Empowering Resource-Constrained IoT Edge Devices: A Hybrid Approach for Edge Data Analysis

The efficient development of accurate machine learning (ML) models for Internet of Things (IoT) edge devices is crucial for enabling intelligent decision-making at the edge of the network. However, the limited computational resources of IoT edge devices, such as low processing power and constrained memory, pose significant challenges in implementing complex ML algorithms directly on these devices. This paper addresses these challenges by proposing a hybrid ML model that combines Principal Component Analysis (PCA), Decision Tree (DT), and Support Vector Machine (SVM) classifiers. By utilizing hardware-friendly techniques such as dimensionality reduction, optimized hyperparameters, and the combination of accurate and interpretable classifiers, the proposed hybrid model addresses the limitations of IoT edge devices. The proposed hybrid model enables intelligent decision-making at the edge while minimizing computational and energy costs. Experimental evaluations demonstrate the improved performance and resource utilization of the proposed model, providing insights into its effectiveness for IoT edge applications.