A testbed for landslide prediction with blockchain-based transmission and cloud offloading

Purpose The purpose of this paper is to develop an IoT-based testbed for land displacement monitoring in real-time with blockchain-enabled transmission and machine learning for predictions. Cloud offloading has also been incorporated into the system proposed. Design/methodology/approach The system consists of a modelled landslide testbed at a laboratory scale with soil, water level, humidity, temperature sensors and a designed extensometer using a 3D printer. An Arduino microcontroller handles all sensor information, and a Raspberry Pi performs blockchain and transmission to a gateway using a 4G transmission module. The transmitted data is received in a server GUI application and a webpage where long short-term memory (LSTM) and multi-layer perceptron (MLP) are used for predictions. For further scalability of this system, cloud offloading was implemented, allowing the information to be accessed across multiple platforms. Findings The accuracy of the designed extensometer was compared to an industrial-grade extensometer. Moreover, the predictions performed with MLP and LSTM yielded a MAPE of 6.0% and 7.8%, respectively. Finally, the blockchain analysis demonstrated that using smaller block sizes provides better security but lower throughput than large block sizes. Originality/value A sophisticated testbed for landslide monitoring, which includes blockchain, AI and cloud offloading, has been proposed along with in-depth performance analysis.