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NeTS: Small: Meta-Modelling for Complex Engineered Networks

NeTS：小型：复杂工程网络的元建模

基本信息

批准号：
1421058
负责人：
Violet Syrotiuk
金额：
$ 50万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-10-01 至 2020-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421058&HistoricalAwards=false
关键词：
NeTS Small Meta Modelling Complex

项目摘要

Complex engineered networks are pervasive in everyday life. A few examples include the internet, power grid, and transportation networks. The complexity arises not just from the size of the network, but also its structure, operation, evolution over time, and the fact that people are involved in its design and operation. Understanding complex engineered networks is challenging; their size and emergent properties means that traditional techniques of design, analysis, and modelling are poorly suited to adequately characterizing their behaviour and fundamental properties. This project develops a new tool for rigorous design, analysis, and modelling in the context of wireless networks. The goal is to improve understanding of the factors and interactions that impact performance. Experimentation in simulation and testbeds, using the NSF Global Environment for Network Innovations (GENI) network, is ongoing.Locating arrays (LAs) are formulated to focus on identification of factor interactions rather than measurement. Consequently, designs using LAs grow logarithmically in the number of experimental factors. This makes practical an order of magnitude more factors in experimentation. Hence, LAs have the potential to transform experimentation in huge factor spaces such as those found in complex engineered networks. An iterative approach, similar to that used in compressive sensing, is applied in model development; this also addresses a grand challenge in wireless networks for new data-driven mathematical models. Assessing the validity and robustness of the models developed is essential to understanding their quality and usefulness for optimization, management, and control of the network. Overall, this project contributes to understanding of complex engineered networks that shape modern society.

复杂的工程网络在日常生活中无处不在。一些例子包括互联网、电网和交通网络。复杂性不仅来自于网络的规模，还来自于它的结构、运作、随时间的演变，以及人们参与其设计和运作的事实。理解复杂的工程网络是具有挑战性的；它们的大小和突发性意味着传统的设计、分析和建模技术很难充分描述它们的行为和基本特性。该项目开发了一种新的工具，用于无线网络环境下的严格设计、分析和建模。目标是提高对影响性能的因素和相互作用的理解。利用美国国家科学基金会全球网络创新环境（GENI）网络进行的模拟和测试实验正在进行中。定位阵列（LAs）的制定重点是识别因素的相互作用，而不是测量。因此，使用LAs的设计在实验因子的数量上呈对数增长。这使得实验中的实际因素增加了一个数量级。因此，人工智能有潜力在巨大的因素空间中改变实验，比如在复杂的工程网络中发现的实验。在模型开发中采用了一种类似于压缩感知的迭代方法；这也解决了无线网络中新的数据驱动数学模型的巨大挑战。评估所开发模型的有效性和鲁棒性对于理解它们的质量和对网络优化、管理和控制的有用性至关重要。总的来说，这个项目有助于理解塑造现代社会的复杂工程网络。