Rancang Bangun Alat Bantu Mitigasi Bencana Dalam Mendeteksi Karhutla di Kalimantan Barat Menggunakan Iot Jaringan Lora

Nurman  Hariyanto; Denny  Primanda

doi:10.46229/jifotech.v5i1.984

Authors

Nurman Hariyanto Universitas Nahdlatul Ulama Kalimantan Barat https://orcid.org/0000-0002-5312-3114
Denny Primanda Universitas Nahdlatul Ulama Kalimantan Barat https://orcid.org/0000-0002-0844-1631

DOI:

https://doi.org/10.46229/jifotech.v5i1.984

Keywords:

design, mitigation tools, LoRa, point-to-point, forest and land fires

Abstract

The data transmission process for IoT-based tools aimed at mitigating forest and land fires faces a significant challenge: if an incident occurs in an area without internet access, data transmission fails. This condition causes the device not to notify that a forest and land fire has occurred in locations with poor internet coverage such as in the forest and land areas of West Kalimantan. The data transmission failure problem will be solved with a LoRa network that uses radio waves with a device configuration that can communicate with other devices at the closest distance (point-to-point). The study aims to determine the maximum distance that can be reached to send data point-to-point in the LoRa network. The results of the study showed that the LoRa network with a point-to-point configuration successfully transmitted data with a distance between devices of 1,720 meters. From the results obtained with this configuration, data can be transmitted so that it can be used as a forest and land fire disaster mitigation tool with an ideal distance between devices of 963.08 meters.

References

Open Data Kalbar, “Karhutla.” Accessed: Jun. 26, 2023. [Online]. Available: https://data.kalbarprov.go.id/pages/karhutla

K. Avazov, A. E. Hyun, A. A. Sami S, A. Khaitov, A. B. Abdusalomov, and Y. I. Cho, “Forest Fire Detection and Notification Method Based on AI and IoT Approaches,” Future Internet, vol. 15, no. 2, Feb. 2023, doi: 10.3390/fi15020061.

K. K. Paidipati, C. Kurangi, U. J, A. S. K. Reddy, G. Kadiravan, and N. H. Shah, “Wireless sensor network assisted automated forest fire detection using deep learning and computer vision model,” Multimed Tools Appl, vol. 83, no. 9, pp. 26733–26750, Mar. 2024, doi: 10.1007/s11042-023-16647-5.

Mahaveerakannan R, C. Anitha, Aby K Thomas, S. Rajan, T. Muthukumar, and G. Govinda Rajulu, “An IoT based forest fire detection system using integration of cat swarm with LSTM model,” Comput Commun, vol. 211, pp. 37–45, Nov. 2023, doi: 10.1016/j.comcom.2023.08.020.

L. Kolobe, B. Sigweni, and C. K. Lebekwe, “Systematic literature survey: Applications of LoRa communication,” 2020, Institute of Advanced Engineering and Science. doi: 10.11591/ijece.v10i3.pp3176-3183.

LoRa Alliance, “What is LoRaWAN?” Accessed: Oct. 26, 2024. [Online]. Available: https://lora-alliance.org/about-lorawan/

F. Firouzi, K. Chakrabarty, and S. Nassif, Intelligent Internet of Things: From Device to Fog and Cloud. Springer International Publishing, 2020. doi: 10.1007/978-3-030-30367-9.

J. Han et al., “LoRa-Based smart iot application for smart city: An Example of Human Posture Detection,” Wirel Commun Mob Comput, vol. 2020, 2020, doi: 10.1155/2020/8822555.

R. Strong, J. T. Wynn, J. R. Lindner, and K. Palmer, “Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation,” Sensors, vol. 22, no. 18, Sep. 2022, doi: 10.3390/s22186833.

S. Kotel, F. Sbiaa, R. M. Kamoun, and L. Hamel, “A Blockchain-based approach for secure IoT,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 3876–3886. doi: 10.1016/j.procs.2023.10.383.

S. R. Devasahayam, Signals and systems in biomedical engineering: Signal processing and physiological systems modeling, vol. 9781461453321. Springer US, 2012. doi: 10.1007/978-1-4614-5332-1.

J. Schroeder, “Signal Processing via Fourier-Bessel Series Expansion,” Digit Signal Process, vol. 3, pp. 112–124, Apr. 1993, doi: 10.1006/dspr.1993.1016.

J. Petäjäjärvi, K. Mikhaylov, M. Pettissalo, J. Janhunen, and J. Iinatti, “Performance of a low-power wide-area network based on lora technology: Doppler robustness, scalability, and coverage,” Int J Distrib Sens Netw, vol. 13, no. 3, Mar. 2017, doi: 10.1177/1550147717699412.

U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low Power Wide Area Networks: An Overview,” IEEE Communications Surveys and Tutorials, vol. 19, no. 2, pp. 855–873, Apr. 2017, doi: 10.1109/COMST.2017.2652320.

F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melia-Segui, and T. Watteyne, “Understanding the Limits of LoRaWAN,” IEEE Communications Magazine, vol. 55, no. 9, pp. 34–40, 2017, doi: 10.1109/MCOM.2017.1600613.

J. Haxhibeqiri, F. Van den Abeele, I. Moerman, and J. Hoebeke, “LoRa scalability: A simulation model based on interference measurements,” Sensors (Switzerland), vol. 17, no. 6, Jun. 2017, doi: 10.3390/s17061193.

A. Lavric and V. Popa, “Internet of Things and LoRaTM Low-Power WideArea Networks: A Survey,” in 2017 International Symposium on Signals, Circuits and Systems (ISSCS), IEEE, 2017. doi: 10.1109/ISSCS.2017.8034915.

M. Bor, U. Roedig, T. Voigt, and J. M. Alonso, “Do LoRa low-power wide-area networks scale?,” in MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Association for Computing Machinery, Inc, Nov. 2016, pp. 59–67. doi: 10.1145/2988287.2989163.