  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 7.4 billion 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 $65 \times 10^{12}$ 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. In this Chapter, we discuss the latest results
and trends in wireless localization and look at paths towards achieving more
sustainable AI. We then elaborate on a methodology for computing DL model
complexity, energy consumption and carbon footprint and show on a concrete
example how to develop a more resource-aware model for fingerprinting. We
finally compare relevant works in terms of complexity and training CO$_2$
footprint.
