Yiming Lin (UCI): LOCATER – Cleaning WiFi Connectivity Datasets for Semantic Localization

Speaker:
Yiming Lin, UCI

Abstract:
Sensor data is abundant in our life but often dirty to generate services with high quality. This talk 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. Evaluation on both real and synthetic datasets shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.

Bio:

Yiming Lin is a Ph.D. student in the Department of Computer Science at the University of California, Irvine. He is currently doing research on data cleaning and data stream analysis. He is the recipient of Hasso Plattner Institute Fellowship since 2020.

Zoom Link:
https://uci.zoom.us/j/95066121155