Collaborative Research: EARS: Crowd-based Spectrum Monitoring and Enforcement

合作研究：EARS：基于人群的频谱监控和执行

• 批准号: 1443945
• 负责人: Xia Zhou
• 金额: $ 23万
• 依托单位: Dartmouth College
• 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: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443945&HistoricalAwards=false
• 关键词: Collaborative; Research; EARS; Crowd; based

The scientists identify an important issue for a world of devices all communicating wirelessly: "How to identify malfunctioning devices or those not fairly sharing the spectrum with others?" The scientists explore scenarios for leveraging local, crowd-sourced mobile devices to detect and identify unauthorized transmitters. The goal of this technique is to reduce the cost of enforcement of network rules by adding the capability of some smart phones to sense the radio spectrum use and report back to a central database.The PIs propose to develop a Crowd-based Spectrum ENforcement System (CSENS), which takes a data-driven approach to spectrum enforcement. Specifically, the PIs propose to conduct 3 tasks: (i) real-time on-demand spectrum monitoring, which enables real-time responses to spectrum measurement tasks; (ii) utilizing physical layer features to embed cryptographic spectrum permits into transmissions, which enables reliably distinguish between authorized and unauthorized spectrum users; (iii) using a library of known signatures for network applications, unauthorized transmitters can be uniquely identified.

科学家们发现了一个重要的问题：“如何识别故障设备或那些没有公平地与他人共享频谱的设备？”科学家们探索了利用本地、众包移动设备来检测和识别未经授权的发射器的方案。这种技术的目标是通过增加一些智能手机感知无线电频谱使用并向中央数据库报告的能力来降低执行网络规则的成本。PIs建议开发一个基于人群的频谱执法系统（CSENS），该系统采用数据驱动的方法进行频谱执法。具体而言，pi建议开展3项任务：(i)实时按需频谱监测，实现对频谱测量任务的实时响应；（ii）利用物理层特征在传输中嵌入加密频谱许可证，从而能够可靠地区分授权和未经授权的频谱用户；（iii）使用网络应用程序的已知签名库，可以唯一地识别未经授权的发送者。