## 6 Experimental Evaluation
### Ama
#### 6.3.1 Experiment 1: Incorporation of the Domain Model in Mapping  
This experiment aims to study the effect of integrating the domain model knowledge into the mapping service.  
A heat source was placed on the sheet edge to the left of the obstacle. After \(30\) s of applying heat, a thermal image for the sheet was taken by the IR camera. Figure 13 shows this heat diffusion map. It can be observed that the obstacle strongly reduced the temperature rise in the area on the right side of the brass sheet. This map has been randomly down-sampled to \(1000\) points, that is \(1.5\%\) of the total \(455\times 147\) points. The sampled set is used by the AMA to re-generate the complete thermal map by three different mapping runs: \((1)\) The mapping service does not have any domain model knowledge (behaves as the standard distributed mapping service); \((2)\) The mapping service integrates _some_ knowledge given by the domain model. Particularly, the presence of the obstacle, its position, and length; \((3)\) The mapping service integrates _all_ the knowledge given by the domain model (all information from run \((2)\) plus the obstacle strength.)  
Figure 14 shows the map generated by run \((1)\). Compared with Figure 13, the obtained map conserves perfectly the global appearance and many of the details of the original map with \(98.5\%\) less data. However, the area containing the obstacle has not been correctly reconstructed and hard edges appeared around the heat source. This is due to the physical discontinuities between adjacent sensors, which attenuates the logical relationship between them. The fact that some interpolation areas contain many sensor readings with almost the same elevation also contributes to distortions in the map quality.  
Figure 14: Heat map generated by run \((1)\).  
Figure 13: Heat diffusion map taken by the IR camera.  
Figure 15 shows the map generated by run \((2)\). In this run, AMA reduced the prediction error compared to run \((1)\). Particularly, AMA accurately captured the effect of the obstacle. The AMA generated map is smoother than that rendered with the distributed mapping service, especially in some sub-regions containing the obstacle and around the heat source location. Moreover, AMA knowledge about the length of the obstacle reduced the heat diffusion prediction error in the area on the right of the obstacle, which was not captured accurately in run \((1)\).  
Figure 16 shows the map generated by run \((3)\). A better approximation to the real surface near the obstacle is observed. The new details included in the domain model removed some artefacts from both ends of the obstacle as shown in Figure 17. This is due to the inclusion of the obstacle width in weighting sensor readings when calculating \(P\), which further reduced the effect of geographically nearby sensors that are separated from \(P\) by the obstacle.  
These experiments show that the incorporation of the domain model in the mapping service significantly improves the performance of the distributed mapping service. It achieves better mapping quality at a lower cost by further excluding nearby nodes that are logically unrelated to the interpolated location.  
Figure 16: Heat map generated by run \((3)\).  
Figure 15: Heat map generated by run \((2)\).