AI and Data Engineering in the Age of IoT: Efficient Data Management for Smart Devices

Abstract

In the era of the Internet of Things (IoT), the integration of Artificial Intelligence (AI) and Data 
Engineering has become pivotal for the efficient management and utilization of vast streams of 
data generated by smart devices. This paper explores the synergies between AI and Data 
Engineering in addressing the challenges posed by the exponential growth of IoT data. We 
examine advanced data management techniques, including data ingestion, preprocessing, storage, 
and real-time analytics, to enhance the performance and scalability of IoT systems. By leveraging 
AI algorithms and machine learning models, we aim to optimize data processing workflows, 
improve data quality, and enable intelligent decision-making. Key methodologies discussed 
include automated data cleaning, feature engineering, and predictive analytics, which collectively 
contribute to a more efficient and responsive IoT ecosystem. The paper presents case studies 
demonstrating the application of these techniques in various IoT domains, such as smart homes, 
industrial automation, and healthcare. Our findings underscore the importance of integrating AIdriven data management strategies to harness the full potential of IoT data, ensuring reliable and 
scalable solutions for emerging smart environments.