Bluetooth Low Energy-based Indoor Localization using Artificial Intelligence

Abstract

Bluetooth is one of the several technologies to cater to indoor localization. It has the lowest power consumption and good accuracy performance. In the world of IoT, data from sensors and software help in giving meaning to physical objects connected to the internet. This paper uses data gathered using Bluetooth Low-Energy sensors in predicting an agent's location in an indoor environment. We propose a Bluetooth-based model that is divided into two parts: a Convolutional Neural Network(CNN) that trains on data transformed into images and ideas from Game Theory that uses the Markov Decision Process(MDP) to determine the exact location of the agent. The data to image transformation uses the Image Generator for Tabular Data (IGTD) algorithm, which considers the Euclidean distances between the access points in creating the images. The results show that the CNN trains well on transformed images and offers a solid approach to determining every beacon used for Bluetooth-based indoor localization. After a beacon is found, MDP finds the optimal policy to locate the access point under which the agent lies.