DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System
Ahmed Shokry, Marwan Torki, Moustafa Youssef

TL;DR
DeepLoc is a deep learning-based outdoor localization system that achieves GPS-like accuracy using cellular signals, offering significant improvements in accuracy and power efficiency over existing cellular-based methods.
Contribution
DeepLoc introduces a novel deep learning approach leveraging cellular signals for accurate outdoor localization, addressing practical challenges like noise and data scaling.
Findings
Median accuracy of 18.8m in urban areas
Median accuracy of 15.7m in rural areas
Over 470% accuracy improvement over state-of-the-art
Abstract
Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large…
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