A Review of Data Aggregation Algorithms for Enhancing IoT Performance

Abstract

With the exponential growth of the Internet of Things, efficient data aggregation is necessary to tackle the issues of network congestion, energy consumption, and latency. This paper presents an adaptive data aggregation algorithm that applies clustering, in-network processing, and optimized routing. The proposed algorithm outperforms traditional methods such as LEACH and PEGASIS with 50 ms latency, 0.5 J/node energy efficiency, and 350 kbps throughput. Key features include dynamic clustering, energy optimization, and real-time adaptation, which offer high scalability and are fit for use in smart cities, healthcare, and industrial IoT. However, future challenges like interoperability and security will still be taken into account on the ground. This paper advances scalable, efficient, and secure IoT systems towards technological advancement.