LoRaWAN Technologies to Enable Landslide Disaster Prone Areas Monitoring


  • Awang Hendrianto Pratomo Universitas Pembangunan Nasional Veteran Yogyakarta
  • Johan Danu Prasetya Universitas Pembangunan Nasional Veteran Yogyakarta
  • Sylvert Prian Tahalea Universitas Pembangunan Nasional Veteran Yogyakarta




Internet of Things, disaster management, LoRaWAN


The rapidly growing communication technology makes communication easier for human-to-human, human-to-machine, and machine-to-machine communication to occur through the internet. Machine-to-machine communication over the internet is known as the Internet of Things (IoT). IoT gives machines the ability to communicate with each other about what they saw, heard, and even think. The purpose of this research is to utilize IoT technology to anticipate catastrophic risks in disaster-prone areas or to reduce impacts when disasters occur. The built-in device design includes several parameters, namely air quality, weather conditions, water conditions, and soil conditions. Rainfall sensors, wind speed and direction, air temperature, and humidity are used to observe the weather conditions. Altitude and water quality sensors are used to observe the water condition. Soil temperature and humidity sensors to observe soil conditions and sensors to measure air quality. Each sensor will send data to the transmitter using the Web service, which will then be managed using IoT and cloud computing technology to provide reports and warnings related to the situation on the research site. Data sent by each sensor can be captured by the server using the Web service and can be managed to be shared with the user through developed applications. The architecture is designed to monitor disaster-prone areas by utilizing rainfall sensors, soil vibration sensors, and soil moisture sensors combined with cloud computing technology to produce an IoT for disaster management.


Van Den Abeele, F. et al. (2017) ‘Scalability Analysis of Large-Scale LoRaWAN Networks in ns-3’, IEEE Internet of Things Journal, 4(6), pp. 2186–2198. DOI: 10.1109/JIOT.2017.2768498.

Aggarwal, S. et al. (2018) 'Landslide Monitoring System Implementing IOT Using Video Camera,' 2018 3rd International Conference for Convergence in Technology, I2CT 2018. IEEE, pp. 2018–2021. DOI: 10.1109/I2CT.2018.8529424.

Bergen, K. M. et al. (2009) ‘Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions’, Journal of Geophysical Research: Biogeosciences. Blackwell Publishing Ltd. DOI: 10.1029/2008JG000883.

Ebi, C. et al. (2019) 'Synchronous LoRa Mesh Network to Monitor Processes in Underground Infrastructure,' IEEE Access, 7, pp. 57663–57677. DOI: 10.1109/ACCESS.2019.2913985.

Fosalau, C. and Zet, C. (2018) ‘Power Management Solutions for a Landslide Monitoring Network’, 2018 International Symposium in Sensing and Instrumentation in IoT Era, ISSI 2018. IEEE. DOI: 10.1109/ISSI.2018.8538132.

Gurdan, D. et al. (2007) ‘Energy-efficient autonomous four-rotor flying robot controlled at 1 kHz’, Proceedings - IEEE International Conference on Robotics and Automation, (April), pp. 361–366. DOI: 10.1109/ROBOT.2007.363813.

Hanumanthaiah, A. et al. (2019) ‘Comparison of Lossless Data Compression Techniques in Low-Cost Low-Power (LCLP) IoT Systems’, Proceedings of the 2019 International Symposium on Embedded Computing and System Design, ISED 2019, pp. 63–67. DOI: 10.1109/ISED48680.2019.9096229.

John Wellington, J., and Ramesh, P. (2018) 'Role of Internet of Things in disaster management,' Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems, ICIIECS 2017, 2018-Janua, pp. 1–4. DOI: 10.1109/ICIIECS.2017.8275928.

Karunarathne, S. M. et al. (2020) 'A technological framework for data-driven IoT systems: Application on landslide monitoring,' Computer Communications. Elsevier BV, 154(February), pp. 298–312. DOI: 10.1016/j.comcom.2020.02.076.

