  The evaluation of Indoor Positioning Systems (IPS) mostly relies on local
deployments in the researchers' or partners' facilities. The complexity of
preparing comprehensive experiments, collecting data, and considering multiple
scenarios usually limits the evaluation area and, therefore, the assessment of
the proposed systems. The requirements and features of controlled experiments
cannot be generalized since the use of the same sensors or anchors density
cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a
similar level as machine-learning models, where new proposals are evaluated
over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in
multiple scenarios, that is validated with three use cases. The results prove
that the proposed aggregation of the evaluation metric values is a useful tool
for high-level comparison of IPSs.
