# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 5 Performance Evaluation
### Comparing ACxSIMv2 and LEACH  
The LEACH protocol was simulated in eight different scenarios considering only the Intel Lab Dataset. In each scenario, the value of \(P\) was modified. As cited earlier, \(P\) defines the probability of a node being elected as the cluster head. The values of \(P\) used were 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9. Table 5 summarizes the results from the simulation.  
Figure 15 shows the percentage energy consumption savings and the MSE of LEACH in the eight simulated scenarios. Considering the figure, MSE is closely correlated with the value of \(P\), which was expected since, in the LEACH protocol, the clusters are created according to the nodes' proximity (based on physical distance). Small values of \(P\) means that few cluster heads are selected. In this case, the MSE is higher since each cluster may contain nodes distant from each other, resulting in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold**} & \multicolumn{2}{c}{**Energy (m)**} & \multicolumn{2}{c}{**Energy Savings (\%)**} & \multicolumn{2}{c}{**MSE**} \\ \cline{2-7}  & **v1** & **v2** & **v1** & **v2** & **v1** & **v2** \\ \hline
0.1 & 1289.77 & 753.39 & 1.09 & 32.90 & 1.01 & 0.29 \\
0.3 & 1256.28 & 688.15 & 3.66 & 38.71 & 0.96 & 0.29 \\
0.5 & 1155.60 & 654.14 & 11.38 & 41.74 & 0.97 & 0.28 \\
1.0 & 903.87 & 582.21 & 30.68 & 48.15 & 1.46 & 0.31 \\
2.0 & 702.25 & 493.04 & 46.14 & 56.09 & 1.83 & 0.38 \\
3.0 & 605.91 & 433.53 & 53.53 & 61.39 & 2.07 & 0.48 \\
4.0 & 554.62 & 397.78 & 57.46 & 64.57 & 2.10 & 0.60 \\
5.0 & 306.30 & 342.40 & 66.52 & 76.84 & 2.50 & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ACxSIMv1 and ACxSIMv2 results.  
Figure 14: Real and predicted temperatures (\({}^{\circ}\)C) of Node 18 with THRESHOLD equal to 5.0.  
clusters with measurements that are very different. As the value of \(P\) increases, the number of cluster heads increases as well. Hence, the MSE decreases because it contains more nodes close together (based on physical distance). Regarding the energy consumption savings, as the value of \(P\) increases, more nodes are elected as cluster heads, which increases the number of transmissions inside the network. Consequently, the energy consumption savings decrease. As the value of \(P\) approaches 1, the energy consumption of LEACH becomes more similar to that of the single-hop communication. It is clear from the aforementioned figures that higher values of \(P\) are not necessary the best configurations, since the energy consumptions tend to be higher.  
According to Tables 4 and 5, the highest value of MSE in ACxSIMv2 (1.05) is about \(27\times\) lower than highest value of MSE in LEACH scenarios (28.72). In this same configuration, the energy consumption savings of ACxSIMv2 (69.50%) is just approximately \(1.15\times\) lower than LEACH. It is important to stress that this is just the scenarios with highest energy savings in both solutions. However, a MSE equals to 28.72 makes LEACH, with \(P=0.2\), infeasible for WSN applications. As depicted in Figure 16, the absolute error between the real data from Node 1 and the data associated with its clusters, during the simulations in the scenarios with \(P=0.2\), falls beyond \(5\,^{\circ}\)C in most of the time. Considering the results with the lowest MSE, LEACH has a MSE equals to 2.09 and energy savings equals to 9.80% for \(P=0.9\), while ACxSIMv2 has 1.05 and 69.50% for the same metrics in the scenario with \(THRESHOLD=6.0\). Therefore, ACxSIMv2 has better performance in both MSE and energy consumption metrics, about 2.0\(\times\) and 7\(\times\), respectively.  
Figure 16: Absolute error in LEACH scenario with P equals to 0.2.  
Figure 15: Energy savings and MSE of LEACH for Intel Lab Dataset