  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
areas, 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 with
low-overhead.
  We implemented DeepLoc on different Android devices. Evaluation results in
realistic urban and rural environments show that DeepLoc can achieve a median
localization accuracy within 18.8m in urban areas and within 15.7m in rural
areas. This accuracy outperforms the state-of-the-art cellular-based systems by
more than 470% and comes with 330% savings in power compared to the GPS. This
highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead
localization system.
