Performance Evaluation of IoT-Based Industrial Automation Using Edge, Fog, and Cloud Architectures

The integration of Internet of Things (IoT) technology into industrial settings has significantly transformed various sectors by automating processes and enhancing decision support systems, thereby boosting productivity and efficiency in agricultural production. This study proposes a Stochastic Petr...

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Bibliographic Details
Published in:Journal of network and systems management 2025, Vol.33 (1), p.15
Main Authors: Barbosa, Vandirleya, Sabino, Arthur, Lima, Luiz Nelson, Brito, Carlos, Feitosa, Leonel, Pereira, Paulo, Maciel, Paulo, Nguyen, Tuan Anh, Silva, Francisco Airton
Format: Article
Language:English
Subjects:
Automation
Business metrics
Cloud computing
Communications Engineering
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Data analysis
Data collection
Data processing
Data transfer (computers)
Decision making
Decision support systems
Design of experiments
Edge computing
Efficiency
Fog
Humidity
Industry 4.0
Information Systems and Communication Service
Internet of Things
Manufacturing
Networks
Operations Research/Decision Theory
Parameters
Performance evaluation
Performance measurement
Petri nets
Productivity
Real time
Resource utilization
Sensitivity analysis
Sensors
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Summary:The integration of Internet of Things (IoT) technology into industrial settings has significantly transformed various sectors by automating processes and enhancing decision support systems, thereby boosting productivity and efficiency in agricultural production. This study proposes a Stochastic Petri Net (SPN) model to assess the performance of smart agricultural industrial facilities that integrate Edge, Fog, and Cloud Computing technologies. These technologies utilize sensors to monitor critical operational parameters such as temperature, humidity, and equipment status, enabling efficient data collection, processing, and analysis for informed decision-making and improved operational efficiency. Key challenges include managing large data volumes and ensuring timely data transfer between computing layers, impacting real-time poultry monitoring. The SPN model evaluates key performance metrics, including response time, resource utilization, discard probability, and throughput, while optimizing parameters to enhance system performance and further the application of IoT in industrial automation.
ISSN:1064-7570
1573-7705
DOI:10.1007/s10922-024-09893-x