Next Article in Journal
A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control
Next Article in Special Issue
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
Previous Article in Journal
Simulation Study of a Frame-Based Motion Correction Algorithm for Positron Emission Imaging
Previous Article in Special Issue
Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model
Article

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

1
Chair of Engineering Geology, Technical University of Munich, 82024 Munich, Germany
2
AlpGeorisk, 85716 Unterschleißheim, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Rafig Azzam
Sensors 2021, 21(8), 2609; https://doi.org/10.3390/s21082609
Received: 5 March 2021 / Revised: 30 March 2021 / Accepted: 6 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue MEMS Sensors for Monitoring in Earth Management)
Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the [email protected] research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world. View Full-Text
Keywords: early warning system; landslides; geosensors; monitoring; Colombian Andes; low income settlements; informal settlements; IoT early warning system; landslides; geosensors; monitoring; Colombian Andes; low income settlements; informal settlements; IoT
Show Figures

Figure 1

MDPI and ACS Style

Gamperl, M.; Singer, J.; Thuro, K. Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems. Sensors 2021, 21, 2609. https://doi.org/10.3390/s21082609

AMA Style

Gamperl M, Singer J, Thuro K. Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems. Sensors. 2021; 21(8):2609. https://doi.org/10.3390/s21082609

Chicago/Turabian Style

Gamperl, Moritz, John Singer, and Kurosch Thuro. 2021. "Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems" Sensors 21, no. 8: 2609. https://doi.org/10.3390/s21082609

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop