IOT DATA ANALYTICS: TECHNIQUES, TOOLS, AND APPLICATIONS

Abstract

The proliferation of Internet of Things (IoT) devices has generated an unprecedented volume of data, necessitating advanced analytics to derive meaningful insights and enhance decision-making processes. This review paper explores the current landscape of IoT data analytics, focusing on the techniques, tools, and applications that drive value from IoT-generated data. It provides a comprehensive overview of various analytical techniques such as machine learning, deep learning, and statistical methods, and examines their applicability in different IoT contexts. The paper also discusses a range of tools and platforms available for IoT data analytics, highlighting their capabilities and limitations. Additionally, it presents real-world applications across diverse sectors including healthcare, manufacturing, smart cities, and agriculture, demonstrating the transformative impact of IoT data analytics. By synthesizing the latest advancements and identifying key challenges, this review aims to provide a holistic understanding of IoT data analytics and guide future research and implementation strategies.