LOCATER 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. Comparing with localisation techniques in sensor network community, LOCATER is 1) off-the-shelf, i.e., LOCATER does not reuqire installing any new hardwares in buildins and thus could potentially be widely deployed; 2) passive, i.e., LOCATER does not need to install any new softwares in users' side, such as phone or laptop; 3) effective, i.e., LOCATER can achieve around 90% accuracy, which is a good number for many applications.
T-COVE is an exposure tracing and occupancy system based on cleaning wi-fi events on organizational premises. It first supports a real-time occupancy tracking application that displays real-time occupancy, i.e., the number of users, of locations of different granularities, such as building/floor/region. T-COVE has been deployed in over 30 buildings in UCI and BSU campuses and has been running since 2020. T-COVE will be planned to be installed in several other campuses and companies in the future. Another application supported in T-COVE is a passive exposure tracing system with potentially 100% adoption in campus area, that could be used effectively to track exposures as one of COVID-19 protection polycies in UCI. T-COVE is passive and off-the-shelf without the needs to install any new hardware or software while achieving a very usable accuracy, around 90%.