Summary of Hierarchical Stage-wise Training Of Linked Deep Neural Networks For Multi-building and Multi-floor Indoor Localization Based on Wi-fi Rssi Fingerprinting, by Sihao Li et al.
Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting
by Sihao Li, Kyeong Soo Kim, Zhe Tang, Graduate, Jeremy S. Smith
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel solution for large-scale multi-building and multi-floor indoor localization using linked neural networks. The approach leverages hierarchical stage-wise training to train each network dedicated to a sub-problem, allowing for scalable processing of data with hierarchical representations. The proposed framework extends the original stage-wise training by training lower-hierarchy networks based on prior knowledge from higher-hierarchy networks. Experimental results on the UJIIndoorLoc dataset demonstrate the effectiveness of the linked neural networks in achieving accurate 3D localization errors (8.19m), outperforming previous neural network-based models trained and evaluated with full datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem: how to accurately locate people indoors across multiple buildings and floors using Wi-Fi signals. The solution uses special kinds of artificial intelligence called linked neural networks. These networks work together to figure out where someone is, taking into account the structure of the building. This approach helps solve the problem of processing large amounts of data efficiently. The results show that this method can be very accurate, even better than previous methods that used similar approaches. |
Keywords
» Artificial intelligence » Neural network
