Published: 2025-12-01
Implementation of N8N Platform for IoT Sensor Monitoring: Real-time Analysis in Smart Farming
DOI: 10.35870/ijsecs.v5i3.5064
Downloads
Abstract
Smart farming has some limitations regarding the management of streaming data from IoT sensors. This is necessary to support real-time decision-making in areas with less infrastructure. This paper discusses the practical use of the N8N platform as a low-code/no-code workflow automation tool for monitoring IoT sensors in smart farming. A mixed-method approach was used, with a prototype design based on Research and Development. The system was built using IoT-A architecture, which includes the perception layer (soil moisture, temperature, humidity, pH, NPK, and ultrasonic sensors on ESP32), network layer (MQTT and HTTP), processing layer (N8N workflow for ingestion, validation, transformation, and decision logic), and application layer (dashboard and alerts). Testing was done in a controlled environment for 72 hours with scenarios such as normal operation, high load, network disruption sensor failure, and scalability up to 20 nodes. Results showed an average response time of 150–300 ms, throughput of up to 500 data points per minute end-to-end latency below 450 ms availability greater than 99% and processing accuracy between 98.7% and 99.2%. The system detected failures accurately and restored operations within an average of 45 seconds. These results proved that N8N can improve the efficiency and reliability of real-time monitoring as an adaptive solution for tropical agriculture in Indonesia. It also suggested long-term field trials together with AI integration for predictive forecasting to enhance scalability and practical adoption.
Keywords
Smart Farming ; IoT Monitoring ; N8N Workflow ; Real-time Sensor ; Precision Agriculture
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 5 No. 3 (2025)
-
Section: Articles
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/ijsecs.v5i3.5064
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Legito Legito
Faculty of Science and Technology, Computer Informatics, Universitas Tjut Nyak Dhien, Medan City, North Sumatra Province, Indonesia
Fitriyani Fitriyani
D3 Informatics Engineering, Faculty of Engineering, Universitas Jabal Ghafur, Pidie Regency, Aceh Province, Indonesia
Ferdy Firmansyah
Digital Business Study Program, Faculty of Economics and Business, Universitas Muhammadiyah Pontianak, Pontianak City, West Kalimantan Province, Indonesia
-
Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20(15), 4231. https://doi.org/10.3390/s20154231
-
Karunathilake, E. M. B. M., Le, A. T., Heo, S., Chung, Y. S., & Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8), 1593. https://doi.org/10.3390/agriculture13081593
-
Abi Hassan, A., Abdullahi, H. O., Ali, A. F., & Ahmed, M. H. (2024). Internet of things in agriculture: A systematic review of applications, benefits, and challenges. Journal of System and Management Sciences, 14(9), 67-80. https://doi.org/10.33168/JSMS.2024.0905
-
Nsoh, B., Katimbo, A., Guo, H., Heeren, D. M., Nakabuye, H. N., Qiao, X., Duan, J., Rudnick, D., & Kiraga, S. (2024). Internet of things-based automated solutions utilizing machine learning for smart and real-time irrigation management: A review. Sensors, 24(23), 7480. https://doi.org/10.3390/s24237480
-
Kour, V. P., & Arora, S. (2020). Recent developments of the internet of things in agriculture: A survey. IEEE Access, 8, 129924-129957. https://doi.org/10.1109/ACCESS.2020.3009298
-
Effah, E., Thiare, O., & Wyglinski, A. M. (2023). A tutorial on agricultural IoT: Fundamental concepts, architectures, routing, and optimization. IoT, 4(3), 265-318. https://doi.org/10.3390/iot4030014
-
Shaikh, F. K., Karim, S., Zeadally, S., & Nebhen, J. (2022). Recent trends in internet-of-things-enabled sensor technologies for smart agriculture. IEEE Internet of Things Journal, 9(23), 23583-23598. https://doi.org/10.1109/JIOT.2022.3210154
-
Rehman, A., Saba, T., Kashif, M., Fati, S. M., Bahaj, S. A., & Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12(1), 127. https://doi.org/10.3390/agronomy12010127
-
Thotho, D., & Macheso, P. S. (2024). Comprehensive survey on applications of internet of things, machine learning and artificial intelligence in precision agriculture. Tanzania Journal of Engineering and Technology, 42(4), 30-45.
