Static vs. Dynamic Databases for Indoor Localization based on Wi-Fi
  Fingerprinting: A Discussion from a Data Perspective
Zhe Tang Ruocheng Gu Sihao Li Kyeong Soo Kim Jeremy S. Smith
Wi-Fi fingerprinting has emerged as the most popular approach to indoor
localization. The use of ML algorithms has greatly improved the localization
performance of Wi-Fi fingerprinting, but its success depends on the
availability of fingerprint databases composed of a large number of RSSIs, the
MAC addresses of access points, and the other measurement information. However,
most fingerprint databases do not reflect well the time varying nature of
electromagnetic interferences in complicated modern indoor environment. This
could result in significant changes in statistical characteristics of
training/validation and testing datasets, which are often constructed at
different times, and even the characteristics of the testing datasets could be
different from those of the data submitted by users during the operation of
localization systems after their deployment. In this paper, we consider the
implications of time-varying Wi-Fi fingerprints on indoor localization from a
data-centric point of view and discuss the differences between static and
dynamic databases. As a case study, we have constructed a dynamic database
covering three floors of the IR building of XJTLU based on RSSI measurements,
over 44 days, and investigated the differences between static and dynamic
databases in terms of statistical characteristics and localization performance.
The analyses based on variance calculations and Isolation Forest show the
temporal shifts in RSSIs, which result in a noticeable trend of the increase in
the localization error of a Gaussian process regression model with the maximum
error of 6.65 m after 14 days of training without model adjustments. The
results of the case study with the XJTLU dynamic database clearly demonstrate
the limitations of static databases and the importance of the creation and
adoption of dynamic databases for future indoor localization research and
real-world deployment.