Deep Learning-Based Indoor Localization by Using WiFi Fingerprinting and a CNN Algorithm

In this research, a new approach is proposed to accurately predict indoor three-dimensional localization based on Received Signal Strength (RSS) values. This work uses WiFi beacons to collect time-series RSS data, preprocess it, and feed it to the proposed model. The proposed model presents a novel architecture based on 2D convolutional neural networks, and this model employs a multitask learning approach. Hence, the model simultaneously has a classifier for floor classification and a regressor for estimating X and Y coordinates, and tries to perform accurate indoor localization even in environments with furniture and other obstacles. The proposed CNN-based model efficiently utilizes RSSI data, achieving 99.0% floor classification accuracy and 6.5 m in terms of Euclidean distance error based on coordinate estimation on the UJIIndoorLoc dataset. On the other hand, the validation results using Tampere datasets were ‘distance error (m) = 3.7868’ form localization and ‘accuracy = 98.99%’ for floor classification. Comprehensive preprocessing significantly enhances localization accuracy.