Research Article
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Application of Monitoring and Controlling of Water Storage Tanks, Pumps, and pH Value in Industrial Areas Based on Industrial Internet of Things

Year 2025, , 70 - 80, 30.05.2025

Hayati Mamur , Harun Şentürk , Mohammad Ruhul Amin Bhuiyan

https://doi.org/10.35193/bseufbd.1446441

Abstract

In this study, the control and monitoring of 110 kW water motors in 4 water wells at different distances from each other, the monitoring of 500-ton water tanks and additionally the monitoring pH value of the rainwater log of a chemical factory have been carried out. In the application, 9 Raspberry Pi 3 embedded system boards based on the Industrial Internet of Things (IIoT) have been utilized. The sensor data have been taken with the developed analog digital converters (ADCs) and collected in the open-source InfluxDB database with embedded system cards. These data have been visualized in a computer setup the open-source supervisory control and data acquisition system (SCADA) Grafana software. Thus, the IIoT application of a large system has been fulfilled cost-effectively by using Raspberry Pi 3 embedded system boards, open-source InfluxDB and Grafana software.

Keywords

IIoT Raspberry Pi InfluxDB Grafana Embedded System

References

  • Peter, O., Pradhan, A., & Mbohwa, C. (2023). Industrial internet of things (IIoT): opportunities, challenges, and requirements in manufacturing businesses in emerging economies. Procedia Computer Science, 217, 856-865.
  • Khalil, R. A., Saeed, N., Masood, M., Fard, Y. M., Alouini, M. S., & Al-Naffouri, T. Y. (2021). Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications. IEEE Internet of Things Journal, 8(14), 11016-11040.
  • Ahmad, T., & Zhang, D. (2021). Using the internet of things in smart energy systems and networks. Sustainable Cities and Society, 68, 102783.
  • Kun, X., Wang, Z., Zhou, Z., & Qi, W. (2021). Design of industrial internet of things system based on machine learning and artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 40(2), 2601-2611.
  • Zayed, S. M., Attiya, G., El-Sayed, A., Sayed, A., & Hemdan, E. E. D. (2023). An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems. International Journal of Computational Intelligence Systems, 16(1), 69.
  • Hafeez, T., Xu, L., & Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access, 9, 49355-49371.
  • Valeske, B., Osman, A., Römer, F., & Tschuncky, R. (2020). Next generation NDE sensor systems as IIoT elements of industry 4.0. Research in Nondestructive Evaluation, 31(5-6), 340-369.
  • Cakir, M., Guvenc, M. A., & Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers & Industrial Engineering, 151, 106948.
  • Almadani, B., & Mostafa, S. M. (2021). IIoT based multimodal communication model for agriculture and agro-industries. IEEE Access, 9, 10070-10088.
  • Verdejo Espinosa, Á., Lopez, J. L., Mata Mata, F., & Estevez, M. E. (2021). Application of IoT in healthcare: keys to implementation of the sustainable development goals. Sensors, 21(7), 2330.
  • Priyanka, E. B., Maheswari, C., & Thangavel, S. (2021). A smart‐integrated IoT module for intelligent transportation in oil industry. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(3), e2731.
  • Al_Janabi, S. (2020). Smart system to create an optimal higher education environment using IDA and IOTs. International Journal of Computers and Applications, 42(3), 244-259.
  • Ahmed, S. F., Alam, M. S. B., Hoque, M., Lameesa, A., Afrin, S., Farah, T., Kabir, M., Shafiullah, A., & Muyeen, S. M. (2023). Industrial Internet of Things enabled technologies, challenges, and future directions. Computers and Electrical Engineering, 110, 108847.
  • Ouafiq, E. M., Saadane, R., Chehri, A., & Jeon, S. (2022). AI-based modeling and data-driven evaluation for smart farming-oriented big data architecture using IoT with energy harvesting capabilities, Sustainable Energy Technologies and Assessments, 52, Part A, 102093.
