Tiny but Mighty: Embedded Machine Learning for Indoor Wireless Localization

There is an increasing demand for accurate indoor localisation that require minimal infrastructure and wide coverage. All radio technologies commonly used for localisation have trade-offs and there is no clear winner. Accurate systems such as ultra-wideband (UWB) radio, due to their short range of tens of meters, require dense deployment of beacons, incurring huge cost. Such systems out-match the 2.4GHz narrowband technologies with their 10cm accuracy against a few metres with the latter. However, long range technologies such as LoRa, while operating in the 2.4GHz band, can provide a few kilometers of range with fewer beacons in ideal conditions and are therefore cost effective whilst providing greater coverage. We argue in this paper that the desire of having the best of both worlds, i.e., long range, low infrastructure costs, and sub-meter accuracy, can be achieved using embedded machine learning, often referred to as TinyML. To this extent, we run an extensive data collection campaign for two radio technologies i.e., UWB and LoRa, and train machine learning models to improve their localization performance. We then deploy these models on the tiniest of devices powered by ARM Cortex M4 microcontrollers. To the best of our knowledge, we are the first to demonstrate that on-device machine learning can significantly improve localization accuracy. In our case, UWB and LoRa based systems have been shown to improve their localization accuracy by similar to 20% and similar to 70% respectively, through learning and essentially calibrating with a more accurate optical camera system without inheriting their weaknesses.