Integration of Wifi and Mobile Communications in Fire Early Warning Data Sending in Home Based on The Internet of Things

Fadli Tamrin; Asrul Asrul; Arman Arman

doi:10.56134/jst.v1i1.1

Authors

Fadli Tamrin STMIK AKBA
Asrul Asrul STMIK AKBA
Arman Arman STMIK AKBA

Keywords:

Internet of Things, Microcontroller, Blynk, Arduino

https://doi.org/10.56134/jst.v1i1.1

Abstract

House fires are one of the most frequent disasters in dry climates, with impacts that not only cause material losses but also cause loss of life. This research aims to develop a fire early warning system that integrates WiFi and cellular communication technology to provide a fast and accurate response. The methods used include black-box testing and hardware testing using Arduino Mega2560, ESP32, DHT-22, MQ-2, LDR Array, SIM900, Mini DC Pump, and Mini DC Fan. The test results show that the system can operate both online and offline, where each sensor is able to detect indications of fire, gas, and smoke in real time. Furthermore, this device is designed not only for household applications but also has the potential for use on an industrial scale. This innovation makes an important contribution to disaster risk mitigation efforts by increasing community preparedness, accelerating the evacuation process, and minimizing losses. Thus, the developed system serves as a practical and applicable solution to create a safer and more resilient environment against the threat of fire.

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References

Anik, S. M. H., Gao, X., Meng, N., Agee, P. R., & McCoy, A. P. (2022). A cost-effective, scalable, and portable IoT data infrastructure for indoor environment sensing. Journal of Building Engineering, 104027.

Asmawati, A., Putra, F. J. E., & Richie, L. (2019). Control Led Through Internet Based On Nodemcu With Blynk Application. Aptisi Transactions on Technopreneurship (ATT), 1(2), 170–179.

Durani, H., Sheth, M., Vaghasia, M., & Kotech, S. (2018). Smart automated home application using IoT with Blynk app. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 393–397.

Forlizzi, J., DiSalvo, C., & Gemperle, F. (2004). Assistive robotics and an ecology of elders living independently in their homes. Human--Computer Interaction, 19(1–2), 25–59.

Groce, A., Holzmann, G., & Joshi, R. (2007). Randomized differential testing as a prelude to formal verification. 29th International Conference on Software Engineering (ICSE’07), 621–631.

Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112–121.

Jumaa, N. K., Abdulkhaleq, Y. M., Nadhim, M. A., & Abbas, T. A. (2022). IoT Based Gas Leakage Detection and Alarming System using Blynk platforms.

Koehler, R. (2021). Design of Control Logic and the Human-Machine Interface for a Demonstration Plant Growth Chamber Implemented on a Programmable Logic Controller.

Koestoer, R. A., Pancasaputra, N., Roihan, I., & Harinaldi. (2019). A simple calibration methods of relative humidity sensor DHT22 for tropical climates based on Arduino data acquisition system. AIP Conference Proceedings, 2062(1), 20009.

Muhammad, K., Ahmad, J., & Baik, S. W. (2018). Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing, 288, 30–42.

Tanwar, S., Parekh, K., & Evans, R. (2020). Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications, 50, 102407.

YADAV, S., MANOHAR, M., & others. (2020). A Smart System For Monitoring Intravenous Drip Bottles In Healthcare.

Zamli, K. Z., Klaib, M. F. J., Younis, M. I., Isa, N. A. M., & Abdullah, R. (2011). Design and implementation of a t-way test data generation strategy with automated execution tool support. Information Sciences, 181(9), 1741–1758.