Two Level Wi-Fi Fingerprinting based Indoor Localization using Machine Learning

Abstract

Indoor localization is defined as the process of locating a user or device in an indoor environment. It plays a crucial role for first responders in disaster and emergency situations. In situations where the environment does not change much after an incident happens, fingerprint based indoor localization can be used for effective localization and positioning. Due to the growth in smartphone users in the last few years, indoor localization using Wi-Fi fingerprints has been studied by researchers. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in various indoor locations. In disaster and emergency situations, localization services should be highly accurate and fast. We can model localization as a classification problem and address using machine learning (ML) approaches. However, these two requirements are conflicting since an accurate fingerprint-based indoor localization system needs to process a large amount of data, and this leads to slow response. This problem becomes even worse when both the number of floors and the number of reference points increase. To address this challenge, we use a two-level localization in order to improve both the accuracy and the response time. First, the fingerprint database is used to train ML models. The localization phase has two steps: (i) floor prediction, and (ii) reference point prediction on the predicted floor. For floor prediction, we use K-Nearest Neighbors (KNN) classification algorithm. Then we use various ML models such as Random Forest, Decision Tree, and Support Vector Machine. We use a dataset having two files with different floor numbers. Experiment results showed that random forest gives the best accuracy among other ML models. So two-level localization method is more suitable than single level localization in terms of localization accuracy and speed, and thus can be utilized in many applications.