Data-Triggered Approach for Real-Time Machine Learning in IoT Systems

We propose a method with the objective of maximizing the energy efficiency of devices equipped with light-weight ML classifiers without compromising the real-time classification accuracy. This specifically helps those devices that collect streaming data via sensors and analyze the data to offer real-time classification services. We propose and implement a hardware-friendly preprocessing mechanism that takes into account the accuracy of the ML classifier along with a proposed similarity metric between incoming data frames. The preprocessing mechanism combines the event-driven and time-driven strategies for scheduling the ML classifiers in a way that reduces the frequency of execution of the energy-hungry ML module. The event-driven strategy triggers the ML module only upon significant changes (dissimilarity) in the streaming data frames. Using the proposed preprocessing mechanism, we achieved up to 80% reduction in the number of function calls for the ML classifier while only losing less than 2% of classification accuracy.