高质量的信息感知实时交通流量分析和报告

Manisha Mukherjee, J. Edwards, H. Kwon, T. L. Porta
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引用次数: 0

摘要

本文提出了一个信息质量感知网络的框架。qi量化了一条信息对于给定查询或应用程序的有用程度。在这里,我们提出了一个通用的qi模型,以及一个贯穿本文其余部分的具体示例实例。在这个模型中,我们关注的是精度和准确度之间的权衡。作为一个激励的例子,我们看一下交通视频分析。我们提出了简单的算法来从视频中获得各种交通指标,如车辆数量和平均速度。我们在桌面工作站和功能较差的移动设备上实现这些算法。然后,我们将展示qos感知如何使终端设备能够就如何处理查询和形成响应做出智能决策,从而实现巨大的带宽节省。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality of information-aware real-time traffic flow analysis and reporting
In this paper we present a framework for Quality of Information (QoI)-aware networking. QoI quantifies how useful a piece of information is for a given query or application. Herein, we present a general QoI model, as well as a specific example instantiation that carries throughout the rest of the paper. In this model, we focus on the tradeoffs between precision and accuracy. As a motivating example, we look at traffic video analysis. We present simple algorithms for deriving various traffic metrics from video, such as vehicle count and average speed. We implement these algorithms both on a desktop workstation and less-capable mobile device. We then show how QoI-awareness enables end devices to make intelligent decisions about how to process queries and form responses, such that huge bandwidth savings are realized.
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