# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 5 Summary and Outlook  
A new IoT based geosensor network for communities in informal settlements has been developed, mainly based on open-source hardware and software. The sensor network consists of three different sensor nodes, which allow to monitor movement/deformation of the subsurface (SN, LCI) or existing infrastructure (IN) as well as the groundwater  
Figure 8: Data processing concept of the Inform@Risk project. conditions in near surface soil layers. The subsurface sensors operate most efficiently for shallow rotational landslides. If translational or deep seated landslides are expected, the effectiveness of the system is reduced. In general, the applicability and placement of each sensor node has to be evaluated based on the expected landslide processes and vulnerability to outside influences. While the newly developed sensor nodes are not as precise as existing high quality geotechnical sensors for landslide monitoring, they offer reasonable measurement quality at much lower cost. Thus, they can be distributed in comparably high numbers over an area, whereby sensor density is varied depending on the hazard level and the expected landslide mechanism. This leads to the necessary spatial density of observations, which is required to reliably detect the onset of landslide movements anywhere in the monitored area. When additionally complemented with other monitoring techniques as, e.g., the Continuous Shear Monitor (CSM) and sensors observing the triggering factors, a versatile, robust and flexible LEWS can be created. A limitation of the system can be expected when very small landslides occur. Since the scenario this system aims at is quite difficult (shallow landslide at unknown location), it is possible that a small landslide occurs in between the sensors. This risk depends on the quality of the hazard and risk assessment and the density of CSM lines and LoRa sensor nodes. Bigger landslides should easily be detected by this system, except for very large and slow creeping which does not cause noticeable shearing and tilting on the surface. These slow movements, on the other hand, are not as relevant to this system because they pose less risk to the residents' lives. Another possible drawback of this system might insufficient pre-failure deformation before the event. If there are no significant shear- or tilt-deformations on the surface before an event, it is not possible to issue a warning based on deformation data. This is especially the case with the shallow measurements this system is based on. Therefore, the system needs to incorporate trigger data and it is incessant to have a learning phase where the thresholds for both trigger and deformation data can be defined and evaluated. The large amount of data produced by the system and the high number of outside influences make new data processing procedures necessary, in order to ensure an effective operation and prevent false alarms. These procedures have been outlined here and will be presented in more detail in a future contribution after the system has been implemented and tested. Especially the relation between weather forecasts, groundwater levels and deformation measurements will be addressed in more detail. As already stated, currently further tests of the sensor system are carried out in Germany. As soon as the COVID-19 related restrictions allow for it, the first installation of the proposed LEWS will be performed in Colombia. This system will comprise about 130 Sensor nodes, five drillings with instrumentation and about 1.5 km of horizontal CSM/EXT lines. The installation will also be used to gain further experiences in terms of the social integration of the system. Concepts as, e.g.,'sensor god-fatherhood', meaning that if, e.g., a sensor node is installed on a house wall, the house will take care of the sensor node, will be tested. After an operation of about one year the complete system will be evaluated and recommendations for future developments stated.