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With the advent of powerful, low-cost IoT systems, processing data closer towhere the data originates, known as edge computing, has become an increasinglyviable option. In addition to lowering the cost of networking infrastructures,edge computing reduces edge-cloud delay, which is essential formission-critical applications. In this paper, we show the feasibility and studythe performance of image classification using IoT devices. Specifically, weexplore the relationships between various factors of image classificationalgorithms that may affect energy consumption such as dataset size, imageresolution, algorithm type, algorithm phase, and device hardware. Ourexperiments show a strong, positive linear relationship between three predictorvariables, namely model complexity, image resolution, and dataset size, withrespect to energy consumption. In addition, in order to provide a means ofpredicting the energy consumption of an edge device performing imageclassification, we investigate the usage of three machine learning algorithmsusing the data generated from our experiments. The performance as well as thetrade offs for using linear regression, Gaussian process, and random forestsare discussed and validated. Our results indicate that the random forest modeloutperforms the two former algorithms, with an R-squared value of 0.95 and 0.79for two different validation datasets.

Image Classification on IoT Edge Devices: Profiling and Modeling