EARS: Machine Learning and Social Protocols for Enhancing Spectrum Access for Wireless Communications

EARS：用于增强无线通信频谱访问的机器学习和社交协议

• 批准号: 1546689
• 负责人: Janne Lindqvist
• 金额: $ 30万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546689&HistoricalAwards=false
• 关键词: EARS; Machine; Learning; Social; Protocols

Radio spectrum is a scarce resource that needs to be managed. Towards this end, this project designs, implements and studies novel protocols for enhancing access to radio spectrum by taking advantage of the fundamentals of human behavior. It produces technologies, theories, and guidelines for protocols that are expected to significantly improve the efficiency of spectrum access. This may lead to substantial societal impact by allowing more work to be done with the same resources. Success will also benefit the environment by limiting the infrastructure needed for the required data traffic; both energy and infrastructure investment can be minimized. Further, the project creates a model of human behavior in a specific technical context that can serve as a basis for similar projects in other technology areas involving resource optimization. The project creates anonymized datasets, and tools to analyze human and wireless behavior, all of which will be distributed to the general public. This project has important educational and training benefits at high school, undergraduate and graduate levels, including undergraduate research prototyping projects, and new graduate-level courses and seminars. This interdisciplinary project applies and develops expertise from areas of social computing, machine learning, wireless technology, security engineering, physical analogs, mobile systems, and user-centered design. The project implements and deploys a system that enables efficient bandwidth sharing with machine learning and social protocols that goes beyond what is possible with technology alone. Social protocols are cooperative yet discretionary methods that allow to users distribute access more fairly using inherently natural decision-making processes as opposed to externally imposed ones. The project consists of three major activities: (1) study the main approaches to social protocols, including persuasive computing, clinical behavior change theories, and micro-tasks; (2) develop a system that observes users trying to access the network and facilitates control rules that allow maximum value to users in the most transparent way; and (3) deploy these social protocols and the system in live networks to evaluate the approaches in real-life settings.

无线电频谱是一种需要管理的稀缺资源。为此，该项目设计，实施和研究新的协议，通过利用人类行为的基本原理来加强对无线电频谱的访问。它产生的技术，理论和协议的指导方针，预计将显着提高频谱接入的效率。这可能会产生重大的社会影响，因为可以用同样的资源完成更多的工作。成功还将通过限制所需数据流量所需的基础设施来使环境受益;能源和基础设施投资都可以最小化。此外，该项目在特定的技术背景下创建了一个人类行为模型，可以作为涉及资源优化的其他技术领域的类似项目的基础。 该项目创建匿名数据集和工具来分析人类和无线行为，所有这些都将分发给公众。该项目在高中、本科和研究生阶段具有重要的教育和培训效益，包括本科生研究原型项目以及新的研究生课程和研讨会。这个跨学科的项目应用和开发来自社会计算，机器学习，无线技术，安全工程，物理模拟，移动的系统和以用户为中心的设计领域的专业知识。该项目实现并部署了一个系统，该系统能够通过机器学习和社交协议实现高效的带宽共享，这超出了仅凭技术所能实现的范围。社会协议是合作但自由裁量的方法，允许用户使用固有的自然决策过程，而不是外部强加的，更公平地分配访问。该项目包括三个主要活动：（1）研究社会协议的主要方法，包括说服计算，临床行为改变理论和微任务;（2）开发一个系统，观察试图访问网络的用户，并促进控制规则，以最透明的方式为用户提供最大价值;以及（3）将这些社交协议和系统部署在实时网络中，以评估现实生活环境中的方法。