# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 4 Results and Discussion
### Energy Consumption
#### 4.2.2 Sparse Data  
There are very limited values in the \(EC_{\text{sparse}}\) data comprised of 4.9% of the total data and this portion is treated as an outlier. However, compared to the unsplitted data, descriptive statistics are expected to be more normal; however, there is still a notable skew with high values for the mean (21.09) and standard deviation (26.07) and a comparatively low median (4.16). The 10 th, 25 th, 50 th, 75 th, and 90 th percentiles are 0.509, 0.917, 4.166, 40.96 and 57.11, respectively. This indicates the progression in the values for \(EC_{\text{sparse}}\). Despite deviant data, a deep learning model is trained to seek prediction results for \(EC_{\text{sparse}}\). Prediction results are described in Figures 10 and 11. True values and predicted values are plotted in Figure 10, which seem to lie in a close proximity to the ideal diagonal line. However, close to the origin, there is a visible variation. To improve the understanding and to have a closer look, predicted values are plotted against actual values in Figure 11. It is visible that the majority of the predicted values are aligned with the true values. Further outcomes are presented in Figure 12. According to Figure 12a, the order of features in yielding lesser \(RMSE\) is \(TP\), \(DT\), \(AT\), and \(PS\). As against \(EC_{\text{dense}}\), \(PS\) moves to the fourth position and each of the next three features slide a position back. This is due to the fact that more failed transmission attempts cause \(EC\) to go up due to the failure of delivery, resulting in an enormous rise in \(EC\) per data bit, thus superseding the influence of \(PS\). Overall, \(RMSE\) reaches its minimum (2.412) with five features. Figure 12b consists of \(AT\), \(TP\), \(PS\), \(DT\), and \(RD\), resulting in an accuracy of 88.5% against a mean (21.09) of \(EC_{\text{sparse}}\). The \(x\)-axis label codes used in Figure 12b are explained in Figure 12d. The prediction error is characterized in Figure 12c,  
Figure 9: Prediction results for \(EC_{\text{dense}}\). where 60% of the predictions have an error of 29.8% or less of the actual \(EC_{\text{dense}}\) values. It can be conjectured that deviation, as well as lack of enough data, both contribute to this higher prediction error in \(EC_{\text{sparse}}\) split. It turns out that \(TP,DT,AT\) and \(OF\) are the most prominent and common features that significantly contribute to minimizing prediction error for both \(PDR\) and \(EC\). Therefore, it encourages the combined consideration of both metrics when it comes to predicting their values. Table 4 presents the values of \(MPE\), \(MAPE\), Pearson Coefficient (R) and the \(p\)-value. It appears that the values for \(MPE\) and \(MAPE\) are very low with a close correlation represented by R. \(p\)-value for all predicted metrics is extremely low. These statistics further strengthen the results and encourage the adoption of predictive modeling of QoS metrics based on configurable stack parameters. Furthermore, this kind of adaptive model is sustainable because learning is based on the values of different communication stack parameters. This is in contrast to the client side approaches, where missing values in the metrics of interest are predicted without paying any regard to the inputs. A comparison of \(RMSE\) for different regression models is presented in Table 5. It is clear that deep NN outperform all other models including baseline NN (having single layer). The only model that performs close to deep NN is random forests because of its inherent design for nonlinear data separation. From this work, it can be concluded that deep learning captures the relationships between the input parameters and performance metrics. This implies that for any change in the parameters, be it variation in value or addition of new parameters, deep learning has the potential to adjust the learning process, and the result will be a system intrinsically sustainable as it can adapt to these changes. Figure 10: Actual values vs. predictions for \(EC_{\text{sparse}}\).