Optimizing decision-making in IoT through ensemble voting and dynamic group formation with DBSCAN

Abstract

In the era of widespread connectivity, leveraging artificial intelligence models and analyzing the vast datasets generated by smart devices are central points in IoT research. While existing studies mainly focus on improving the decision-making prowess of central systems, the potential for local optimization remains largely unexplored. This paper presents an Ensemble Voting Scheme with Multilayer Dynamic Groups (EVMDS), which assigns decision weights to IoT devices based on their attribute data. By employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, dynamic clusters among IoT devices can be identified, the application of ensemble voting rules at each stage of group formation, enabling layered computations to ease backend burden and achieve hierarchical decision-making capability, facilitating regional-level decision-making that strikes a balance between local and global optimization. Through simulated decision-making scenarios in a small-scale IoT environment, our experiments demonstrate the superior accuracy and reliability of the proposed approach compared to existing models.