## 8 Simulation Results
### Performance Evaluation of the Prediction Accuracy  
Figure 16 shows the prediction performance of the three application versions which use linear regression over one day of data gathering from the Intel Research Lab's trace. The error and improvement performance to the humidity and light variables ensures that our solution is better than current solutions.  
The SSerr and R\({}^{2}\) results from prediction of humidity [Figure 16(a,c)] show, for all scenarios, that the lowest prediction accuracy was obtained when we compared simple linear regression based on the time and temperature variables as explanatory variable. The best prediction accuracy was obtained when multiple linear regression was used. However, energy consumption is higher in the versions that use simple or multiple linear regression based on the temperature variable instead of the time variable, although they still get better values than the original version.  
The SSerr and R\({}^{2}\) results from prediction of the light [Figure 16(b,d)] show for all scenarios that the highest prediction error was obtained when we compared simple linear regression based on the time and temperature variables as explanatory variable. The lowest prediction error was obtained when multiple linear regression was used.  
We also observed that there are different behaviors in the results [Figure 16(b,d)] where the light variable is irregular. As per the previous section, the gathered readings of the light variable in the trace are irregular, _i.e._, the values in the trace do not follow a sequence (increasing or decreasing). This probably denotes noise or on and off procedures, and high sensitivity of the light sensor. Thus, the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**App.**} & \multicolumn{2}{c}{**Scenario \#1**} & \multicolumn{2}{c}{**Scenario \#2**} & \multicolumn{2}{c}{**Scenario \#3**} & \multicolumn{2}{c}{**Scenario \#4**} & \multicolumn{2}{c}{**Scenario \#5**} & \multicolumn{2}{c}{**Scenario \#6**} \\ \cline{2-13}  & **Sent** & **Gossiped** & **Sent** & **Gossiped** & **Sent** & **Gossiped** & **Sent** & **Gossiped** & **Sent** & **Gossiped** & **Sent** & **Gossiped** \\ \hline
2 & 0.93 & 0.93 & 0.93 & 0.93 & 0.93 & 0.89 & 0.93 & 0.92 & 0.92 & 0.87 & 0.92 & 0.87 \\
3 & 0.87 & 0.87 & 0.87 & 0.87 & 0.87 & 0.82 & 0.87 & 0.85 & 0.86 & 0.82 & 0.86 & 0.82 \\
4 & 0.86 & 0.86 & 0.86 & 0.86 & 0.86 & 0.81 & 0.86 & 0.84 & 0.85 & 0.79 & 0.85 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage of the energy saving for sending and receiving data in face of the original application version.  
results of the prediction of the light variable show the drawback of multiple linear regression, although it still gets better results than the current approach. When there is no correlation between the variables, prediction accuracy decreases or does not work properly.  
Figure 16: Performance evaluation of the prediction accuracy over one day from the trace to the application versions which use linear regression (app v2 to app v4): **(a)** Humidity error. **(b)** Light error. **(c)** Humidity improvement. **(d)** Light improvement.  
Therefore, we suggest that by using prediction based on multiple linear regression, the sensor node checks the improvements in an adaptive way, as in [PERSON] _et al._[11]. Tables 6 and 7 show more details of the results of SSerr and R\({}^{2}\).  
Figure 16: _Cont._