  • Chelliah, B. J., Latchoumi, T. P., & Senthilselvi, A. (2024). Analysis of demand forecasting of agriculture using machine learning algorithm. Environment, Development and Sustainability, 26(1), 1731-1747.
  • Manikandan, R., Ranganathan, G., & Bindhu, V. (2023). Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions. Wireless Personal Communications, 128(3), 1715-1732.
  • Devi, N., Sarma, K. K., & Laskar, S. (2023). Design of an intelligent bean cultivation approach using computer vision, IoT and spatio-temporal deep learning structures. Ecological Informatics, 75, 102044.
  • Rahman, M. Z., Akbar, M. A., Leiva, V., Tahir, A., Riaz, M. T., & Martin-Barreiro, C. (2023). An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients. Computers in Biology and Medicine, 154, 106583.
  • Mukati, N., Namdev, N., Dilip, R., Hemalatha, N., Dhiman, V., & Sahu, B. (2023). Healthcare assistance to COVID-19 patient using internet of things (IoT) enabled technologies. Materials Today: Proceedings, 80, 3777-3781.
  • Mao, J., Zhou, P., Wang, X., Yao, H., Liang, L., Zhao, Y., ... & Zheng, H. (2023). A health monitoring system based on flexible triboelectric sensors for intelligence medical internet of things and its applications in virtual reality. Nano Energy, 118, 108984.
  • Jhuang, Y. Y., Yan, Y. H., & Horng, G. J. (2023). GDPR Personal Privacy Security Mechanism for Smart Home System. Electronics, 12(4), 831.
  • Al-Turjman, F., Salama, R., & Altrjman, C. (2023). Overview of IoT Solutions for Sustainable Transportation Systems. NEU Journal for Artificial Intelligence and Internet of Things, 2(3).
  • Memika, T., & Polat, T. K. (2023). Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy. Intelligent Automation & Soft Computing, 35(3).
  • Zulfiqar, H., Ul Haque, H. M., Tariq, F., & Khan, R. M. (2023). A survey on smart parking systems in urban cities. Concurrency and Computation: Practice and Experience, 35(15), e6511.
  • Mohammed, K., Abdelhafid, M., Kamal, K., Ismail, N., & Ilias, A. (2023). Intelligent driver monitoring system: An Internet of Things-based system for tracking and identifying the driving behavior. Computer Standards & Interfaces, 84, 103704.
  • Yang, P., Zhang, L., & Tao, G. (2023). Smart chemical industry parks in China: Current status, challenges, and pathways for future sustainable development. Journal of Loss Prevention in the Process Industries, 105105.
  • Siswanto, B., Kerta, J. M., Ranny, & Nugroho, D. D. (2020). automatic detection of carbon dioxide concentration using IoT. International Journal of Engineering and Advanced Technology (IJEAT), 9(4), 2474-2477.
  • Magadán, L., Suárez, F. J., Granda, J. C., & García, D. F. (2023). Low-cost industrial IoT system for wireless monitoring of electric motors condition. Mobile Networks and Applications, 28(1), 97-106.
  • Hosny, K. M., Magdi, A., Salah, A., El-Komy, O., & Lashin, N. A. (2023). Internet of things applications using Raspberry-Pi: a survey. International Journal of Electrical & Computer Engineering, 13(1), 90-910.
  • Abas, S. U., Duran, F., & Tekerek, A. (2023). A Raspberry Pi based blockchain application on IoT security. Expert Systems with Applications, 229, 120486.
  • Noprianto, N., Wijayaningrum, V. N., & Wakhidah, R. (2023). Monitoring development board based on InfluxDB and Grafana. Telematika: Jurnal Informatika dan Teknologi Informasi, 20(1), 81-90.
  • Razak, S. F. A., Wee, Y. J., Yogarayan, S., Ismail, S. N. M. S., & Abdullah, M. F. A. (2024). Real-time monitoring tool for heart rate and oxygen saturation in young adults. Bulletin of Electrical Engineering and Informatics, 13(2), 1307-1315.
  • Bolanowski, M., Paszkiewicz, A., Żabiński, T., Piecuch, G., Salach, M., & Tomecki, K. (2023). System Architecture for Diagnostics and Supervision of Industrial Equipment and Processes in an IoE Device Environment. Electronics, 12(24), 4935.

