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

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.

A new IoT geosensor network for cost-effective landslide early warning systems

<p>Worldwide, cities in mountainous areas struggle with increasing landslide risk as consequence of global warming and population growth, especially in low-income informal settlements. For these situations, current monitoring systems are often too expensive and too difficult to maintain. Therefore, innovative monitoring systems are needed in order to facilitate low-cost landslide early warning systems (LEWS) which can be applied easily.</p><p>Based on technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication standard, we are currently developing a cost-effective IoT (Internet of Things) geosensor network. It is specifically designed for local scale LEWS in informal settlements.</p><p>The system, which is open source and can be replicated without restrictions, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g. tilt sensor) on board and to which various additional sensors can be connected. The nodes are autonomous and can operate on standard batteries or solar panels. The sensor nodes can be installed on critical infrastructure such as house walls or foundations. Two of the possible additions are the Subsurface Sensor Node and the Low-Cost Inclinometer. Both are installed underground and offer tilt- and groundwater-measurements of the subsurface.</p><p>Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed monitoring system offers a great cost to benefit ratio and easy application for similar sites and LEWS, especially in urbanized areas in developing countries.</p><p>This work is being developed as part of the project Inform@Risk, where the monitoring system will be installed as part of an early warning system in Medellín, Colombia. It is funded by the German Ministry of Education and Research (BMBF).</p>

Spatial modeling of air pollution based on data streams from geosensor networks

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

3D EXTENTION OF THE VOR ALGORITHM TO DETERMINE AND OPTIMIZE THE COVERAGE OF GEOSENSOR NETWORKS

Recent advances in electrical, mechanical and communication systems have led to development of efficient low-cost and multi-function geosensor networks. The efficiency of a geosensor network is significantly based on network coverage, which is the result of network deployment. Several optimization methods have been proposed to enhance the deployment efficiency and hence increase the coverage, but most of them considered the problem in the 2D environment models, which is usually far from the real situation. This paper extends a Voronoi-based deployment algorithm to 3D environment, which takes the 3D features into account. The proposed approach is applied on two case studies whose results are evaluated and discussed.