Development Of Sensor Network System For Digital Transformation Transfer Laboratory


Abstract views: 139 / PDF downloads: 36

Authors

  • Gökçe İye Izmir Katip Celebi University
  • Merih Palandöken Izmir Katip Celebi University

Keywords:

IoT, MQTT, Industry 4.0, Sensor, Digital Transformation

Abstract

Along with technological innovations, the internet of things, artificial intelligence and big data,
as a result of all these, the "digital age" or "digitalization" processes we have discussed have entered our
lives. These concepts emerged in the Industry 4.0 period, which represents the fourth phase of the
industrial revolution and is still experienced today. Internet of things (IoT) refers to all systems that can
transfer data over a network. In the world of the Internet of Things, the role of human-to-human
commands and even human-computer interaction is minimized. The paper aims to provide both cost and
high efficiency by using IoT and MQTT. The application was designed with object-oriented
programming languages such as Python and C#. In the Digital Transformation Transfer Laboratory, data
recorded and transferred through sensors are analyzed. The focus of the paper is on processing data from
DHT11 and HC-SR04 sensors, analyzing this data intelligently and monitoring it in different applications.
The use of sensor networks provides timely access and convenience in accessing information. With the
development of the systems to be designed in the paper, it is aimed to control the smart system in an
easier and more visual way and to develop a sensor network system for a Digital Transformation Transfer
Laboratory suitable for Industry 4.0 requirements with high original value. In this context, the paper aims
to develop a sensor network system for the Digital Transformation Transfer Laboratory that complies
with the high value requirements of Industry 4.0 and makes the control of the smart system easier and
more visually accessible.

Downloads

Download data is not yet available.

Author Biographies

Gökçe İye, Izmir Katip Celebi University

Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Turkey

Merih Palandöken, Izmir Katip Celebi University

Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Turkey

References

David R. Sjödin, Vinit Parida, Markus Leksell & Aleksandar Petrovic (2018) Smart Factory Implementation and Process Innovation, Research-Technology Management, 61:5, 22-31, DOI: 10.1080/08956308.2018.1471277.

Philipp Osterrieder, Lukas Budde, Thomas Friedli,The smart factory as a key construct of industry 4.0: A systematic literature review,International Journal of Production Economics,Volume 221,2020,107476,ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2019.08.011 .

The Internet of Things: Foundational ethical issues, Fritz Allhoff, Adam Henschke,

https://doi.org/10.1016/j.iot.2018.08.005 .

F. Allhoff, G Nicholas, Evans, and Adam Henschke, The Routledge Handbook of Ethics and War: Just War Theory in the Twenty-First Century, Routledge, London (2013).

R. Karunamoorthi, Mohit Tiwari, Tripti Tiwari, Radha Kuruva, Arvind K. Sharma, M. Jemimah Carmichael, T.C. Manjunath, Design and development of IoT based home computerization using Raspberry pi, Materials Today: Proceedings, 2020, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2020.10.673 .

A. A. Osuwa, E. B. Ekhoragbon and L. T. Fat, "Application of artificial intelligence in Internet of Things," 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), 2017, pp. 169-173, doi: 10.1109/CICN.2017.8319379.

R. Karunamoorthi, Mohit Tiwari, Tripti Tiwari, Radha Kuruva, Arvind K. Sharma, M. Jemimah Carmichael, T.C. Manjunath, Design and development of IoT based home computerization using Raspberry pi, Materials Today: Proceedings, 2020, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2020.10.673

El Naqa I., Murphy M.J. (2015) What Is Machine Learning?. In: El Naqa I., Li R., Murphy M. (eds) Machine Learning in Radiation Oncology. Springer, Cham, https://doi.org/10.1007/978-3-319-18305-3_1

Frederik Wedel, Steffen Marx, Application of machine learning methods on real bridge monitoring data, Engineering Structures, Volume 250, 2022, 113365, ISSN 0141-0296, https://doi.org/10.1016/j.engstruct.2021.113365

L. Paradis and Q. Han, “A survey of fault management in wireless sen-sor networks,” Journal of Network and Systems Management, vol. 15,no. 2, pp. 171–190, 2007

Downloads

Published

2024-05-27

How to Cite

İye, G., & Palandöken, M. (2024). Development Of Sensor Network System For Digital Transformation Transfer Laboratory . International Journal of Advanced Natural Sciences and Engineering Researches, 8(4), 259–263. Retrieved from https://as-proceeding.com/index.php/ijanser/article/view/1843

Issue

Section

Articles