: Black-box Optimization of White-box Networks: Online Learning for Autonomous Resource Management in NextG Wireless Networks
合作研究:NeTS:中:白盒网络的黑盒优化:下一代无线网络中自主资源管理的在线学习
  • 批准号:
    2312833
  • 财政年份:
    2023
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Medium: Information Freshness in Scalable and Energy Constrained Machine to Machine Wireless Networks
合作研究:CNS 核心:中:可扩展且能量受限的机器对机器无线网络中的信息新鲜度
  • 批准号:
    2106427
  • 财政年份:
    2021
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Continuing Grant
NSF Student Travel Grant for 2020 ACM International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS 2020)
NSF 学生旅费资助 2020 年 ACM 国际计算机系统测量和建模会议 (ACM SIGMETRICS 2020)
  • 批准号:
    2013729
  • 财政年份:
    2020
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Standard Grant
NSF Student Travel Grant for 2020 ACM International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS 2020)
NSF 学生旅费资助 2020 年 ACM 国际计算机系统测量和建模会议 (ACM SIGMETRICS 2020)
  • 批准号:
    2110139
  • 财政年份:
    2020
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Standard Grant
CAREER: Theory and Algorithms for Efficient Control of Wireless Networks with Jointly Optimized Performance: High Throughput, Low Delay, and Low Complexity
职业:具有联合优化性能的无线网络高效控制的理论和算法:高吞吐量、低延迟和低复杂性
  • 批准号:
    2112694
  • 财政年份:
    2020
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Continuing Grant
CAREER: Theory and Algorithms for Efficient Control of Wireless Networks with Jointly Optimized Performance: High Throughput, Low Delay, and Low Complexity
职业:具有联合优化性能的无线网络高效控制的理论和算法:高吞吐量、低延迟和低复杂性
  • 批准号:
    1651947
  • 财政年份:
    2017
  • 资助金额:
    $ 17.26万
  • 项目类别:
    Continuing Grant

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CIF: Small: Latent Neural Factor Models for Radio Cartography From Bits
CIF:小:来自 Bits 的无线电制图的潜在神经因子模型
  • 批准号:
    2210004
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    2022
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Collaborative Research: CIF: Small: Deep Sparse Models: Analysis and Algorithms
合作研究:CIF:小型:深度稀疏模型:分析和算法
  • 批准号:
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  • 财政年份:
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  • 批准号:
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    $ 17.26万
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CIF:中:通过关系反馈学习、完善和理解模型
  • 批准号:
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    $ 17.26万
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Collaborative Research: CIF: Small: Convexification-based Decomposition Methods for Large-Scale Inference in Graphical Models
合作研究:CIF:小型:图模型中大规模推理的基于凸化的分解方法
  • 批准号:
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Collaborative Research: CIF: Medium: Security through the Lens of Information Asymmetry: A Pragmatic Evolution of Goals and Models in Physical Layer Security
合作研究:CIF:媒介:信息不对称视角下的安全:物理层安全目标和模型的务实演变
  • 批准号:
    1955401
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    $ 17.26万
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Collaborative Research: CIF: Small: Deep Sparse Models: Analysis and Algorithms
合作研究:CIF:小型:深度稀疏模型:分析和算法
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  • 批准号:
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