LOCATER：清理WiFi连接数据集以进行语义本地化
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization
摘要：
本文探讨了使用WiFi连接数据将用户定位到建筑物、区域、房间等语义室内位置时出现的数据清理挑战。WiFi连接数据由设备和附近的WiFi接入点（AP）之间的零星连接组成，每个AP可以覆盖建筑物内相对较大的区域。我们的系统，名为语义LOCATion清洁（LOCATER），假设语义本地化作为一系列的数据清理任务-首先，它对待的问题，确定一个设备连接到AP之间的任何两个连接事件作为一个缺失值检测和修复问题。然后，它将设备与语义子区域相关联（例如，区域中的会议室）。LOCATER使用自举半监督学习方法进行粗略定位，并使用概率方法实现更精细的定位。本文表明，定位器可以实现显着的高精度在粗和细的水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
