Machine Learning-Based Indoor Localization System with Human Computer Interaction System

Abstract

Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods. KEYWORDS: Indoor positioning, RSSI, Fingerprinting, Machine Learning, MQTT, ESP32, BLE beacons, NodeRED