Classifying Smart IoT Devices for Running Machine Learning Algorithms

Abstract

Tiny computers called System-on-a-Chip like Raspberry Pi have revolutionized the development of applications for Smart Home and Smart City. Some Machine Learning algorithms have been used to process a large amount of data produced by these Internet of Things (IoT) devices. An important issue in the context of processing IoT data is the decision on where the machine learning algorithm will run. To support this decision, it is necessary to classify the IoT devices according to their capabilities to run these algorithms, in terms of CPU performance, required memory, and energy demand. The aim of this paper is to classify IoT devices according to their capabilities to run machine learning algorithms, and reporting real experiments that validate the proposed classification.