IoT-Based Environmental Monitoring: Enhancing Energy Efficiency Through Edge Computing Integration
TL;DR
This paper examines how edge computing offloads tasks such as data pre-processing, anomaly detection, and local decision-making, thus minimizing the need for continuous data transmission to the cloud, thus minimizing the need for continuous data transmission to the cloud.
Abstract
Environmental monitoring is essential for tracking parameters such as air quality, temperature, humidity, and pollution levels in real-time. However, the scale and energy demands of such IoT-based systems are often challenging. This paper proposes a novel approach to enhance the energy efficiency of IoT-based environmental monitoring systems by integrating edge computing. By processing data closer to the source (at the edge of the network), the system reduces the energy consumption of communication and central processing, leading to significant energy savings. We examine how edge computing offloads tasks such as data pre-processing, anomaly detection, and local decision-making, thus minimizing the need for continuous data transmission to the cloud. The performance of this system is evaluated through simulation experiments on various environmental monitoring scenarios, demonstrating that edge computing integration significantly improves energy efficiency without compromising data accuracy or real-time monitoring capabilities. The proposed approach is compared with traditional cloud-based IoT systems and the results highlight the benefits of edge computing in energy-critical applications.