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面向交通信息采集的无线传感器网络节点
  • 期刊名称:张豫鹤, 黄希, 崔莉, 面向交通信息采集的无线传感器网络节点, 计算机研究与发展, 45(1):1
  • 时间:0
  • 分类:TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] U491.116[交通运输工程—交通运输规划与管理;交通运输工程—道路与铁道工程]
  • 作者机构:[1]中国科学院计算技术研究所,北京100080, [2]中国科学院研究生院,北京100049
  • 相关基金:国家自然科学基金面上项目(60572060);国家“八六三”高技术研究发展计划基金项目(2006AA012215);国家“九七三”重点基础研究发展规划基金项目(2006CB303000)致谢作者在工作过程中得到赵泽、苗勇、李栋、牛晓光、陈海明、孙海成、梁伟伟、陆世龙的大力协助,还得到北京工业大学交通工程实验室的支持,在此一并感谢!
  • 相关项目:无线传感器网络的多层能量优化机制和算法研究
中文摘要:

智能交通系统的关键技术环节之一是能够准确地获取实时交通参数,包括交通流量、车速、车道占有率等,无线传感器网络在智能交通方面有潜在的广泛应用前景.设计实现了面向交通信息采集的无线传感器网络节点,提出了一系列相关交通信息采集专用算法,包括基于数字滤波和匹配滤波的交通流量监测算法、车速测量算法和车辆识别算法,在道路上进行了实测验证并对节点功耗进行了分析.实测结果表明,交通信息采集节点能以较高精度得到交通流量、车速、车道占有率等信息,并能较准确地对机动车和自行车进行识别。

英文摘要:

With the rapid development of modern cities and fast increasing amount of vehicles, ITS has become crucial nowadays. Many elements of ITS are based on the real-time road condition represented mainly by the traffic volume, velocity and lane occupancy. Traditional traffic information detection systems can't meet the requirements of deployment convenience, detection accuracy and overall cost. WSN technology has potential wide prospect of applications. In this paper, the network architecture of a WSN- based wireless traffic information detection system, the hardware design of the detection nodes, and a series of detection algorithms are presented. Two kinds of algorithms are developed for traffic volume detection, which are a digital filter based algorithm (DiFiA) and a matched filter based algorithm (MaFiA) respectively. Two nodes for velocity detection are adopted and a velocity detection algorithm, namely VeDA is proposed. An efficient vehicle/non-vehicle classification algorithm is also designed to improve the accuracy of traffic volume, which is called ReSDiRA. And a lightweight synchronization method between two nodes is described for both velocity detection and vehicle/non-vehicle classification. On-road experiments are carried out to test and verify the accuracy and efficiency of the proposed methods, and the power consumption of the node is analytically evaluated. Experimental results indicate potential promises that the developed sensor node may be used in real-time traffic information detection as a new technique.

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