A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka

Abstract

Strict air pressure control is paramount in industries such as petroleum, chemicals, transportation, and mining to ensure production safety and to improve operational efficiency. In these fields, accurate real-time air pressure monitoring is critical to optimize operations and ensure facility and personnel safety. Although current Internet of Things air pressure monitoring systems enable users to make decisions based on objective data, existing approaches are limited by long response times, low efficiency, and inadequate preprocessing. Additionally, the exponential increase in data volumes creates the risk of server downtime. To address these challenges, this paper proposes a novel real-time air pressure monitoring scheme that uses Arduino microcontrollers in conjunction with GPRS network communication. It also uses Apache Kafka to construct a multi-server cluster for high-performance message processing. Furthermore, data are backed up by configuring multiple replications, which safeguards against data loss during server failures. The scheme also includes an intuitive and user-friendly visualization interface for data analysis and subsequent decision making. The experimental results demonstrate that this approach offers high throughput and timely responsiveness, providing a more reliable option for real-time gathering, analysis, and storage of massive data.

Keywords

• IoT
• real-time processing
• distributed stream processing
• Apache Kafka
• monitoring