GA-Tuned Ensemble Learning for Improving the Performance of Wi-Fi RSS-Based Indoor Positioning

This research explores indoor location estimation in the Internet of Things (IoT) using Wi-Fi signals. Due to the influence of surroundings on signal strength, advanced techniques like Machine Learning (ML) algorithms are needed for accurate location estimation. The challenge lies in balancing accuracy with real-time operation and resource limitations. This research proposes using Ensemble Learning (EL), combining multiple machine learning models, to improve accuracy. However, this increases computational cost. The study uses Genetic Algorithm (GA) to find an optimal Ensemble model that balances accuracy and resource usage.The proposed method is tested on Wi-Fi data collected across multiple floors of a building, unlike previous studies that focused on a single floor. This comprehensive approach leads to an average localization error of 0.27 meters with an accuracy of 95%. In comparison to previous single-floor studies, the model achieves an accuracy of 98.3% with an error of 0.25 meters on it.