Energy-Efficient Data Engineering and Transformation for IoT Systems
TL;DR
This paper presents a comprehensive framework for energy-efficient data engineering and transformation tailored for IoT systems and explores key techniques including edge-based preprocessing, adaptive sampling, lightweight transformation pipelines, and context-aware data aggregation.
Abstract
As the Internet of Things (IoT) continues to expand across domains such as smart cities, healthcare, and industrial automation, the volume and velocity of data generated by connected devices pose significant challenges for energy-efficient data management. Traditional data engineering pipelines are often ill-suited for the resource-constrained and distributed nature of IoT environments. This paper presents a comprehensive framework for energy-efficient data engineering and transformation tailored for IoT systems. We explore key techniques including edge-based preprocessing, adaptive sampling, lightweight transformation pipelines, and context-aware data aggregation. Through empirical analysis and system-level evaluations, we demonstrate how optimized data workflows can significantly reduce energy consumption without compromising data fidelity. Our findings offer practical insights and design guidelines for building sustainable, scalable, and energy-aware IoT data infrastructures.