Data Aggregation Techniques and Challenges in the Internet of Things: A Comprehensive Review

Abstract

The Internet of Things (IoT) has revolutionized the way people interact with their environment, generating massive amounts of data from interconnected devices. With the exponential growth of IoT devices, efficient data aggregation techniques are essential for extracting meaningful insights and reducing network traffic. This review paper aims to provide a comprehensive overview of data aggregation techniques in IoT, focusing on their methodologies, advantages, and challenges. The paper begins by discussing the fundamentals of IoT data aggregation. It then categorizes the data aggregation techniques into two main approaches: centralized and distributed. For each approach, various algorithms and protocols are explored, including clustering-based aggregation, tree-based aggregation, and centralized-based aggregation. Furthermore, the paper investigates the trade-offs involved in data aggregation, such as energy consumption, latency, and data accuracy. It examines the impact of different factors, such as data heterogeneity, and security considerations, on the choice of aggregation technique. Furthermore, in this paper researcher discussed the existing techniques, while highlights the emerging trends and future directions in IoT data aggregation. In this review paper we concluded by summarizing the key findings and highlighting the challenges that need to be addressed in the field of IoT data aggregation.