Collaborative Research: NeTS-NBD: An Integrated Approach to Computing Capacity and Developing Efficient Cross-Layer Protocols for Wireless Networks

合作研究：NeTS-NBD：计算能力和开发高效无线网络跨层协议的综合方法

• 批准号: 0626636
• 负责人: Aravind Srinivasan
• 金额: $ 36.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0626636&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; NBD; Integrated

The research develops a unified mathematical framework for estimating the capacity and designing efficient cross-layer protocols in wireless ad hoc and mesh networks. In contrast to earlier efforts to derive analytical bounds on the capacity of random instances, the focus here is an algorithmic theory of network capacity. The mathematical programming framework guides the development of novel cross-layer protocols, and the theoretical effort is complemented by careful implementation and evaluation of protocols within existing simulation tools, such as ns-2, as well as on real rooftop wireless networks. The research consists of three basic components: (1) mathematical programming-based formulations of network capacity and efficient algorithms for computing capacity under multiple constraints, such as latency, energy, interference, etc; (2) the design and development of protocols and associated metrics that are motivated by the mathematical programming framework, and (3) prototype implementation and rigorous statistical analysis of protocols in a simulation environment, as well as on real roof-top wireless networks. The algorithmic theory of network capacity is useful in establishing the fundamental achievable limits of wireless networks for information transmission. New statistical techniques result in the ability to characterize the algorithmic interactions between protocols. The rigorous statistical methods for performance evaluation, combined with simulation based experiments, yield general methods for quantifying uncertainty in simulations of complex systems. The research provides students hands-on experience in building real wireless networks. New classroom courses enhance graduate as well as undergraduate curricula in numerous disciplines.

该研究开发了一个统一的数学框架，用于估计无线自组网和网状网络的容量并设计高效的跨层协议。与早期推导出随机实例容量的分析界限的努力相比，这里的重点是网络容量的算法理论。数学编程框架指导新型跨层协议的开发，并且通过在现有仿真工具（例如 ns-2）以及真实屋顶无线网络中仔细实施和评估协议来补充理论工作。该研究由三个基本组成部分组成：（1）基于数学规划的网络容量公式以及在延迟、能量、干扰等多重约束下计算容量的高效算法； (2) 由数学编程框架推动的协议和相关指标的设计和开发，以及 (3) 在模拟环境以及真实的屋顶无线网络中对协议进行原型实现和严格的统计分析。网络容量的算法理论有助于确定无线网络信息传输的基本可实现限制。新的统计技术能够描述协议之间的算法交互。用于性能评估的严格统计方法与基于模拟的实验相结合，产生了量化复杂系统模拟中的不确定性的通用方法。该研究为学生提供了构建真实无线网络的实践经验。新的课堂课程增强了众多学科的研究生和本科生课程。