NeTS: Medium: Collaborative Research: Shaping, Learning and Optimizing Dynamic Networks

NeTS：媒介：协作研究：塑造、学习和优化动态网络

• 批准号: 0964391
• 负责人: Sujay Sanghavi
• 金额: $ 48.75万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964391&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Shaping

This project develops, from the ground up, a new theoretical framework for analyzing and designing algorithms for dynamic ad-hoc wireless networks. This proposal embraces network dynamics as an opportunity to be exploited, not an adversity to be overcome. The approach is based on four inter-related thrusts:1. Incremental Topology Learning: Tracking changes in the network much more efficiently than re-learning entire topology, using sparse "error graph" representations.2. Topology and Traffic Shaping: Controlling the "effective" wireless network topology so that (i) at any instant of time it appears to be highly disconnected to scheduling algorithms, but retains global connectivity over time; and (ii) modifying traffic statistics to ensure statistical spatial correlation decay.3. Warm-starting Distributed Algorithms: Message-passing algorithms that can warm-start the optimization based on local knowledge of past solutions.4. Proteus - A Mobile Robot Testbed: This project validates its approach via implementation on a mobile robot testbed called Proteus, which is used to optimize algorithms in a practical setting.Broader Impact: Industry is involved in this research from the start, via the WNCG Affiliates program at UT. The research will be disseminated via publications in top-tier venues, industry interactions, and specially organized workshops. Both graduate students and undergraduate students, via a REU program at UT (with emphasis on recruiting women and minorities), get exposure to both real-world wireless networks (via the testbed), and cutting edge theory.

从头开始，该项目开发了一个新的理论框架，用于分析和设计动态临时无线网络的算法。该建议将网络动态作为被利用的机会，而不是要克服的逆境。该方法基于四个相互关联的推力：1。增量拓扑学习：使用稀疏的“错误图”表示2。拓扑和流量塑造：控制“有效”的无线网络拓扑，以便（i）在任何时间的瞬间，它似乎与安排算法相关，但会随着时间的推移保留全球连接性； （ii）修改流量统计以确保统计空间相关性衰减3。温暖启动的分布式算法：可以根据过去解决方案的本地知识进行优化启动优化的消息通讯算法。4。 PROTEUS-移动机器人测试床：该项目通过在称为Proteus的移动机器人测试床上实施验证其方法，该测试用于在实用环境中优化算法。BOADERIMPACT：行业从一开始就通过UT的WNCG附属机构参与了这项研究。 该研究将通过顶级场地，行业互动和特殊组织的讲习班的出版物进行传播。研究生和本科生通过UT的REU计划（重点是招募妇女和少数民族），接触两种现实世界中的无线网络（通过测试台）和最先进的理论。