Enhancing Decision-Making with Data Science in the Internet of Things Environments

Abstract

The Internet of Things (IoT) has emerged as a transformative technology, enabling various devices to interconnect and generate vast amounts of data. The insights contained within this data can revolutionize industries and improve decision-making processes. The heterogeneity, scale, and complexity of IoT data pose challenges for efficient analysis and utilization. In this paper, the field of data science is explored in the IoT context, focusing on critical techniques, applications, and challenges vital to realizing the full potential of IoT data. This paper explores the field of data science in the IoT context, focusing on critical techniques, applications, and challenges vital to realizing the full potential of IoT data. The distinctive qualities of IoT data, including its volume, velocity, variety, and veracity, are examined, and their impact on data science approaches is analyzed. Additionally, cutting-edge data science approaches and methodologies designed for IoT data, such as data preprocessing, data fusion, machine learning, and anomaly detection, are discussed. The importance of scalable and distributed data processing frameworks to handle IoT data's large-scale and real-time nature is highlighted. Furthermore, the application of data science in various IoT fields, such as smart cities, healthcare, agriculture, and industrial IoT, is explored. Finally, areas for future research and development are identified, such as privacy and security issues, understanding machine learning models, and ethical aspects of data science in IoT.