Endüstriyel Alanlarda Su Depolarının, Pompaların ve pH Değerinin İzlenmesi ve Kontrol Edilmesine Yönelik Endüstriyel Nesnelerin İnterneti Tabanlı Uygulama

Year 2025, , 70 - 80, 30.05.2025

Hayati Mamur , Harun Şentürk , Mohammad Ruhul Amin Bhuiyan

https://doi.org/10.35193/bseufbd.1446441

Abstract

Bu çalışmada, birbirinden farklı mesafelerdeki 4 adet su kuyusunda bulunan 110 kW'lık su motorlarının kontrolü ve izlenmesi, 500 tonluk su depolarının izlenmesi ve ayrıca bir kimya fabrikasının yağmur suyu giderinin pH değerinin izlenmesi yapılmıştır. Uygulamada, Endüstriyel Nesnelerin İnterneti (IIoT) tabanlı 9 adet Raspberry Pi 3 gömülü sistem kartı kullanılmıştır. Farklı alanlardaki sensör verileri, geliştirilen analog dijital dönüştürücüler (ADC) ile alınmış ve gömülü sistem kartları ile açık kaynaklı InfluxDB veri tabanında toplanmıştır. Bu veriler, açık kaynaklı yönetici kontrol ve veri toplama sistemi (SCADA) Grafana yazılımı olan bir bilgisayar kurulumunda görselleştirilmiştir. Böylece Raspberry Pi 3 gömülü sistem kartları, açık kaynaklı InfluxDB ve Grafana yazılımı kullanılarak büyük bir sistemin IIoT uygulaması maliyet etkin bir şekilde gerçekleştirilmiştir.

