Power-efficient access-point selection for indoor location estimation
Power-efficient access-point selection for indoor location estimation
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DOI：
10.1109/tkde.2006.112
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发表时间：
2006-07-01
影响因子：
8.9
通讯作者：
Chai, Xiaoyong
中科院分区：
文献类型：
--
作者：
Chen, Yiqiang;Yang, Qiang;Chai, Xiaoyong
室内位置估计系统的一个重要目标是在降低功耗的同时提高估计精度。在本文中，我们提出了一种新的算法称为CaDet的功率效率的位置估计，通过智能地选择用于位置估计的接入点（AP）的数量。我们表明，通过采用机器学习技术，CaDet能够使用环境中的AP的一个小子集来检测客户端的位置，具有很高的准确性。CaDet结合了信息论、聚类分析和决策树算法。通过收集数据和测试我们的算法在一个现实的WLAN环境中，在香港科技大学的计算机科学系领域，我们表明，CaDet（聚类和决策树为基础的方法）可以更高的准确性相比，其他方法。我们还通过实验表明，通过智能选择AP，我们能够节省客户端设备上的功率，同时达到相同的准确度。
An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of Access Points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet ( Clustering and Decision Tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy.