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NeTS: Small: Generalized Submodular Optimization for Integrated Networked Sensing Systems

NeTS: Small: Generalized Submodular Optimization for Integrated Networked Sensing Systems
NeTS:小型:集成网络传感系统的广义子模块优化
批准号:
1017701
负责人:
Yixin Chen
金额:
$44.32万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-08-01 至 2014-07-31

项目摘要

项目成果

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中文摘要
翻译
无线传感器网络正在从专用平台发展为多应用的共享基础设施。共享传感器网络通过在不同应用程序之间动态分配资源来提供灵活性、自适应性和成本效益。共享传感器网络面临的关键需求,优化质量监测(库姆)的资源约束。由于共享传感器网络的非线性、离散性和动态性,其新出现的库姆优化问题在计算上具有挑战性。这个项目利用了一个关键的属性,称为子模块化,许多物理现象的库姆属性展示。该项目开发了有效的和理论上合理的分布式方法,通过一个新的子模块优化集成在一个基于市场的方法,为库姆优化。它进一步研究了能够快速适应网络和应用动态的在线算法,能够有效扩展大规模网络的基于分区的算法,以及能够适应能耗和异构网络优化的新优化算法。 该项目的预期成果包括管理和优化新一代集成网络传感系统的理论、算法和软件,这些系统具有很高的社会和环境影响。该项目还在华盛顿大学泰森研究中心部署了一个用于环境监测的集成传感器网络,用于环境和生态研究。这项研究促进了与环境和生物科学家的跨学科合作,以及高中学生的外联活动。
英文摘要
Wireless sensor networks are evolving from specialized platforms to shared infrastructure for multiple applications. Shared sensor networks offer flexibility, adaptivity, and cost-effectiveness through dynamic resource allocation among different applications. Shared sensor networks face the critical need for optimizing Quality of Monitoring (QoM) subject to resource constraints. The emerging QoM optimization problems in shared sensor networks are computationally challenging due to their nonlinear, discrete, and dynamic nature. This project exploits a key property known as submodularity that many QoM attributes of physical phenomena exhibit. This project develops efficient and theoretically sound distributed approaches for QoM optimization, through a novel integration of submodular optimization in a market-based approach. It further studies online algorithms which can quickly adapt to network and application dynamics, partition-based algorithms that scale effectively for large-scale networks, and new optimization algorithms that can accommodate the optimization of energy consumption and heterogenous networks. Expected results of this project include theory, algorithms, and software for managing and optimizing a new generation of integrated networked sensing systems with high societal and environmental impact. The project also deploys an integrated sensor network for environmental monitoring in Tyson Research Center of Washington University for environmental and ecological research. This study promotes interdisciplinary collaboration with environmental and biological scientists, as well as outreach activities for high-school students.
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III: Small: Collaborative Research: Towards Interpretable Machine Learning
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ICES: Small: Artificial Human Agents for Virtual Economies
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    2022
  • 负责人:
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Small RNA调控I-F型CRISPR-Cas适应性免疫性的应答及分子机制
Small RNAs调控解淀粉芽胞杆菌FZB42生防功能的机制研究
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  • 负责人:
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