Multi-Device IoT Integration Using an API-Based Modular Architecture for Environmental Monitoring Systems
DOI:
10.33395/sinkron.v10i3.15970Keywords:
Internet of Things (IoT), Multi-Device, Modular Architecture, API, Environmental MonitoringAbstract
The Internet of Things (IoT) has increasingly played a significant role in the development of adaptive and real-time environmental monitoring systems. However, integrating multiple IoT devices remains challenging due to variations in data transmission intervals, communication protocols, and processing capabilities across devices. These differences often complicate system interoperability and data management within a unified monitoring platform. To address this issue, this study proposes an API-based modular architecture as a solution for integrating heterogeneous IoT devices in environmental monitoring systems. The proposed architecture separates core system functions into independent modules, including data acquisition, device management, and data visualization. The proposed architecture is evaluated through a multi-device environmental monitoring implementation configured with different logging intervals in order to assess communication performance and data consistency. The novelty of this study lies in its architectural approach to handling heterogeneous data transmission intervals in multi-device IoT environments using a modular API-based design. The experimental results indicate that the average communication latency is approximately 200ms, while the average daily data logging volume exceeds 3,500 entries per device. Furthermore, analysis of logging interval variations shows a time deviation of less than 3 seconds, which remains within the acceptable range for real-time environmental monitoring applications. The results demonstrate that the proposed architecture achieves success rate of over 97%, confirming the reliability of the proposed API-based modular architecture. Overall, the findings suggest that the modular API-driven architecture not only improves the flexibility and scalability of multi-device IoT integration but also maintains reliable data consistency and efficient communication performance.
Downloads
References
Alam, M., Islam, M. M., Nayan, N. M., & Uddin, J. (2025). An IoT Based Real-Time Environmental Monitoring System for Developing Areas. Journal of Advanced Research in Applied Sciences and Engineering Technology, 52(1), 106–121. https://doi.org/10.37934/araset.52.1.106121
Alam, T. (2021). Cloud-based iot applications and their roles in smart cities. Smart Cities, 4(3), 1196–1219. https://doi.org/10.3390/smartcities4030064
Alshammari, H. H. (2023). The internet of things healthcare monitoring system based on MQTT protocol. Alexandria Engineering Journal, 69, 275–287. https://doi.org/10.1016/j.aej.2023.01.065
Azhari, Nasution, T. I., Sinaga, S. H., & Sudiati. (2023). Design of Monitoring System Temperature And Humidity Using DHT22 Sensor and NRF24L01 Based on Arduino. Journal of Physics: Conference Series, 2421(1), 12018. https://doi.org/10.1088/1742-6596/2421/1/012018
Bukhsh, M., Abdullah, S., & Bajwa, I. S. (2021). A Decentralized Edge Computing Latency-Aware Task Management Method with High Availability for IoT Applications. IEEE Access, 9, 138994–139008. https://doi.org/10.1109/ACCESS.2021.3116717
Catovic, A., Kadusic, E., Ruland, C., Zivic, N., & Hadzajlic, N. (2022). Air pollution prediction and warning system using IoT and machine learning. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, 1–4. https://doi.org/10.1109/ICECCME55909.2022.9987957
Hercog, D., Lerher, T., Truntič, M., & Težak, O. (2023). Design and Implementation of ESP32-Based IoT Devices. Sensors, 23(15), 6739. https://doi.org/10.3390/s23156739
Hussein, N. M., Mohialden, Y. M., & Salman, S. A. (2024). Impact of IoT-Based Environmental Monitoring on Lab Safety and Sustainability. Babylonian Journal of Internet of Things, 2024, 16–26. https://doi.org/10.58496/bjiot/2024/003
Islam, M. M., Kashem, M. A., & Uddin, J. (2022). An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors. International Journal of Electrical and Computer Engineering, 12(1), 826–833. https://doi.org/10.11591/ijece.v12i1.pp826-833
