Development of Indoor Location Estimation System Using Wi-Fi Access Points and Cloud Service

We develop an indoor location estimation model using Azure Machine Learning (Azure ML) with the goal of establishing an indoor location estimation method using Wi-Fi access points and cloud service on Artificial Intelligence (AI). In this study, we evaluate the accuracy of location estimation by access point selection method and the amount of data set for indoor location estimation in the corridor of the school. This study evaluates the accuracy of location estimation based on access point selection methods and the amount of data set for indoor location estimation of the school corridor. As a result, it is found that the use of access points in the corridor is optimal when targeting the corridor, and the amount of data set is required for 4 days.