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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68532

IoT-BASED DIGITAL FACTORY DASHBOARD

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68532

Volume:

10 |

10 |

Oct 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68532

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

ARUN RAJ R

ARUN RAJ R

ELECTRONICS AND COMMUNICATION

K.RAMAKRISHNAN COLLEGE OF TECHNOLOGY

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ARUNKUMAR K

ARUNKUMAR K

ELECTRONICS AND COMMUNICATION

K.RAMAKRISHNAN COLLEGE OF TECHNOLOGY

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ARUNMANI V

ARUNMANI V

ELECTRONICS AND COMMUNICATION

K.RAMAKRISHNAN COLLEGE OF TECHNOLOGY

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DHARSHAN R

DHARSHAN R

ELECTRONICS AND COMMUNICATION

K.RAMAKRISHNAN COLLEGE OF TECHNOLOGY

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Mentors

MANI P

MANI P

ELECTRONICS AND COMMUNICATION

K.RAMAKRISHNAN COLLEGE OF TECHNOLOGY

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Abstract

The IoT-Based Digital Factory Dashboard is a smart industrial monitoring system designed to improve manufacturing operations through real-time data collection and analysis. Traditional manufacturing systems often face challenges such as manual machine monitoring, unexpected equipment failures, production delays, excessive energy consumption, and limited visibility into machine performance. The proposed system uses Internet of Things (IoT) sensors and an IoT gateway to collect essential parameters such as machine status, production count, temperature, vibration, energy consumption, and machine downtime. The collected data is transmitted through communication technologies such as Wi-Fi and Message Queuing Telemetry Transport (MQTT) to a cloud-based database for storage and processing. A web-based dashboard provides centralized access to machine conditions, production performance, energy usage, and operational efficiency. The system is intended to identify abnormal machine conditions, monitor production activities, reduce unplanned downtime, and support efficient maintenance planning. The performance of the proposed system can be evaluated through experiments involving data transmission, monitoring response time, sensor accuracy, machine status detection, and dashboard reliability. Proper system configuration, database management, and experimental testing are essential to ensure reliable industrial monitoring.

KEYWORDS

IoT-Based Digital Factory Dashboard Internet of Things (IoT) Smart Manufacturing Industrial Monitoring MQTT Communication Cloud Database Machine Status Monitoring Predictive Maintenance Energy Monitoring Web-Based Dashboard.

Cite This Article

ARUN RAJ R, ARUNKUMAR K, ARUNMANI V, DHARSHAN R, MANI P, "IoT-BASED DIGITAL FACTORY DASHBOARD," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-6, Oct 2026. https://doi.org/10.55041/IJSREM68532

Full Article/Conclusion

The IoT-Based Digital Factory Dashboard is a proposed industrial monitoring solution that integrates IoT sensors, an IoT gateway, wireless communication, cloud-based data management, and a web-based dashboard. The system is designed to monitor machine status, production count, temperature, vibration, energy consumption, and machine downtime through a centralized platform. The web-based interface provides an accessible method for viewing manufacturing information and analyzing operational performance. Continuous collection of industrial data can support early identification of abnormal machine conditions, improved production visibility, energy monitoring, and maintenance planning. The modular architecture also allows additional machines and sensors to be integrated as manufacturing requirements change. The proposed system offers a foundation for digital manufacturing and Industry 4.0 applications, particularly in facilities that require centralized and scalable industrial monitoring. However, improvements in productivity, energy efficiency, and downtime reduction must be verified through experimental measurements and comparison with baseline manufacturing data. Future enhancements may include predictive maintenance using machine learning, integration with industrial automation systems, mobile notifications, advanced energy analytics, and improved cloud security. Further testing will be required to evaluate sensor accuracy, data transmission reliability, response time, and overall system performance.

References

[1] Agistya Anugrah, R., Adhidarma, F. B., Hutama, R. S., Maulana, V. K. R., & Winarno, Y. F. (2025). Arduino and IoT-based LPG gas leak detection systems: A focused review of sensors, architectures, and reliability issues. Journal of Automotive and Mechanical Applied Technology. [2] K. Ashton, “That ‘Internet of Things’ Thing,” RFID Journal, vol. 22, no. 7, pp. 97–114, 2009.  [3] Design and development of a gas leak and fire detection system using the Internet of Things (IoT) with Telegram notification. (2025). In Proceedings of the 5th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE. [4] S. Hoque, I. Ahmmed, A. Jobair, M. S. Hossain, M. R. Uddin, and M. Hasan, “Development of a Scalable Industrial Process Monitoring Sensor System for Large-Scale IoT Networks Using MQTT and Time Series Database,” in Proceedings of the IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES), 2024. [5] International Organization for Standardization, ISO 50001:2018—Energy Management Systems—Requirements with Guidance for Use, 2018. [6] H. Lasi, P. Fettke, H. G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Business & Information Systems Engineering, vol. 6, pp. 239–242, 2014. [7] J. Lee, B. Bagheri, and H.-A. Kao, “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015. [8] Mozilla Developer Network, JavaScript Documentation, MDN Web Docs. [9] National Institute of Standards and Technology (NIST), Guide to Operational Technology (OT) Security, NIST Special Publication 800-82, Revision 3, 2023. Available: [10] NXP Semiconductors, MFRC522: Standard Performance MIFARE and NTAG Frontend, Datasheet, Revision 3.9, 2016. [10] OWASP Foundation, Authentication Cheat Sheet, OWASP Cheat Sheet Series.

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Publication Date

10/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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