## 6 Experimental Evaluation
### Ama
#### 6.3.3 Experiment 3: Adaptations to Changes in the Domain Model  
The aim of this experiment is the study the effectiveness of the proposed VCA in modifying the domain model to better fit the current state of the application domain. It also aims to study the effectiveness of VCA in differentiating between faulty sensor behaviour and actual changes in the domain model. After applying heat on the brass sheet for \(30\) s, the obstacle length was increased. Then, we observed how the AMA used the VCA to adapt its behaviour to the emerging change in the application domain. Particularly, the bill that contains the best (closest to the actual obstacle length) detected obstacle length value, the agreed bills, and the federal laws that were examined. It was assumed here that the obstacle is continuous and the existence of this obstacle between two directly communicating nodes will break the wireless links between them. In addition, \(30\%\) of the nodes were configured to act as faulty nodes in each experimental run and \(v\) was assigned different values. Faulty nodes were allowed to propose bills that carry incorrect changes in the application domain. Finally, it was studied how the choice of \(v\) will affect the VCA ability to discover bills generated by faulty nodes. Due to the effect that network density and nodes distribution have on the mapping accuracy results, five simulation runs are combined to estimate uncertainties in the simulations. In each simulation run, sensor nodes were randomly positioned in the simulation window. At each point, the measured metric of the five runs was averaged. Results in Table 1 show that VCA has always detected the obstacle length accurately, but the best proposed bill was not always agreed within the cluster. This is partially due to the properties of the used clustering protocol, which is able to deal with obstacles (see [19]). Nonetheless, the average VCA agreed bills in the three mapping runs were \(53.91\) pixels, which is close to the actual obstacle length (\(60\) pixels). Adapting the mapping service to the new obstacle length improves the produced map quality. Figure 19 shows the heat map generated by AMA using the initial obstacle length. Visually, this map does not totally reflect the current state of the application domain compared to Figure 15 (map with obstacle length equal to \(60\) pixels). Quantitatively, the RMSD difference from Figure 15 was increased by \(1.0\). In the three mapping runs, zero bills became state laws. This is because all the changes in the application domain were local to part of the network and the majority of the clusters did not sense these changes. This illustrates the benefit of localising the VCA. Table 1 shows the results of VCA testing with \(100\) and \(500\) network densities each with results from three different random topologies. Table 2 shows the number of bills proposed by faulty nodes and how many of those bills were agreed. First, the number of bills generated by the faulty sensors in each experimental run with different values for \(\upsilon\) were counted. The values tested were \(\upsilon=40\%\), \(\upsilon=50\%\), and \(\upsilon=60\%\). It was found that the \(\upsilon=50\%\) gave the minimum number of agreed faulty bills while avoiding the false positives we get with the \(\upsilon=40\%\). Whereas the \(\upsilon=60\%\) caused many correct bills to be rejected. The results shown in Table 2 were generated using \(\upsilon=50\%\). This experiment shows that VCA helps to adapt to some changes in the domain model in a distributed manner. It also shows that the optimal value for \(\upsilon\) is \(50\%\). This experiment is an instance of the general case studied in Experiment \(2\), particularly, the nearest neighbour triangulation RF connectivity map is used as one dimension to predict the heat map.