  The primary expectation from positioning systems is for them to provide the
users with reliable estimates of their position. An additional piece of
information that can greatly help the users utilize position estimates is the
level of uncertainty that a positioning system assigns to the position estimate
it produced. The concept of dynamically estimating the accuracy of position
estimates of fingerprinting positioning systems has been sporadically discussed
over the last decade in the literature of the field, where mainly handcrafted
rules based on domain knowledge have been proposed. The emergence of IoT
devices and the proliferation of data from Low Power Wide Area Networks
(LPWANs) have facilitated the conceptualization of data-driven methods of
determining the estimated certainty over position estimates. In this work, we
analyze the data-driven approach of determining the Dynamic Accuracy Estimation
(DAE), considering it in the broader context of a positioning system. More
specifically, with the use of a public LoRaWAN dataset, the current work
analyses: the repartition of the available training set between the tasks of
determining the location estimates and the DAE, the concept of selecting a
subset of the most reliable estimates, and the impact that the spatial
distribution of the data has to the accuracy of the DAE. The work provides a
wide overview of the data-driven approach of DAE determination in the context
of the overall design of a positioning system.
