  A smartphone-based user mobility tracking could be effective in finding
his/her location, while the unpredictable error therein due to low
specification of built-in inertial measurement units (IMUs) rejects its
standalone usage but demands the integration to another positioning technique
like WiFi positioning. This paper aims to propose a novel integration technique
using a graph neural network called Mobility-INduced Graph LEarning (MINGLE),
which is designed based on two types of graphs made by capturing different user
mobility features. Specifically, considering sequential measurement points
(MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor
MPs as edges, called time-driven mobility graph (TMG). Second, a user's
relatively straight transition at a constant pace when moving from one position
to another can be captured by connecting the nodes on each path, called a
direction-driven mobility graph (DMG). Then, we can design graph convolution
network (GCN)-based cross-graph learning, where two different GCN models for
TMG and DMG are jointly trained by feeding different input features created by
WiFi RTTs yet sharing their weights. Besides, the loss function includes a
mobility regularization term such that the differences between adjacent
location estimates should be less variant due to the user's stable moving pace.
Noting that the regularization term does not require ground-truth location,
MINGLE can be designed under semi- and self-supervised learning frameworks. The
proposed MINGLE's effectiveness is extensively verified through field
experiments, showing a better positioning accuracy than benchmarks, say root
mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and
semi-supervised learning cases, respectively.
