NeTS: Small: Predictable Optimization of Opportunistic Communication

NeTS：小型：机会通信的可预测优化

• 批准号: 0916106
• 负责人: Lili Qiu
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916106&HistoricalAwards=false
• 关键词: NeTS; Small; Predictable; Optimization; Opportunistic

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Opportunistic communication leverages communication opportunities arising by chance to provide significant performance benefit or even enable communication where it would be impossible otherwise. This project develops algorithms, techniques, and protocols that optimize opportunistic communication to achieve good, predictable performance in wireless mesh networks and vehicular networks.A key challenge involved is how to systematically optimize opportunistic communication to achieve good predictable wireless performance. The research addresses the challenge by making three major contributions. First, the PIs develop novel robust optimization techniques that systematically optimize opportunistic communication even in the presence of high uncertainty. Second, they design efficient models, measurement and inference techniques, and prediction algorithms to obtain the required inputs to the optimization algorithms. Third, they exploit inter-flow coding opportunities arising from multi-flow diversity to further enhance the efficiency of opportunistic communication. To demonstrate the effectiveness of the approaches, the PIs design, implement, and experiment in a wireless mesh network and a vehicular network testbed deployed at UT Austin.The project produces publications in leading network conferences and journals, and software that is publicly available online. The resulting techniques and software significantly advance wireless mesh network and vehicular network technology, and benefit other wireless network environments. The research results are also integrated into undergraduate and graduate curricula as well as outreach activities.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。主动式通信利用偶然出现的通信机会来提供显著的性能优势，甚至在不可能的情况下实现通信。本项目旨在研究无线网状网络和车载网络中机会通信的优化算法、技术和协议，以获得良好的、可预测的性能，其中的一个关键挑战是如何系统地优化机会通信以获得良好的、可预测的无线性能。这项研究通过做出三大贡献来应对这一挑战。首先，PI开发了新的鲁棒优化技术，即使在存在高度不确定性的情况下也能系统地优化机会通信。 其次，他们设计有效的模型，测量和推理技术，以及预测算法，以获得优化算法所需的输入。 第三，它们利用由多流分集产生的流间编码机会来进一步提高机会通信的效率。为了证明这些方法的有效性，PI设计，实施，并在无线网状网络和部署在UT Austin的车载网络测试平台中进行实验。该项目在领先的网络会议和期刊上发表论文，并在线公开提供软件。由此产生的技术和软件大大推进了无线网状网络和车载网络技术，并有益于其他无线网络环境。研究成果也被纳入本科和研究生课程以及推广活动。