LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization
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DOI：
10.14778/3430915.3430923
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Yiming Lin;Daokun Jiang;Roberto Yus;Georgios Bouloukakis;Andrew Chio;S. Mehrotra;N. Venkatasubramanian-N.-Venkatasub
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文献类型：
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作者：
Yiming Lin;Daokun Jiang;Roberto Yus;Georgios Bouloukakis;Andrew Chio;S. Mehrotra;N. Venkatasubramanian-N.-Venkatasub
本文探讨了在使用WiFi连接数据将用户定位到语义室内位置（如建筑物、区域、房间）时出现的数据清理挑战。WiFi连接数据由设备与附近WiFi接入点（ap）之间的零星连接组成，每个接入点可能覆盖建筑物内相对较大的区域。我们的系统，称为语义定位清洁器（LOCATER），假设语义定位作为一系列数据清理任务——首先，它将确定设备在任意两个连接事件之间连接到的AP的问题视为缺失值检测和修复问题。然后，它将设备与语义子区域（例如，该区域的会议室）联系起来，假设它是一个位置消歧问题。LOCATER使用自举半监督学习方法进行粗定位，使用概率方法实现精细定位。研究表明，LOCATER在粗、细两个层次上都能达到很高的精度。
This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.