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.

这个项目的目标是开发有效的分布式算法，用于联合信息处理，优化和协调的静态，无处不在的传感器节点和移动的，自主代理，共同监测和作用于环境。应用场景包括以底层传感器网络作为支撑通信基础设施，对移动的智能体进行跟踪、搜索和规划，以及以实时传感器检测为指导的在线资源管理和分配。分布式最小成本二分匹配、动态最小Steiner树和移动的节点的设施定位，通过使用两种非平凡的技术方法，即，将网络度量嵌入到树度量中，以及分布式原始对偶框架。在这个项目中有两个中心的智力问题研究。1）如何利用传感器数据，以最好地服务于用户的请求？这涉及开发用于传感器检测感兴趣的本地事件和移动的用户寻找这样的信息以找到彼此的通信有效方案。2）如何最好地利用移动性中的连续性和相干性，或者在存在移动性使能的传感器数据（例如，连续移动目标的检测），以及移动的用户的位置？该项目提供了适应系统配置的解决方案，更新成本低，避免了剧烈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，鼓励物理环境的传感和控制的无缝集成。PI将研究议程与本科和研究生教育的新课程和现有课程开发相结合，并继续努力提高高中学生在计算机科学和研究方面的女性存在。