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Recent years have witnessed fast growth in outdoor location-based services.While GPS is considered a ubiquitous localization system, it is not supportedby low-end phones, requires direct line of sight to the satellites, and candrain the phone battery quickly.  In this paper, we propose DeepLoc: a deep learning-based outdoor localizationsystem that obtains GPS-like localization accuracy without its limitations. Inparticular, DeepLoc leverages the ubiquitous cellular signals received from thedifferent cell towers heard by the mobile device as hints to localize it. To dothat, crowd-sensed geo-tagged received signal strength information coming fromdifferent cell towers is used to train a deep model that is used to infer theuser's position. As part of DeepLoc design, we introduce modules to address anumber of practical challenges including scaling the data collection to largeareas, handling the inherent noise in the cellular signal and geo-tagged data,as well as providing enough data that is required for deep learning models withlow-overhead.  We implemented DeepLoc on different Android devices. Evaluation results inrealistic urban and rural environments show that DeepLoc can achieve a medianlocalization accuracy within 18.8m in urban areas and within 15.7m in ruralareas. This accuracy outperforms the state-of-the-art cellular-based systems bymore than 470% and comes with 330% savings in power compared to the GPS. Thishighlights the promise of DeepLoc as a ubiquitous accurate and low-overheadlocalization system.

DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System