Keywords

IIoT Raspberry Pi InfluxDB Grafana Gömülü Sistem

References

  • Peter, O., Pradhan, A., & Mbohwa, C. (2023). Industrial internet of things (IIoT): opportunities, challenges, and requirements in manufacturing businesses in emerging economies. Procedia Computer Science, 217, 856-865.
  • Khalil, R. A., Saeed, N., Masood, M., Fard, Y. M., Alouini, M. S., & Al-Naffouri, T. Y. (2021). Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications. IEEE Internet of Things Journal, 8(14), 11016-11040.
  • Ahmad, T., & Zhang, D. (2021). Using the internet of things in smart energy systems and networks. Sustainable Cities and Society, 68, 102783.
  • Kun, X., Wang, Z., Zhou, Z., & Qi, W. (2021). Design of industrial internet of things system based on machine learning and artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 40(2), 2601-2611.
  • Zayed, S. M., Attiya, G., El-Sayed, A., Sayed, A., & Hemdan, E. E. D. (2023). An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems. International Journal of Computational Intelligence Systems, 16(1), 69.
  • Hafeez, T., Xu, L., & Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access, 9, 49355-49371.
  • Valeske, B., Osman, A., Römer, F., & Tschuncky, R. (2020). Next generation NDE sensor systems as IIoT elements of industry 4.0. Research in Nondestructive Evaluation, 31(5-6), 340-369.
  • Cakir, M., Guvenc, M. A., & Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers & Industrial Engineering, 151, 106948.
  • Almadani, B., & Mostafa, S. M. (2021). IIoT based multimodal communication model for agriculture and agro-industries. IEEE Access, 9, 10070-10088.
  • Verdejo Espinosa, Á., Lopez, J. L., Mata Mata, F., & Estevez, M. E. (2021). Application of IoT in healthcare: keys to implementation of the sustainable development goals. Sensors, 21(7), 2330.
  • Priyanka, E. B., Maheswari, C., & Thangavel, S. (2021). A smart‐integrated IoT module for intelligent transportation in oil industry. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(3), e2731.
  • Al_Janabi, S. (2020). Smart system to create an optimal higher education environment using IDA and IOTs. International Journal of Computers and Applications, 42(3), 244-259.
  • Ahmed, S. F., Alam, M. S. B., Hoque, M., Lameesa, A., Afrin, S., Farah, T., Kabir, M., Shafiullah, A., & Muyeen, S. M. (2023). Industrial Internet of Things enabled technologies, challenges, and future directions. Computers and Electrical Engineering, 110, 108847.
  • Ouafiq, E. M., Saadane, R., Chehri, A., & Jeon, S. (2022). AI-based modeling and data-driven evaluation for smart farming-oriented big data architecture using IoT with energy harvesting capabilities, Sustainable Energy Technologies and Assessments, 52, Part A, 102093.
  • Chelliah, B. J., Latchoumi, T. P., & Senthilselvi, A. (2024). Analysis of demand forecasting of agriculture using machine learning algorithm. Environment, Development and Sustainability, 26(1), 1731-1747.
  • Manikandan, R., Ranganathan, G., & Bindhu, V. (2023). Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions. Wireless Personal Communications, 128(3), 1715-1732.
  • Devi, N., Sarma, K. K., & Laskar, S. (2023). Design of an intelligent bean cultivation approach using computer vision, IoT and spatio-temporal deep learning structures. Ecological Informatics, 75, 102044.
  • Rahman, M. Z., Akbar, M. A., Leiva, V., Tahir, A., Riaz, M. T., & Martin-Barreiro, C. (2023). An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients. Computers in Biology and Medicine, 154, 106583.
  • Mukati, N., Namdev, N., Dilip, R., Hemalatha, N., Dhiman, V., & Sahu, B. (2023). Healthcare assistance to COVID-19 patient using internet of things (IoT) enabled technologies. Materials Today: Proceedings, 80, 3777-3781.
  • Mao, J., Zhou, P., Wang, X., Yao, H., Liang, L., Zhao, Y., ... & Zheng, H. (2023). A health monitoring system based on flexible triboelectric sensors for intelligence medical internet of things and its applications in virtual reality. Nano Energy, 118, 108984.
  • Jhuang, Y. Y., Yan, Y. H., & Horng, G. J. (2023). GDPR Personal Privacy Security Mechanism for Smart Home System. Electronics, 12(4), 831.
  • Al-Turjman, F., Salama, R., & Altrjman, C. (2023). Overview of IoT Solutions for Sustainable Transportation Systems. NEU Journal for Artificial Intelligence and Internet of Things, 2(3).
  • Memika, T., & Polat, T. K. (2023). Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy. Intelligent Automation & Soft Computing, 35(3).
  • Zulfiqar, H., Ul Haque, H. M., Tariq, F., & Khan, R. M. (2023). A survey on smart parking systems in urban cities. Concurrency and Computation: Practice and Experience, 35(15), e6511.
  • Mohammed, K., Abdelhafid, M., Kamal, K., Ismail, N., & Ilias, A. (2023). Intelligent driver monitoring system: An Internet of Things-based system for tracking and identifying the driving behavior. Computer Standards & Interfaces, 84, 103704.
  • Yang, P., Zhang, L., & Tao, G. (2023). Smart chemical industry parks in China: Current status, challenges, and pathways for future sustainable development. Journal of Loss Prevention in the Process Industries, 105105.
  • Siswanto, B., Kerta, J. M., Ranny, & Nugroho, D. D. (2020). automatic detection of carbon dioxide concentration using IoT. International Journal of Engineering and Advanced Technology (IJEAT), 9(4), 2474-2477.
  • Magadán, L., Suárez, F. J., Granda, J. C., & García, D. F. (2023). Low-cost industrial IoT system for wireless monitoring of electric motors condition. Mobile Networks and Applications, 28(1), 97-106.
  • Hosny, K. M., Magdi, A., Salah, A., El-Komy, O., & Lashin, N. A. (2023). Internet of things applications using Raspberry-Pi: a survey. International Journal of Electrical & Computer Engineering, 13(1), 90-910.
  • Abas, S. U., Duran, F., & Tekerek, A. (2023). A Raspberry Pi based blockchain application on IoT security. Expert Systems with Applications, 229, 120486.
  • Noprianto, N., Wijayaningrum, V. N., & Wakhidah, R. (2023). Monitoring development board based on InfluxDB and Grafana. Telematika: Jurnal Informatika dan Teknologi Informasi, 20(1), 81-90.
  • Razak, S. F. A., Wee, Y. J., Yogarayan, S., Ismail, S. N. M. S., & Abdullah, M. F. A. (2024). Real-time monitoring tool for heart rate and oxygen saturation in young adults. Bulletin of Electrical Engineering and Informatics, 13(2), 1307-1315.
  • Bolanowski, M., Paszkiewicz, A., Żabiński, T., Piecuch, G., Salach, M., & Tomecki, K. (2023). System Architecture for Diagnostics and Supervision of Industrial Equipment and Processes in an IoE Device Environment. Electronics, 12(24), 4935.
There are 33 citations in total.

Details

Primary Language English
Subjects Embedded Systems
Journal Section Articles
Authors

Hayati Mamur 0000-0001-7555-5826

Harun Şentürk 0000-0001-5680-4057

Mohammad Ruhul Amin Bhuiyan 0000-0001-7335-4158

Publication Date May 30, 2025
Submission Date March 3, 2024
Acceptance Date May 8, 2024
Published in Issue Year 2025

Cite

APA Mamur, H., Şentürk, H., & Bhuiyan, M. R. A. (2025). Application of Monitoring and Controlling of Water Storage Tanks, Pumps, and pH Value in Industrial Areas Based on Industrial Internet of Things. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 12(1), 70-80. https://doi.org/10.35193/bseufbd.1446441