# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 5 Conclusions  
The mobility of nodes have many constraints in a WSN, energy efficiency being one of them. This study proposed an improvement in energy efficient LEACH-MAE and Mobile-LEACH. We proposed that the selection of CL is based on two parameters, i.e., remaining energy and probabilistic connectivity among neighboring nodes. We calculated neighborhood connectivity for mobile sensor nodes based on radio radial range. Hence, the improved selection of CL enhance remaining energy of mobile sensor nodes and it improved the life time of network. This study analyzed the proposed algorithm with LEACH-MAE, Mobile-LEACH and LEACHDistance-M using three performance parameters (NAN, RE and PDR). The proposed work outperformed other algorithms while using datasets from four mobility models with respect to different speeds.  
Another contribution of this paper is selection of mobility model that is suitable for our proposed work. The results show that the performance of RPGM mobility model is better than that of other mobility models since it has higher connectivity and all the nodes move in the form of group.  
To further strengthen our claim, we performed four statistical tests (difference of mean, one-way ANOVA, post hoc ([PERSON]'s test and LSD) and [PERSON]'s two-stage test. It was found that the difference of means of RPGM (considering ANAN, ARE, APDR and AC) is statistically significant in comparison with other mobility models. We verified significant difference within and between the groups of mobility models with respect to all performance parameters by applying one-way ANOVA and post hoc ([PERSON]'s test and LSD). It is proved through both tests that RPGM is more statistically significant within and between the groups as compared to other mobility models. Lastly, we verified through [PERSON]'s two-stage test that our proposed connectivity parameter is not selection biased. In addition, we found that there is no impact of another instrumental variable. Simulation results and statistical analyses suggest that RPGM mobility model is better for hierarchal clustering in MWSN. In the future, this research work can be extended by increasing the number of mobile sensor nodes and their speeds in a multi-hop environment. We are also working to make the full source code publicly available.  
[PERSON]. ([PERSON]), [PERSON] and Z.K. conceived the idea for this work. [PERSON]. ([PERSON]), F.K. and M.A. ([PERSON]) designed and performed the experiments. [PERSON]. ([PERSON]), Z.K., F.K. and M.A. ([PERSON]) wrote the paper. [PERSON]. reviewed the paper. All authors read and approved the final version of the paper.  
This work is supported by the Fundamental Research Funds for the Central Universities (No. 2682015 QM02).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RWP** & **GM** & **MH** \\ \hline RPGM-RWP & 0.065 ** & & \\  & (2.248) & & \\ RPGM-GM & & 0.093 ** & \\  & & (1.805) & \\ RPGM-MH & & & 0.148 ** \\  & & & (3.632) \\ ANAN & 0.326 ** & 0.253 ** & 0.192 ** \\  & (2.701) & (2.964) & (1.982) \\ ARE & 0.628 ** & 1.838 ** & 0.682 * \\  & (5.136) & (13.221) & (2.026) \\ APDR & 0.045 * & 0.070 & 0.150 ** \\  & (1.804) & (1.178) & (2.333) \\ \(\lambda mr\) & 0.011 & \(-\)0.008 & 0.003 \\  & (0.791) & (\(-\)0.438) & (0.021) \\ Adjusted R2 & 0.712 & 0.725 & 0.742 \\ F-Statistics & 62.456 ** & 66.56 ** & 72.74 ** \\ \hline \hline \end{tabular} Results significant at 5%, 10% are indicated by **, *, respectively.  
\end{table}
Table 5: [PERSON]’s two-stage statistical test.