Image Classification on IoT Edge Devices: Profiling and Modeling
This work addresses energy efficiency for IoT edge computing applications, but it is incremental as it applies existing machine learning methods to model energy consumption without introducing new algorithms.
The paper studied the feasibility and performance of image classification on IoT edge devices, analyzing how factors like model complexity, image resolution, and dataset size affect energy consumption, and found that a random forest model best predicts energy consumption with R-squared values of 0.95 and 0.79 on validation datasets.
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.