# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 3 Methodology
### Selection of Dataset and Model  
In our study, we selected two standard benchmarking datasets that are symbolic of common tasks in IoT machine learning, pairing each with a model best suited to its characteristics for an insightful comparative analysis. We selected the FashionMNIST dataset ([[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)), accessed on 15 May 2024), which comprises 60,000 grayscale images of 10 fashion categories, each of which was 28 \(\times\) 28 pixels, and we utilized a Convolutional Neural Network (CNN) model. This  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Key Findings or Features** & **Identified Gaps and Our Contributions** \\ \hline \multirow{6}{*}{[PERSON] et al. [26]} & Twenty Python libraries of six different categories were benchmarked. Deep learning was of one of those categories that had 4 libraries. The comparision was made based on the available feature each library offered. Pytorch and Keras was recommended for rapid prototyping. & Practical evaluation was not considered to assess the efficiency of the selected libraries. Only Python libraries were analyzed instead of covering lower-level libraries written in C++. Environmental sustainability was overlooked. Our system includes an environmental impact assessment, guiding towards more sustainable ML development. & The CPU temperature was not measured during the experiments, although it was in our case. Only trained models were loaded in the device. We train the model in the device. \\ \hline \multirow{2}{*}{[PERSON] et al. [28]} & Compared and analyzed the deployment of two trained model architectures using five ML libraries in multiple types of devices. The device selection ranged from low-resource to high-performing laptops. & Limited to only high-level language library. Low-level libraries for deep learning are assessed in our study. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Proposed methodology for our experimental setup, chosen metrics, and decision support system.  
choice was driven by the CNN's proven capability in handling image data, particularly its effectiveness in discerning patterns and features essential for classifying different types of clothing and accessories. In addition, we also opted for the MNIST dataset ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)), accessed on 15 May 2024), which is the predecessor of the FashionMNIST dataset. It consists of 60,000 grayscale images of the English digits (i.e., from 0 to 9) having the same resolution as the FashionMNIST. The architecture of the CNN, tailored to efficiently process the varying shapes and styles present in the FashionMNIST and the MNIST dataset, enables accurate and rapid image classification, making it an ideal match for this type of visual data common in e-commerce and fashion industry applications.