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

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Arabsorkhi Hamid; Ghaemifar Mohammadali; Ebadollahi Saeed; Moradi Saina

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

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

Author(s)

Arabsorkhi Hamid | Ghaemifar Mohammadali | Ebadollahi Saeed | Moradi Saina

Keywords

Internet Of Things

Indoor Positioning

Machine Learning

Ensemble Learning

Genetic Algorithm

Abstract

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.

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APA: Copy

Arabsorkhi, Hamid, Ghaemifar, Mohammadali, Ebadollahi, Saeed, & Moradi, Saina. (2024). GA-Tuned Ensemble Learning for Improving the Performance of Wi-Fi RSS-Based Indoor Positioning. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147678/en

Vancouver: Copy

Arabsorkhi Hamid, Ghaemifar Mohammadali, Ebadollahi Saeed, Moradi Saina. GA-Tuned Ensemble Learning for Improving the Performance of Wi-Fi RSS-Based Indoor Positioning. 2024. Available from: https://sid.ir/paper/1147678/en

IEEE: Copy

Hamid Arabsorkhi, Mohammadali Ghaemifar, Saeed Ebadollahi, and Saina Moradi, “GA-Tuned Ensemble Learning for Improving the Performance of Wi-Fi RSS-Based Indoor Positioning,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147678/en

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