  Indoor localization has gained significant attention in recent years due to
its various applications in smart homes, industrial automation, and healthcare,
especially since more people rely on their wireless devices for location-based
services. Deep learning-based solutions have shown promising results in
accurately estimating the position of wireless devices in indoor environments
using wireless parameters such as Channel State Information (CSI) and Received
Signal Strength Indicator (RSSI). However, despite the success of deep
learning-based approaches in achieving high localization accuracy, these models
suffer from a lack of generalizability and can not be readily-deployed to new
environments or operate in dynamic environments without retraining. In this
paper, we propose meta-learning-based localization models to address the lack
of generalizability that persists in conventionally trained DL-based
localization models. Furthermore, since meta-learning algorithms require
diverse datasets from several different scenarios, which can be hard to collect
in the context of localization, we design and propose a new meta-learning
algorithm, TB-MAML (Task Biased Model Agnostic Meta Learning), intended to
further improve generalizability when the dataset is limited. Lastly, we
evaluate the performance of TB-MAML-based localization against conventionally
trained localization models and localization done using other meta-learning
algorithms.
