Spectral Tweets: A Community Paradigm for Spatio-temporal Cognitive Sensing and Access

频谱推文：时空认知感知和访问的社区范式

基本信息

批准号：
1247885
负责人：
Nikolaos Sidiropoulos
金额：
$ 49.94万
依托单位：
University of Minnesota-Twin Cities
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=1247885&HistoricalAwards=false
关键词：
Spectral Tweets Community Paradigm Spatio

项目摘要

Advances in wireless communication, networking, and computation using miniaturized devices have come to a confluence that has greatly improved productivity even in traditionally "low-tech" sectors of the economy. The flip side is that demand for radio spectrum is on track to outpace advances in spectral efficiency. Innovative and pragmatic dynamic spectrum sensing and measurement-based spectrum access modalities are needed to bridge the gap, and support demand and the national economy in the not-so-distant future. It is now widely accepted that effective spectrum sensing must be a collaborative endeavor, involving many sensors taking relatively sparse measurements across space, time, and frequency. The vision and starting point of this project is that today's smart phones and tablets are ideal platforms for crowdsourcing spectrum sensing, and this is a viable way to create a spectrum sensing web that spans across much of our living and working space. But can all these devices be coordinated to produce local spectrum maps, reveal transmission opportunities, and enable intelligent dynamic access? An appealing idea is to employ Twitter as a service model to multicast short "spectral tweets." Devices will hook up to local communities based on GPS information, and set up "bots" to follow tweets from other devices in the neighborhood.

使用小型设备的无线通信、网络和计算的进步已经达到了融合，即使在传统的“低技术”经济领域，也极大地提高了生产力。另一方面是，对无线电频谱的需求正在超过频谱效率的增长。需要创新和务实的动态频谱感知和基于测量的频谱接入方式来弥合差距，并在不远的将来支持需求和国民经济。现在人们普遍认为，有效的频谱感知必须是一项协作努力，涉及许多传感器在空间、时间和频率上进行相对稀疏的测量。该项目的愿景和出发点是，当今的智能手机和平板电脑是众包频谱感测的理想平台，这是创建跨越我们大部分生活和工作空间的频谱感测网络的可行方法。但是，所有这些设备能否协调起来生成本地频谱图、揭示传输机会并实现智能动态接入？一个吸引人的想法是使用 Twitter 作为服务模型来组播短的“频谱推文”。设备将根据 GPS 信息连接到当地社区，并设置“机器人”来跟踪附近其他设备的推文。