Comparative Analysis of Various Feature Extraction Methods on IoT 2023

Abstract

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond.