Summary of Deeploc: a Ubiquitous Accurate and Low-overhead Outdoor Cellular Localization System, by Ahmed Shokry et al.
DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System
by Ahmed Shokry, Marwan Torki, Moustafa Youssef
First submitted to arxiv on: 25 Jun 2021
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 DeepLoc is a deep learning-based outdoor localization system that achieves GPS-like accuracy without its limitations, leveraging cellular signals received from different cell towers as hints to localize mobile devices. The system trains on crowd-sensed geo-tagged received signal strength information to infer user positions, addressing practical challenges like data collection scalability and noise handling. Implemented on Android devices, DeepLoc’s median localization accuracy outperforms state-of-the-art systems by 470% in urban areas (18.8m) and rural areas (15.7m), while reducing power consumption by 330%. This promising system has the potential to become a ubiquitous accurate and low-overhead localization solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DeepLoc is a new way to figure out where your phone is using cellular signals. Instead of using GPS, which can be slow or unreliable, DeepLoc uses machine learning to analyze the signals it receives from nearby cell towers. This lets it pinpoint your location accurately, even in areas with poor coverage. The system was tested on different phones and showed impressive results, beating current systems by a big margin. It also uses much less power than GPS, making it a great option for low-end phones or devices that need to run for a long time. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
