  Indoor localization is the process of determining the location of a person or
object inside a building. Potential usage of indoor localization includes
navigation, personalization, safety and security, and asset tracking. Commonly
used technologies for indoor localization include WiFi, Bluetooth, RFID, and
Ultra-wideband. Among these, WiFi's Received Signal Strength Indicator
(RSSI)-based localization is preferred because of widely available WiFi Access
Points (APs). We have two main contributions. First, we develop our method,
'IndoorGNN' which involves using a Graph Neural Network (GNN) based algorithm
in a supervised manner to classify a specific location into a particular region
based on the RSSI values collected at that location. Most of the ML algorithms
that perform this classification require a large number of labeled data points
(RSSI vectors with location information). Collecting such data points is a
labor-intensive and time-consuming task. To overcome this challenge, as our
second contribution, we demonstrate the performance of IndoorGNN on the
restricted dataset. It shows a comparable prediction accuracy to that of the
complete dataset. We performed experiments on the UJIIndoorLoc and MNAV
datasets, which are real-world standard indoor localization datasets. Our
experiments show that IndoorGNN gives better location prediction accuracies
when compared with state-of-the-art existing conventional as well as GNN-based
methods for this same task. It continues to outperform these algorithms even
with restricted datasets. It is noteworthy that its performance does not
decrease a lot with a decrease in the number of available data points. Our
method can be utilized for navigation and wayfinding in complex indoor
environments, asset tracking and building management, enhancing mobile
applications with location-based services, and improving safety and security
during emergencies.
