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

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 pe...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 12; p. 4967
Main Authors Zhou, Zixiang, Zhou, Lei, Chen, Zhiguo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2024
Subjects
Analysis
Apache Kafka
Batch processing
Communication
Data analysis
Data processing
Data transmission
Decision making
distributed stream processing
Efficiency
Environmental monitoring
Industry 4.0
Information management
Internet of Things
IoT
Middleware
monitoring
Monitoring systems
real-time processing
Sensors
New Jersey
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14124967