  With the advent of powerful, low-cost IoT systems, processing data closer to
where the data originates, known as edge computing, has become an increasingly
viable option. In addition to lowering the cost of networking infrastructures,
edge computing reduces edge-cloud delay, which is essential for
mission-critical applications. In this paper, we show the feasibility and study
the performance of image classification using IoT devices. Specifically, we
explore the relationships between various factors of image classification
algorithms that may affect energy consumption such as dataset size, image
resolution, algorithm type, algorithm phase, and device hardware. Our
experiments show a strong, positive linear relationship between three predictor
variables, namely model complexity, image resolution, and dataset size, with
respect to energy consumption. In addition, in order to provide a means of
predicting the energy consumption of an edge device performing image
classification, we investigate the usage of three machine learning algorithms
using the data generated from our experiments. The performance as well as the
trade offs for using linear regression, Gaussian process, and random forests
are discussed and validated. Our results indicate that the random forest model
outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79
for two different validation datasets.
