# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 4 Discussion  
This study presents a user-friendly tool for identifying coverage detection areas for new TRs in WSNs. By integrating OOP with Python libraries that provide geospatial technologies, the proposed tool offers a practical and effective solution for visualizing and quantifying SR coverage. The interactive maps that are generated provide a comprehensive view of the SR layout, the TR locations, and the triangulated detection areas. The novelty of the tool presented here lies in its ability to visualize the WSN network and its triangulated coverage areas on a global positioning map, with the triangulated areas calculated using geodesic distances. This distinguishes our tool from other existing tools used for visualizing WSN, such as those presented in [25], for example. Table 3 presents several existing packages that are, or can be, used for geospatial visualization of WSNs, both commercial and open-source. The table focuses on key functionalities, such as geospatial mapping (very important for understanding the physical placement of the network), geodesic area calculations, and the identification of overlapping areas (essential for accurately determining triangulated coverage areas). As shown in Table 3, only four tools, CupCarbon, Mapbox, QGIS, and QualNet, meet all the criteria outlined in this study for geospatial network visualization and triangulated coverage calculations. However, none of these packages performs geospatial calculations automatically. Users must rely on external software or custom programming to handle  
Figure 9: Triangulated detection areas of the five SRs of Scenario 4, shown in transparent red. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Network simulation \\ with GIS \\ System-level \\ performance \\ simulation \\ \end{tabular} & Moderate & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Existing tools for WSN simulation and geospatial visualization. range-free triangulation processing. In contrast, our tool, built on open-source libraries, offers greater flexibility, customizability, and control. Furthermore, our tool is specifically designed for range-free SR network visualization and the calculation of triangulated coverage areas, without the inclusion of extraneous features, such as those offered by packages like Mapbox or QGIS, which are not necessary for the purposes of this study. Regarding network coverage deterioration caused by TRs, this issue has been addressed in [7], where the importance of visualizing changes in SR network performance, particularly in relation to triangulated coverage, is also emphasized. The present study, similar to the study presented in [7], assumes the SRs to be in fixed and known positions. At this stage of research, the algorithm does not need to process SR positions. Instead, the tool quantifies the triangulated coverage over the predefined AoI and identifies potential blind zones. This information is crucial for decision-making, allowing necessary adjustments to optimize triangulated coverage. In this context, the tool adapts from an initial state to a new state by adjusting its parameters to reflect changes in the network. The capabilities of the proposed tool have been briefly demonstrated through four example scenarios. In the first scenario (Scenario 1), which includes only one SR and four TRs, it is shown how easily and effectively the detection zones of an SR can be illustrated and adapted to account for the presence of nearby transmitters within the detection range of the SR. In Figure 6, the four distinct blind zones (\(B_{1}=4\)) caused by the four TRs are easily observed. Scenario 2 (Figure 7) demonstrated how the blind zones of an SR dynamically adapt, as the location of the SR changes. In Scenario 2, relocating \(SR_{1}\) at a randomly selected position closer to the TRs, caused changes in the geometry and the number of the blind zones created.