Room-Level Indoor Localization with Artificial Neural Networks

Abstract

Indoor localization system determines the location of the users or some assets in indoor environments. There are important applications of indoor localization including smart home systems, indoor navigation and tracking systems. In this work, a reliable neural network model is developed for localizing users in room level. Model is based on Wi-Fi signals received by the users’ devices at different rooms from various Wi-Fi access points. A neural network with two hidden layers with sigmoid activation functions is trained with back-propagation optimizing collected signal data. Some of the signals are set to 0 during the training process, which gives significant stability to the model under the conditions where some of the data required for prediction are not available. An additional dataset is collected for the evaluation in addition to the existing datasets. Performance of the model on the existing datasets as well as the new collected dataset is discussed and evaluated. Results are promising in terms of reliability and accuracy.