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T. Burgess, B. Metzler, A. Ettlinger, H.-B. Neuner:
"Geometric Constraint Model and Mobility Graphs for Building Utilization Intelligence";
Talk: 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes (invited); 09-24-2018 - 09-27-2018; in: "IPIN 2018", (2018), 7 pages.

In recent years the availability of mobile indoor way
finding technologies, Internet-of-Things sensor infrastructures,
and geographic information systems has dramatically increased.
These systems have been introduced for many reasons, such as:
increasing accessibility to users with reduced mobility, optimizing
logistics, or reducing the environmental impact of facilities. While
these technologies can bring some new insight in how buildings
are used, they all suffer from limitations in coverage, accuracy, or
scalability. However, when they are combined, a new window into
the understanding of human mobility and building utilization is
opened.
This paper introduces methods to increase accuracy of data
obtained from an off-the-shelf commercial indoor navigation by
including Internet of Things (IoT) sensor infrastructure and
geographic information through a Geometric Constraint Model
(GCM). Furthermore, a Mobility Graph (MG) is introduced
to visualize trajectory distributions. A simple experiment was
conducted to validate the methods. For this, a long narrow
hall was equipped with iBeacon infrastructure, an indoo.rs
Navigation instance, and a few cheap Raspberry Pi based sensor
stations. The environment was mapped using state-of-art geodetic
measurements, and a set of recorded experiments were made.
In such a long and narrow environment the indoor navigation
system is unable to resolve features along the short traverse axis.
Without a combined analysis, a divider in the environment is not
discernible in the MG, while results using the GCM introduced
in this paper clearly indicate the presence of this divider.
Automating this approach, can bring indoor location analytics
from a descriptive to a prescriptive regime. Furthermore, the
application of MG provide a useful tool to better understand
large indoor navigation data sets.

Indoor navigation, Internet of Things, Data Mining, Particle Filter, Data Acquisition