Kitchin, R. (2013) ‘The Real-Time City? Big Data and Smart Urbanism’, SSRN Electronic Journal, (June 2013), pp. 20–21. DOI: 10.2139/ssrn.2289141.

Lu, D. et al. (2012) ‘Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates’, International Journal of Forestry Research. Hindawi Limited, 2012, pp. 1–16. DOI: 10.1155/2012/436537.

Lwin, K. K. et al. (2019) 'City Geospatial Dashboard: IoT and Big Data Analytics for Geospatial Solutions Provider in Disaster Management,' 6th International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2019. IEEE, pp. 2–5. DOI: 10.1109/ICT-DM47966.2019.9032921.

Mei, G. et al. (2020) 'A Survey of Internet of Things (IoT) for Geohazard Prevention: Applications, Technologies, and Challenges,' IEEE Internet of Things Journal. IEEE, 7(5), pp. 4371–4386. DOI: 10.1109/JIOT.2019.2952593.

Moulat, M. El et al. (2018) 'Monitoring System Using Internet of Things for Potential Landslides,' Procedia Computer Science, 134, pp. 26–34. DOI: 10.1016/j.procs.2018.07.140.

Muhammad, M. A., and Supangkat, S. H. (2012) 'Cloud ITS Indonesia: Transportation information sharing platform,' Proceedings - International Conference on Cloud Computing and Social Networking 2012: Cloud Computing and Social Networking for Smart and Productive Society, ICCCSN 2012, (April). DOI: 10.1109/ICCCSN.2012.6215742.


Papageorgiou, M. et al. (2003) ‘Review of road traffic control strategies’, Proceedings of the IEEE, 91(12), pp. 2043–2065. DOI: 10.1109/JPROC.2003.819610.

Pounds, P. et al. (2004) 'Towards Dynamically-Favourable Quad-Rotor Aerial Robots,' in Australasian Conference on Robotics and Automation. Canberra, Australia: ANU.

Pratomo, A. H., Zakaria, M. S., and Prabuwono, A. S. (2009) 'A study on image calibration technique for an autonomous robot,' in Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, pp. 243–246. DOI: 10.1109/ICEEI.2009.5254782.

Premsankar, G. et al. (2020) 'Optimal configuration of LoRa networks in smart cities,' IEEE Transactions on Industrial Informatics. IEEE, 16(12), pp. 1–1. DOI: 10.1109/tii.2020.2967123.

Riasetiawan, M. et al. (2019) 'G-Connect: Real-Time Early Warning System for Landslide Data Monitoring,' Proceedings of the 2019 6th International Conference on Instrumentation, Control, and Automation, ICA 2019. IEEE, (August), pp. 127–130. DOI: 10.1109/ICA.2019.8916747.

Romdhane, R. F. et al. (2017) 'Wireless sensors network for landslides prevention,' 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017 - Proceedings, pp. 222–227. DOI: 10.1109/CIVEMSA.2017.7995330.

Sadowski, S. and Spachos, P. (2018) 'RSSI-Based Indoor Localization with the Internet of Things,' IEEE Access. IEEE, 6, pp. 30149–30161. DOI: 10.1109/ACCESS.2018.2843325.

Santiko, I. and Rosidi, R. (2018) ‘Pemanfaatan Private Cloud Storage Sebagai Media Penyimpanan Data E-Learning Pada Lembaga Pendidikan’, Jurnal Teknik Informatika, 10(2), pp. 137–146. doi: 10.15408/jti.v10i2.6992.

Sung, W. T. et al. (2016) 'IOT-type Cloud Online Real-Time Multi-Car Localization and Communication System,' in Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015. Institute of Electrical and Electronics Engineers Inc., pp. 913–917. DOI: 10.1109/CICN.2015.180.

Susanto, E. et al. (2019) ‘Slope, humidity and vibration sensors performance for landslide monitoring system’, Proceedings - 2019 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2019, pp. 139–142. DOI: 10.1109/APWiMob48441.2019.8964155.