  Location based services, already popular with end users, are now inevitably
becoming part of new wireless infrastructures and emerging business processes.
The increasingly popular Deep Learning (DL) artificial intelligence methods
perform very well in wireless fingerprinting localization based on extensive
indoor radio measurement data. However, with the increasing complexity these
methods become computationally very intensive and energy hungry, both for their
training and subsequent operation. Considering only mobile users, estimated to
exceed 7.4billion by the end of 2025, and assuming that the networks serving
these users will need to perform only one localization per user per hour on
average, the machine learning models used for the calculation would need to
perform 65*10^12 predictions per year. Add to this equation tens of billions of
other connected devices and applications that rely heavily on more frequent
location updates, and it becomes apparent that localization will contribute
significantly to carbon emissions unless more energy-efficient models are
developed and used. This motivated our work on a new DL-based architecture for
indoor localization that is more energy efficient compared to related
state-of-the-art approaches while showing only marginal performance
degradation. A detailed performance evaluation shows that the proposed model
producesonly 58 % of the carbon footprint while maintaining 98.7 % of the
overall performance compared to state of the art model external to our group.
Additionally, we elaborate on a methodology to calculate the complexity of the
DL model and thus the CO2 footprint during its training and operation.
