Analysis Of Ways To Reduce The Volume Of Data Transmitted In Internet Of Things Systems

Abstract

The rapid expansion of the Internet of Things (IoT) has resulted in an exponential increase in the volume of data transmitted across networks, posing significant challenges related to bandwidth limitations, energy consumption, latency, and overall network performance. This study analyzes contemporary methods aimed at reducing the volume of data transmitted within IoT systems while maintaining data accuracy, reliability, and system responsiveness. The research examines three main categories of approaches: data compression techniques, data reduction at the sensor level (including sampling optimization and event-driven transmission), and edge or fog computing–based processing. Comparative analysis demonstrates that local preprocessing and intelligent filtering significantly decrease communication overhead, leading to improved energy efficiency in resource-constrained IoT devices. The findings highlight that hybrid approaches—combining compression, adaptive sampling, and distributed processing—offer the most effective solutions for large-scale IoT deployments. The study concludes by emphasizing the importance of developing adaptive, context-aware algorithms to further minimize data traffic without compromising system quality and user experience.