## 4 Digital Model of the Area of Interest
### Data Model of the Network  
The coordinate system (coordinate system origin, coordinate system axes), the set of significant points, the polygon of the area of interest and the polygon of the network belong to the mathematical model of the network. We also have other inputs, such as a digital terrain model. This terrain model depends on the detailedness of the terrain \(d\). All these objects need to be defined in the data model of the network.  
The _raster data model_ contains the following data structures:  
_Origo_--a table that contains one record in which there are the following items: \(lat,\ lon,\ H,\ d\). The origins of the coordinate system \((lat,lon)\) are the actual GPS coordinates, \(H\) is its altitude and \(d\) is the detailedness of the terrain, which was applied in the creation of a digital terrain model. This information determines the place where the terrain is located in which the network will be set. Values \((lat,lon)\) need to be calculated according to relations (4) and (5). Parameters \(H\) and \(d\) are going to be input parameters. These data define the origin of the coordinate system, it is the _reference point_ of the system and the _detailedness_ of the terrain model, in which resolution we will work.  
Figure 4: Selecting the next polygon(network) vertex.  
\(T_{Origo}\)--table (matrix \(A=(a_{xy})\)) of size \(mx\times my\)--digital terrain model. Digital terrain model can be obtained from existing software such as MATLAB (for detail \(d=1\)) or it is possible to apply special scanners such as LiDAR, where we can achieve a detail of \(d=10\), which means we can divide a 100 cm section into 10 parts, or in other words, the resolution of one table point will be \(10\times 10\) cm.  
\(S_{Origo}\)--a table that contains rows of \(x\), \(y\), _type_--set of significant points \([x,y]\) coordinates in the table \(T_{Origo}\), _type_--significant point coding, 1--internal (blue color), 2--necessary boundary (yellow color), 3--borderline (red color in Figures 1 and 3). We note that a suitable choice of significant points helps to define different types of obstacles in the field.  
With the help of significant internal points, we can define obstacles as things, events or constructions. Different observation layers can also be created. In that case, matrix \(A\) will be three-dimensional. The third dimension can contain layers; for example, the first layer includes the elevation, the second layer structural obstacles and the third layer natural obstacles. In the case of layers, the data complexity of the system grows.  
\(P_{Origo}\)--table (polygon of the area of interest): contains the lines \([x,y]\)--coordinates of points from the table \(S_{Origo}\), which create the polygon of the area of interest.  
\(Q_{Origo}\)--table (polygon of the grid): include lines \([x,y]\)--some coordinates of \(P_{Origo}\) points and some position data of \(T_{Origo}\) points.  
We can match this basic raster data model to a mathematical model. For completeness, let us note that there may also be a _vector data model_ (see [6]) of the monitored area; but we do not deal with this type of model in this work.  
The data model is needed for the next step, for the software implementation of the network modeling application, which can be considered a small geographic information system (GIS). Algorithms applied for GIS systems can be found in the publication [11].