-
Patidar, S., Kumar, N., & Jindal, R. (2024). IoT data stream handling, analysis, communication and security issues: A systematic survey. Wireless Personal Communications, 137, 2823-2872. https://doi.org/10.1007/s11277-024-11177-1
-
Chegini, H., Naha, R. K., Mahanti, A., & Thulasiraman, P. (2021). Process automation in an IoT–fog–cloud ecosystem: A survey and taxonomy. IoT, 2(1), 92-118. https://doi.org/10.3390/iot2010006
-
Compagnucci, I., Corradini, F., Fornari, F., Polini, A., Re, B., & Tiezzi, F. (2023). A systematic literature review on IoT-aware business process modeling views, requirements and notations. Software and Systems Modeling, 22(3), 969-1004. https://doi.org/10.1007/s10270-022-01049-2
-
Fortino, G., Guerrieri, A., Savaglio, C., & Spezzano, G. (2021). A review of internet of things platforms through the IoT-A reference architecture. In International Symposium on Intelligent and Distributed Computing (pp. 25-34). Springer. https://doi.org/10.1007/978-3-030-96627-0_3
-
Fortino, G., Savaglio, C., Spezzano, G., & Zhou, M. (2020). Internet of things as system of systems: A review of methodologies, frameworks, platforms, and tools. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 223-236. https://doi.org/10.1109/TSMC.2020.3042898
-
Folgado, F. J., Calderón, D., González, I., & Calderón, A. J. (2024). Review of Industry 4.0 from the perspective of automation and supervision systems: Definitions, architectures and recent trends. Electronics, 13(4), 782. https://doi.org/10.3390/electronics13040782
-
Marcu, I., Suciu, G., Bălăceanu, C., Vulpe, A., & Drăgulinescu, A. M. (2020). Arrowhead technology for digitalization and automation solution: Smart cities and smart agriculture. Sensors, 20(5), 1464. https://doi.org/10.3390/s20051464
-
Cisternas, I., Velásquez, I., Caro, A., & Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626. https://doi.org/10.1016/j.compag.2020.105626
-
Akhtar, M. N., Shaikh, A. J., Khan, A., Awais, H., Bakar, E. A., & Othman, A. R. (2021). Smart sensing with edge computing in precision agriculture for soil assessment and heavy metal monitoring: A review. Agriculture, 11(6), 475. https://doi.org/10.3390/agriculture11060475
-
Senoo, E. E. K., Anggraini, L., Kumi, J. A., Karolina, L. B., Akansah, E., Sulyman, H. A., Kyeremeh, F., & Aritsugi, M. (2024). IoT solutions with artificial intelligence technologies for precision agriculture: Definitions, applications, challenges, and opportunities. Electronics, 13(10), 1894. https://doi.org/10.3390/electronics13101894
-
Wali, M., Nasir, N., & Iqbal, T. (2025). Implementing workflow automation with N8N to enhance operational efficiency and performance in the Sharia Cooperative of Bank Indonesia, Aceh Province. Journal Digital Technology Trend, 4(1), 36-47. https://doi.org/10.56347/jdtt.v4i1.341
-
Jaya, R. (2022). Digitalisasi sistem traceability dan keberlanjutan agroindustri pangan: Telaah kritis literatur. Journal of Agroindustrial Technology/Jurnal Teknologi Industri Pertanian, 32(2), 146-163. https://doi.org/10.24961/j.tek.ind.pert.2022.32.2.146
-
Aswaldi, H. (2025). Penerapan teknologi internet of things (IoT) untuk monitoring kualitas udara dalam ruangan. Journal of Computer Science and Information Technology, 1(2), 39-45. https://doi.org/10.70716/jocsit.v1i2.255
-
Islamy, S., Gusti, W. R., & Zakarijah, M. (2024). Penerapan IoT pada prototipe pengukur tekanan darah non-invasive berbasis ESP8266. JST (Jurnal Sains Dan Teknologi), 12(3), 823-832. https://doi.org/10.23887/jstundiksha.v12i3.56356
-
Yusri, M., Maulita, Y., & Sembiring, H. (2024). Penerapan IoT dalam monitoring dan pengendalian kualitas air. Repeater: Publikasi Teknik Informatika Dan Jaringan, 2(4), 231-242. https://doi.org/10.62951/repeater.v2i4.250
-
Anshori, R. F., Saleh, M., & Aula, A. (2025). Smart farming system design based on long range and internet of things. Journal of Computer Science and Informatics Engineering, 4(2), 85-95. https://doi.org/10.55537/cosie.v4i2.1128
-
Saputra, D. P., Nugraha, M. B., Tampubolon, M., & Arhan, K. S. (2023). Design and development of soil monitoring system for precision farming on small-scale outdoor farm. In 2023 3rd International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) (pp. 212-217). IEEE.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.