摘要
由于GPS无法在楼宇内使用,而目前的楼宇内定位技术一般都需要预先部署额外的设施,因此楼宇内无基础设施定位成为了一个热点研究问题.提出了一种利用Wi-Fi接入点的MAC地址和RSSI(received signal strength indication)值,通过机器分类的方式实现楼宇内房间级定位的算法R-kNN(relativity k-nearest neighbor).R-kNN是一种属性加权k近邻算法,它通过将AP之间的相关性反应在权值的分配上,有效地降低了维度冗余对分类准确率的负面影响.R-kNN没有对房间和AP的物理位置做出任何假设,只需要使用环境中现存的AP就可以取得较好的定位效果,无需部署任何额外设施或修改现有设施.实验结果表明,在AP数量较多的楼宇环境中,R-kNN能够取得比k近邻算法和朴素贝叶斯分类器更好的定位效果.
Since GPS cannot be used under in-building environment and current in-building localization approaches require pre-installed infrastructure, in-building localization becomes a problem demanding prompt solutions for location-based services. Therefore, this paper proposes a novel room-level in- building localization algorithm R-kNN （relativity k-nearest neighbor）, which solves the localization problem by leveraging MAC address and RSSI （received signal strength indication） of Wi-Fi access points （APs） deployed in buildings. R-kNN falls into category of property-weighted k-nearest neighbor algorithm. By assigning the weight of each AP according to the relativity between AP pairs, R-kNN can reduce the negative effect of dimension redundancy. Moreover, since it makes no assumption on the physical distribution of rooms and APs, R-kNN can work well with existing APs without deploying any new infrastructure or modifying the existing ones. Experimental results demonstrate that when a large number of APs are available, the localization accuracy of R-kNN is bigger than those of the original kNN algorithm and naive Bayes classifier, while its false positive ratio and false negative ratio is smaller than those of the original kNN algorithm and Naive Bayes classifier in most cases.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2013年第3期568-577,共10页
Journal of Computer Research and Development
基金
国家自然科学基金项目(61170296)
新世纪优秀人才支持计划基金项目(NECT-09-0028)
软件开发环境国家重点实验室基金项目(SKLSDE-2012ZX-17)
关键词
楼宇内定位
WI-FI
RSSI
K近邻算法
属性加权k近邻算法
in-building localization
Wi-Fi
received signal strength indication （RSSI）
k-nearestneighbor
property-weighted k-nearest neighbor
