CAREER: Designing Large-scale Ad Hoc Networking Systems: Models, Analysis, and Protocols

设计能够支持异构业务类型的大规模自组织联网系统涉及大量因素（例如，协议、参数、网络规模、业务特性）以非常复杂的方式交互，使得系统优化成为一个重要的问题。该项目的主要目标是对基本性能，缩放特性和权衡进行更深入的了解，包括两个主要研究方向。第一个重点是构建分析和经验模型，准确地表征性能指标之间的函数关系（例如，端到端延迟）和重要因素。分析端到端的延迟和数据包丢弃模型正在开发，占流量到达过程中，无线信道，差错控制机制，和移动性引起的路径故障之间的相互作用。经验模型基于两种建模方法：（1）响应曲面法和回归分析，（2）Levenberg-Marquardt多层感知器神经网络类。在第二个研究重点中，我们使用上述模型来探索几个扩展和性能属性，包括哪些系统配置在指定的感兴趣区域上导致最佳，鲁棒，可扩展或可满足的性能响应。使用预测的经验模型，自适应跨层反馈为基础的机制正在设计的传输和QoS敏感的路由策略。拟议项目的成功将产生坚实的模型，原则和协议，建立大规模的特设网络。教育部分将通过将统计实验设计和建模研究整合到无线网络课程中来加强教育，并通过与非博士建立研究伙伴关系来加强国家研究计划。授予大学。

Designing large-scale ad hoc networking systems capable of supporting heterogeneous traffic types involves a large number of factors (e.g., protocols, parameters, network size, traffic characteristics) that interact in a very complex manner, making system optimization a non-trivial problem. The principle goal of this project is to develop a deeper understanding of the fundamental performance, scaling properties, and tradeoffs and includes two main research thrusts. The first is focused on constructing analytical and empirical models that accurately characterize the functional relationship between performance metrics (e.g., end-to-end delay) and significant factors. Analytical end-to-end delay and packet discard models are being developed that account for interactions among the traffic arrival process, wireless channels, error control mechanisms, and mobility-induced path failures. The empirical models are based on two modeling approaches: (1) response surface methodology and regression analysis and (2) the class of Levenberg-Marquardt multilayer perceptron neural networks. In the second research thrust, we are using the above models to explore several scaling and performance properties, including which system configuration(s) lead to optimal, robust, scalable, or satisfiable performance response(s) over specified regions of interests. Using the predictive empirical models, adaptive cross-layer feedback-based mechanisms are being designed for transport and QoS-sensitive routing strategies. The success of the proposed project will produce solid models, principles, and protocols on which to build large-scale ad hoc networks. An educational component will enhance education by integrating statistical experimental design and modeling research into wireless networking courses and will enhance national research initiatives by establishing research partnerships with non-Ph.D. granting universities.