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

研究开发了一个统一的数学框架，估计的容量和设计有效的跨层协议在无线ad hoc和mesh网络。与早期的努力，以获得分析的随机实例的能力上的界限，这里的重点是网络容量的算法理论。数学规划框架指导新的跨层协议的发展，理论上的努力得到了补充，在现有的仿真工具，如ns-2，以及真实的屋顶无线网络协议的仔细实施和评估。该研究包括三个基本组成部分：（1）基于数学规划的网络容量公式和在时延、能量、干扰等多约束条件下计算网络容量的有效算法;（2）设计和开发由数学编程框架激励的协议和相关度量，以及（3）在仿真环境中以及在真实的屋顶无线网络上的协议的原型实现和严格的统计分析。网络容量的算法理论在建立无线网络信息传输的基本可实现极限方面是有用的。新的统计技术导致能够表征协议之间的算法交互。性能评估的严格统计方法，结合基于仿真的实验，产生量化复杂系统仿真中的不确定性的一般方法。该研究为学生提供了构建真实的无线网络的实践经验。新的课堂课程加强了许多学科的研究生和本科生课程。

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.