From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning Systems
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Summary
This summary is machine-generated.This review explores Machine Learning (ML) for Indoor Positioning Systems (IPS) using Wi-Fi and Bluetooth. It highlights Deep Learning (DL) advancements and identifies challenges like environmental variability for future research.
Area Of Science
- Computer Science
- Electrical Engineering
- Ubiquitous Computing
Background
- Indoor Positioning Systems (IPS) are crucial for smart environments, evolving from basic signal strength to advanced techniques.
- Methods like Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA) are increasingly integrated with Machine Learning (ML).
Purpose Of The Study
- To systematically review Machine Learning-based Indoor Positioning Systems (IPS) utilizing IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE).
- To analyze measurement techniques, ML models, and the rise of Deep Learning (DL) in IPS literature from 2020-2024.
Main Methods
- Systematic literature review adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology.
- Analysis of studies published between 2020 and 2024 focusing on ML applications in Wi-Fi and BLE-based IPS.
Main Results
- Identified a growing trend in the application of Deep Learning (DL) approaches for indoor positioning.
- Examined various measurement collection techniques and ML models employed in recent IPS research.
- Highlighted persistent implementation challenges including environmental variability, device heterogeneity, and calibration requirements.
Conclusions
- Future research should focus on expanding ML to RTT and AoA, developing hybrid multimetric systems, and creating efficient, adaptive DL models.
- Advances in wireless standards and emerging technologies are key to enhancing the accuracy and scalability of next-generation IPS.
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