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.

该项目的目标是开发高效的分布式算法，用于静态、普遍的传感器节点和移动、自主代理的联合信息处理、优化和协调，共同监视和作用于环境。应用场景包括以底层传感器网络作为支撑通信基础设施的跟踪、搜索和规划移动代理，以及以实时传感器检测为指导的在线资源管理和分配。该项目通过使用两种重要的技术方法（即网络度量的嵌入），重点研究三个研究问题：分布式最小成本二分匹配、动态最小斯坦纳树和移动节点的设施定位 分为树度量和分布式原始对偶框架。该项目研究了两个核心知识问题。1）如何利用传感器数据最好地满足用户请求？这涉及到为传感器开发有效的通信方案来检测有趣的本地事件以及寻求此类信息以找到彼此的移动用户。2）在存在支持移动性的传感器数据（例如，检测到连续移动的目标）以及移动用户的位置的情况下，如何最好地利用移动性的连续性和一致性？该项目提供了以较低的更新成本适应系统配置的解决方案，避免了突然的剧烈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 将研究议程与本科生和研究生教育的新课程和现有课程开发相结合，并继续努力提高女性在计算机科学领域的存在以及高中生研究的曝光度。