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Data Communication Based on MQTT in a Polymer Extrusion Process

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Published on: July 15, 2022

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems.

Bo Jin¹, Fazlullah Khan², Ryan Alturki³

¹School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

ISA Transactions

|June 28, 2022

Summary

This study introduces a computational intelligence mechanism for autonomous systems using the Internet of Things (IoT). The system enhances decision-making accuracy by predicting device criticality, improving data transmission reliability and traffic distribution.

Keywords:

Computational intelligence Decision support system Intelligent prediction Intelligent sensors Internet of Things IoT-based autonomous systems

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Area of Science:

Computer Science
Artificial Intelligence
Network Engineering

Background:

Autonomous systems and the Internet of Things (IoT) are increasingly integrated into daily life.
Accurate decision-making is critical for the performance of autonomous systems.
Developing fully autonomous systems remains an open research challenge.

Purpose of the Study:

To propose a computational intelligence-based prediction and communication mechanism for autonomous systems.
To enhance the reliability and accuracy of decision support systems within IoT networks.
To ensure uniform traffic distribution and optimize routing paths.

Main Methods:

Utilizing energy gauge (EG) devices to collect data on neighboring IoT devices.
Implementing a computational intelligent decision support system within each EG device to predict device criticality.
Broadcasting information about potential relaying devices to adjust routing paths dynamically.
Defining device criticality based on residual energy and received signal strength indicator (RSSI) thresholds.

Main Results:

The proposed mechanism accurately predicts the criticality of neighboring devices, particularly relays.
Data transmission is routed through reliable paths, avoiding critical devices.
Uniform traffic distribution is achieved across the autonomous system.
Enhanced accuracy in autonomous system operations compared to existing approaches.

Conclusions:

The computational intelligence-enabled prediction mechanism significantly improves the accuracy and reliability of autonomous systems.
The proposed approach effectively manages IoT network resources for better performance.
This mechanism offers a robust solution for enhancing decision-making in complex autonomous environments.