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

职业：设计大规模自组织网络系统：模型、分析和协议

• 批准号: 0448055
• 负责人: Dmitri Perkins
• 金额: --
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0448055&HistoricalAwards=false
• 关键词: CAREER; Designing; Large; scale; Ad

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.

设计能够支持异质流量类型的大规模临时网络系统涉及以非常复杂的方式进行交互的大量因素（例如协议，参数，网络大小，流量特征），从而使系统优化成为非平凡的问题。该项目的主要目标是对基本绩效，扩展特性和权衡的更深入了解，并包括两个主要的研究推力。第一个专注于构建分析和经验模型，以准确表征性能指标（例如端到端延迟）和重要因素之间的功能关系。正在开发分析端到端延迟和数据包丢弃模型，以说明流量到达过程之间的相互作用，无线通道，错误控制机制以及移动性诱导的路径故障。经验模型基于两种建模方法：（1）响应表面方法论和回归分析以及（2）Levenberg-Marquardt多层感知ptron神经网络的类别。在第二项研究中，我们正在使用上述模型来探索几种缩放和性能属性，包括哪种系统配置可导致在指定的利益区域中最佳，健壮，可扩展或令人满意的性能响应。使用预测性的经验模型，正在为运输和QoS敏感的路由策略设计自适应跨层反馈机制。拟议项目的成功将产生可靠的模型，原理和协议，以构建大规模的临时网络。教育组成部分将通过整合统计实验设计和对无线网络课程的研究建模来增强教育，并通过与非PH.D建立研究合作伙伴关系来增强国家研究计划。授予大学。