NeTS: Small: Algorithmic Foundations for Joint Information Processing and Optimization in a Hybrid Mobile Sensor Network

NeTS：小型：混合移动传感器网络中联合信息处理和优化的算法基础

• 批准号: 1116881
• 负责人: Jie Gao
• 金额: $ 25.76万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116881&HistoricalAwards=false
• 关键词: NeTS; Small; Algorithmic; Foundations; Joint

The objective of this project is to develop efficient distributed algorithms for joint information processing, optimization and coordination of static, pervasive sensor nodes and mobile, autonomous agents that altogether monitor and act on the environment. The application scenarios include tracking, searching and planning mobile agents with the underlying sensor network as a supporting communication infrastructure, and online resource management and allocation guided by real-time sensor detections.This project focuses on three research problems: distributed min-cost bipartite matching, kinetic minimum Steiner tree, and facility location for mobile nodes, by using two non-trivial technical approaches, namely, embedding of the network metric into tree metrics, and distributed primal dual framework. There are two central intellectual questions investigated in this project.1) How to make use of the sensor data to best serve user requests? This involves developing communication efficient schemes for sensors detecting interesting local events and the mobile users seeking such information to find each other.2) How to best make use of the continuity and coherence in mobility, either in the presence of mobility enabled sensor data (e.g., detections of continuously moving targets), as well as the locations of mobile users? The project provides solutions that adapt to the system configuration with low update cost, avoiding drastic sudden changes or any level of reconstruction.This project helps to extend the current Internet to the physical world, encouraging seamless integrations of sensing and control of the physical environment. The PI integrates the research agenda with new and existing curriculum development for both undergraduate and graduate education, and continues her efforts in improving female presence in computer science and exposure of research for high school students.

该项目的目的是开发有效的分布式算法，用于静态信息处理，优化和协调的静态，普遍传感器节点和移动，自动源，以完全监控和对环境作用。应用程序场景包括以基础传感器网络为支持的通信基础架构以及在线资源管理以及以实时传感器检测指导的在线资源管理和分配的操作方案。该项目重点介绍三个研究问题：分布式的最小分配的双分部分匹配的分布式型号的最小台词和机构的位置，用于移动nonodive nontrivial notrivial notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv notiv in-trivir notiv notiv notiv nor-triviv and-triviv进入树指标，并分发了原始的双重框架。该项目中研究了两个中心的智力问题。1）如何利用传感器数据来最好地服务用户请求？这涉及开发用于检测有趣当地事件的传感器的沟通有效方案以及寻求此类信息以互相找到的移动用户。2）如何最好地利用移动性的连续性和相干性，要么在有启用移动性传感器数据的情况下（例如，对持续移动目标的检测）以及移动用户的位置？该项目提供了适应系统配置的解决方案，以低更新成本，避免突然的突然变化或任何级别的重建。该项目有助于将当前的互联网扩展到物理世界，鼓励对物理环境的感应和控制无缝集成。 PI将研究议程与本科和研究生教育的新的和现有的课程开发相结合，并继续为改善女性在计算机科学领域的影响力以及为高中生的研究暴露而努力。