Jiang, C., Fu, C., Zhao, Z., & Du, X. (2022). Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals. Procedia Computer Science, 210(C), 53–60. https://doi.org/10.1016/j.procs.2022.10.119
Martín-Baos, J. Á., Rodriguez-Benitez, L., García-Ródenas, R., & Liu, J. (2022). IoT based monitoring of air quality and traffic using regression analysis. Applied Soft Computing, 115, 108282. https://doi.org/10.1016/j.asoc.2021.108282
Maurya, R. (2021). Application of Restful APIs in IOT: A Review. International Journal for Research in Applied Science and Engineering Technology, 9(2), 145–151. https://doi.org/10.22214/ijraset.2021.33013
Megantoro, P., Pramudita, B. A., Vigneshwaran, P., Yurianta, A., & Winarno, H. A. (2021). Real-time monitoring system for weather and air pollutant measurement with html-based ui application. Bulletin of Electrical Engineering and Informatics, 10(3), 1669–1677. https://doi.org/10.11591/eei.v10i3.3030
Panduman, Y. Y. F., Funabiki, N., Fajrianti, E. D., Fang, S., & Sukaridhoto, S. (2024). A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform. Information (Switzerland), 15(3), 153. https://doi.org/10.3390/info15030153
Pereira, G. P., Chaari, M. Z., & Daroge, F. (2023). IoT-Enabled Smart Drip Irrigation System Using ESP32. Internet of Things, 4(3), 221–243. https://doi.org/10.3390/iot4030012
Polymeni, S., Athanasakis, E., Spanos, G., Votis, K., & Tzovaras, D. (2022). IoT-based prediction models in the environmental context: A systematic Literature Review. Internet of Things (Netherlands), 20, 100612. https://doi.org/10.1016/j.iot.2022.100612
Reddy, C. D., & Kumar, D. K. (2026). Iot-Based Multi-Sensor Data Fusion for Precision Crop Yield Optimization Using Arduino,Esp32,and Webcam Integration. Journal of Engineering and Technology for Industrial Applications, 12(57), 602–611. https://doi.org/10.5935/jetia.v12i57.2893
Sartaj, H., Ali, S., & Gjøby, J. M. (2025). REST API Testing in DevOps: A Study on an Evolving Healthcare IoT Application. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3765744
Sefati, S. S., & Halunga, S. (2023). Ultra-reliability and low-latency communications on the internet of things based on 5G network: Literature review, classification, and future research view. Transactions on Emerging Telecommunications Technologies, 34(6), e4770. https://doi.org/10.1002/ett.4770
Shukla, S., Hassan, M. F., Tran, D. C., Akbar, R., Paputungan, I. V., & Khan, M. K. (2023). Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR). Cluster Computing, 26(5), 2657–2680. https://doi.org/10.1007/s10586-021-03279-3
Singh, P., Saman Azari, M., Vitale, F., Flammini, F., Mazzocca, N., Caporuscio, M., & Thornadtsson, J. (2022). Using log analytics and process mining to enable self-healing in the Internet of Things. Environment Systems and Decisions, 42(2), 234–250. https://doi.org/10.1007/s10669-022-09859-x
Singh, R. R., Banerjee, S., Manikandan, R., Kotecha, K., Indragandhi, V., & Vairavasundaram, S. (2022). Intelligent IoT Wind Emulation System Based on Real-Time Data Fetching Approach. IEEE Access, 10, 78253–78267. https://doi.org/10.1109/ACCESS.2022.3193774
Wahyuni Sabran, F., Zalfiana Rusfian, E., Ilmu Administrasi dan Kebijakan Bisnis, M., & Ilmu Administrasi, F. (2023). Penggunaan Internet of Things pada eFishery untuk keberlanjutan Akuakultur di Indonesia. Innovative: Journal Of Social Science Research, 3(2), 8142–8156. https://j-innovative.org/index.php/Innovative/article/view/1359
Wibawa, I. M. S., & Putra, I. K. (2022). Design of air temperature and humidity measurement based on Arduino ATmega 328P with DHT22 sensor. International Journal of Physical Sciences and Engineering, 6(1), 9–17. https://doi.org/10.53730/ijpse.v6n1.3065
Yan, K., Zhou, X., & Yang, B. (2023). Editorial: AI and IoT applications of smart buildings and smart environment design, construction and maintenance. Building and Environment, 229. https://doi.org/10.1016/j.buildenv.2022.109968
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2026 Andi Marwan Elhanafi, Dedy Irwan, Kissi Lola Armedia Br Siregar

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






















Moraref
PKP Index
Indonesia OneSearch
OCLC Worldcat
Index Copernicus
Scilit
