In EDS ansehen

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

Gespeichert in:
Bibliographische Detailangaben
Titel: A testbed for landslide prediction with blockchain-based transmission and cloud offloading.
Autoren: Fowdur, Tulsi Pawan, Boolaky, Ebrahim Muhammad Issack, Appadoo, Sarvesh Sanjeevi
Quelle: Sensor Review; 2025, Vol. 45 Issue 5, p738-765, 28p
Schlagwörter: LANDSLIDE prediction, BLOCKCHAINS, MACHINE learning, REAL-time computing, INTERNET of things, CLOUD computing, DETECTORS, DATA transmission systems
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Sensor Review is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:02602288
DOI:10.1108/SR-12-2024-1017