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Indoor localization is the process of determining the location of a person orobject inside a building. Potential usage of indoor localization includesnavigation, personalization, safety and security, and asset tracking. Commonlyused technologies for indoor localization include WiFi, Bluetooth, RFID, andUltra-wideband. Among these, WiFi's Received Signal Strength Indicator(RSSI)-based localization is preferred because of widely available WiFi AccessPoints (APs). We have two main contributions. First, we develop our method,'IndoorGNN' which involves using a Graph Neural Network (GNN) based algorithmin a supervised manner to classify a specific location into a particular regionbased on the RSSI values collected at that location. Most of the ML algorithmsthat perform this classification require a large number of labeled data points(RSSI vectors with location information). Collecting such data points is alabor-intensive and time-consuming task. To overcome this challenge, as oursecond contribution, we demonstrate the performance of IndoorGNN on therestricted dataset. It shows a comparable prediction accuracy to that of thecomplete dataset. We performed experiments on the UJIIndoorLoc and MNAVdatasets, which are real-world standard indoor localization datasets. Ourexperiments show that IndoorGNN gives better location prediction accuracieswhen compared with state-of-the-art existing conventional as well as GNN-basedmethods for this same task. It continues to outperform these algorithms evenwith restricted datasets. It is noteworthy that its performance does notdecrease a lot with a decrease in the number of available data points. Ourmethod can be utilized for navigation and wayfinding in complex indoorenvironments, asset tracking and building management, enhancing mobileapplications with location-based services, and improving safety and securityduring emergencies.

IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI