An Exhaustive and Methodical Analysis of Literature Pertaining To Technigues for Managing Big Data within the Context of the Internet of Things

Abstract

The merging of big data and the Internet of Things (IoT) has brought both exceptional difficulties and possibilities in data management. This study offers a thorough and methodical examination of the current literature on large data management approaches inside the Internet of Things framework. The study encompasses a broad spectrum of inquiry, ranging from fundamental notions to sophisticated approaches. The Internet of Things (IoT) is a powerful force that seeks to improve user experience and lifestyle. It incorporates several essential technologies including human-machine and machine-to machine communications, networking technologies, and sensor technologies. Fundamental to the success of the Internet of Things is the effective management of data transmitted through these technologies. This article explores the issues and challenges associated with data management in the context of the Internet of Things and examines different aspects of data, including its sources, collection processes, processing methods, and transmission devices. The article identifies and discusses problems arising from the need to process huge amounts of heterogeneous data in different systems. In relation to these issues, the logical and physical aspects of data management and communications networks are discussed. Additionally, the article takes an in-depth look at the data models used in IoT and explores data management, cleansing, and indexing techniques that take into account the unique characteristics of IoT data. The final sections of the paper comprehensively discuss the benefits and limitations associated with data management in IoT.