Online Co-Localization in Indoor Wireless Networks by Dimension Reduction

This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals that come in a stream manner, a problem which we call online co-localization, by exploiting both labeled and unlabeled data from mobile devices and access points. Many tracking systems function in two phases: an offline training phase and an online localization phase. In the training phase, models are built from a batch of data that are collected offline. Many of them can not cope with a dynamic environment in which calibration data may come sequentially. In such case, these systems may gradually become inaccurate without a manually costly re-calibration. To solve this problem, we proposed an online co-localization method that can deal with labeled and unlabeled data stream based on semi-supervised manifold-learning techniques. Experiments conducted in wireless local area networks show that we can achieve high accuracy with less calibration effort as compared to s...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Jeffrey Junfeng Pan, Qiang Yang, Sinno Jialin Pan
• Keywords: AAAI 2007, Access Points, Intelligent Agents, Mobile Devices, Training Phase