NETS: Small: Machine Learning Based Algorithms for Quasi-Static Ad Hoc Wireless Networks

NETS：小型：用于准静态自组织无线网络的基于机器学习的算法

• 批准号: 1218823
• 负责人: Rohit Negi
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218823&HistoricalAwards=false
• 关键词: NETS; Small; Machine; Learning; Based

Quasi-static ad hoc wireless networks are models for city-wide mesh networks, machine-to-machine networks deployed for control, and certain sensor networks, which are of growing importance in applications such as smart electricity grids, transportation grid control and infrastructure monitoring. The goal of this project is to design distributed online network protocols that learn the characteristics of such networks and self-optimize to achieve more predictable performance, such as delay guarantees. This is especially important for networks such as smart grid, where end-to-end delay must be predictable. A factor graph is used to model the probability distribution over the allowed network control actions. A novel information theoretic formulation is being investigated, which measures the information sharing required to coordinate network actions, between devices and across the network stack within each device. The solution to this problem will then be obtained by using statistical sampling techniques from machine learning, providing the online algorithm for network control. This algorithm will be distributed, because it is obtained by minimizing the information sharing required. Thus, the project will provide a formal mathematical basis for the probabilistic design of distributed protocols for quasi-static ad hoc networks, utilizing the information theoretic concepts of information, entropy, and side-information to quantify the value of distributed actions. Results from this project will be published and presented in major professional conferences and journals, and will be available to the wider public. The project will support the training of doctoral students in the important field of wireless networks. The theoretical framework obtained will be discussed in courses on statistical engineering methods and information theory. The MAC protocol concepts will be used to augment the PI?s under-graduate experimental set-up based on software radios, so that students of communication theory can obtain hands-on experience with system design.

准静态ad hoc无线网络是城市范围的网状网络、用于控制的机器对机器网络和某些传感器网络的模型，这些网络在诸如智能电网、交通电网控制和基础设施监控等应用中越来越重要。这个项目的目标是设计分布式在线网络协议，学习这些网络的特点和自我优化，以实现更可预测的性能，如延迟保证。这对于智能电网等网络尤其重要，因为端到端延迟必须是可预测的。因子图用于对允许的网络控制动作的概率分布进行建模。正在研究一种新的信息理论公式，它测量协调网络动作所需的信息共享，设备之间和每个设备内的网络堆栈。然后，通过使用机器学习中的统计采样技术来获得该问题的解决方案，为网络控制提供在线算法。这种算法将是分布式的，因为它是通过最小化所需的信息共享来获得的。因此，该项目将提供一个正式的数学基础的准静态ad hoc网络的分布式协议的概率设计，利用信息，熵和边信息的信息理论的概念来量化分布式行动的价值。该项目的成果将在主要的专业会议和期刊上发表和介绍，并将提供给更广泛的公众。该项目将支持在无线网络这一重要领域培养博士生。所获得的理论框架将在统计工程方法和信息论课程中讨论。MAC协议的概念将被用来增加PI？的本科生实验设置基于软件无线电，使通信理论的学生可以获得与系统设计的实践经验。