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. 2021 Apr 8;21(8):2609.
doi: 10.3390/s21082609.

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

Moritz Gamperl  1 , John Singer  2 , Kurosch Thuro  1
Affiliations

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

Moritz Gamperl et al. Sensors (Basel). .

Abstract

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 Inform@Risk 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.

Keywords: Colombian Andes; IoT; early warning system; geosensors; informal settlements; landslides; low income settlements; monitoring.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic layout of the Inform@Risk monitoring system [64]. Data from CSM (Continuous Shear Monitor) and extensometer (bottom), as well as LoRa (Long Range) Nodes (top right) are combined in the Inform@Risk network and cloud system (top left).
Figure 2
Figure 2
Measurement concept for the Infrastructure node (IN), Subsurface Node (SN) and Low-Cost Chain Inclinometer (LCI).
Figure 3
Figure 3
Schematic depiction of the Inform@Risk basic module and the additional sensors for subsurface measurements (SN, LCI). The colors show the data transmission: red: analog signal; orange: I2C (Inter-Integrated Circuit) bus; violet: SPI (Serial Peripheral Interface) bus.
Figure 4
Figure 4
Hardware design of the inclination sensor, the groundwater sensor and the tip and filter section for both the SN and LCI.
Figure 5
Figure 5
Installation suggestions for steel casing (blue) and PVC casing (red, green).
Figure 6
Figure 6
Pictures from a field installation where the presented sensors were tested and evaluated and the installation procedures were developed. (a) Steel and PVC housings with drilled holes for the filter section. (b) Left: installation of sensors into the housing, right: preliminary sensor encasing. (c) Installation of the housing with a jackhammer. (d) Preliminary circuit board for the IN on a wall with attached potentiometer.
Figure 7
Figure 7
Exemplary inclination data taken from an accelerometer on top of a subsurface node. While the measurements do not yet show deformations, daily cycles as well as sensor noise are visible. The sensor noise in this real-world application is higher than that measured in the laboratory.
Figure 8
Figure 8
Data processing concept of the Inform@Risk project.
See this image and copyright information in PMC

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