Data-Driven IoT Systems: Enhancing Efficiency and Performance in Smart Environments.

Abstract

This paper explores the integration of data-driven approaches in Internet of Things (IoT) systems to enhance efficiency and performance within smart environments. As IoT devices proliferate across various domains such as smart cities, healthcare, and industrial automation, the volume of data generated is exponentially increasing. The ability to collect, analyze, and utilize this data in real-time is critical for optimizing system performance, improving decision-making, and driving automation. This research discusses advanced data analytics techniques, including machine learning and edge computing, to process and extract actionable insights from IoT data. By leveraging these technologies, IoT systems can optimize resource usage, improve predictive capabilities, and enable dynamic adaptation to changing environmental conditions. Additionally, the paper addresses the challenges related to data privacy, security, and scalability in IoT ecosystems. The findings highlight the potential of data-driven IoT systems in advancing the functionality and impact of smart environments, providing a pathway for more efficient and sustainable technological solutions.