Integration of bluetooth technology and artificial neural networks in high accuracy indoor positioning

Nowadays, with the increasing digitalization and widespread use of Internet of Things (IoT) applications, accurate and reliable indoor positioning system applications have become increasingly important. With the increasing need in the areas of use, researchers have proposed various indoor positioning system technologies and methods. In this context, Bluetooth Low Energy (BLE) technology offers a cost-effective, energy-efficient, and easy-to-integrate solution within existing infrastructures. The integration of BLE technology and ANNs for indoor positioning will be the subject of this section, which will cover the theoretical basis and practical applications. A comprehensive review of BLE-based positioning algorithms, performance evaluations, and the design and training of ANN models will be presented. In addition, the limitations of existing systems will be highlighted through the examination of the challenges. The aim of this study is to make a meaningful contribution to the field of indoor positioning and to provide applicable solutions in both academic and industrial environments.