  Efficiently querying data on embedded sensor and IoT devices is challenging
given the very limited memory and CPU resources. With the increasing volumes of
collected data, it is critical to process, filter, and manipulate data on the
edge devices where it is collected to improve efficiency and reduce network
transmissions. Existing embedded index structures do not adapt to the data
distribution and characteristics. This paper demonstrates how applying learned
indexes that develop space efficient summaries of the data can dramatically
improve the query performance and predictability. Learned indexes based on
linear approximations can reduce the query I/O by 50 to 90% and improve query
throughput by a factor of 2 to 5, while only requiring a few kilobytes of RAM.
Experimental results on a variety of time series data sets demonstrate the
advantages of learned indexes that considerably improve over the
state-of-the-art index algorithms.
