# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion
#### 4.4.4 Sensitivity Analysis  
Our simulation results show that the proposed algorithm outperforms existing LEACH-MAE, Mobile-LEACH and LEACHDistanc-M using three performance parameters for four mobility models. To further strengthen our claim, statistical analysis was performed. (_a_) To find the statistically significant mobility model with respect to the performance parameter of our proposed work. **Difference of mean:** Table 2 reports the difference of mean calculated from performance parameter results of our proposed work with respect to four mobility models with average speeds of 1.5 and 7.5 meter per second. Column 1 of Table 2 reports the difference of mean of RPGM with RWP, GM and MH, concerning Average Number of Alive Nodes (ANAN). The result shows that average alive nodes of RPGM are higher than other mobility models and that is statistically significant. Column 2 reports the difference of mean of RPGM with RWP, GM and MH, with respect to average remaining energy. It was found that Average Remaining Energy (ARE) of RPGM based dataset is higher than other mobility models and that is highly significant. Column 3 shows the difference of mean of RPGM with RWP, GM and MH, with respect to average packet delivery ratio (APDR). The result shows that APDR of RPGM mobility model is much higher than other mobility model and that is statistically significant. Similarly, Column 4 compares connectivity metric difference of mean. Here, again, simulation results show that average connectivity (AC) of RPGM mobility model has a high mean difference with comparison to RWP and MH mobility models, whereas it has slightly less mean difference in comparison with GM mobility model, which is still significant. Hence, our proposed algorithm shows better performance with respect to RPGM mobility model dataset than other mobility models that supports our argument. Figure 9: Packet delivery ratio versus number of nodes, maximum speed 7.5 m/s, (**a**) RPGM Dataset; (**b**) RWP Dataset; (**c**) GM Dataset; (**d**) MH Dataset. (_b_) To verify the significant difference of independent variable within the group. **One-way Anova and post hoc tests:** We used one-way variance analysis (ANOVA) to check whether RPGM mean is statistically different from other mobility models with respect to four performance parameters. Here, we stated a null hypothesis that \"means of all mobility models are equal with respect to four performance parameters\". An alternate hypothesis that at least one of the mobility model's mean in a group differs. Table 3 reports that all mobility models are different based on four performance parameters. However, it does not tell us individual effect of mobility models and how much one mobility model mean is significantly different from others. Therefore, we conducted two post hoc test (PHT), i.e., [PERSON]'s test and least square difference (LSD) test. Table 4 shows that the RPGM differs significantly at \(p<0.05\) and it is stronger than other mobility models. Similarly, we further investigated our results by using LSD PHT. It also shows that the effect of RPGM mobility model estimated mean is significantly different. The reason is that RPGM nodes moves in the form of a group. Consequently, it has higher neighborhood connectivity and it balances the remaining energy. Figure 10 shows the performance of mobility models on estimated means of four parameters. RPGM outperforms all other mobility models. It further strengthens our claim that RPGM mobility model performs better with LEACH-MEEC. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**ANAN**} & \multicolumn{2}{c}{**ARE**} & \multicolumn{2}{c}{**APDR**} & \multicolumn{2}{c}{**AC**} \\ \cline{3-10} \multicolumn{1}{c}{**PHT**} & **CMM** & **MM** & **M.D** & **Sig. ** & **M.D** & **Sig. ** & **M.D** & **Sig. ** & **M.D** & **Sig.