# MCCM: An Approach for Connectivity and Coverage Maximization
## 5 Validation, Simulation and Comparison with Existing Algorithms
### Comparative Study
#### 5.1.1 Coverage Gain and Displacement
Both algorithms require low computational resources and they use simple mathematical formulas. \(ABSDC\) achieved 143% coverage gain while algorithm \(CMHN\) achieved 185% coverage gain. This means 42% more coverage gain over \(ABSDC\). Figure 7: The matrix \(C\) of coverage. _MCCM_ showed better results compared to the distributed algorithms \(DCTC_{k}\) and \(DCHGA\). \(DCTC_{k}\) achieved 75% coverage gain while \(MCCM\) achieved 87% coverage gain. This means that our algorithm increases the coverage gain by 12% in heterogeneous environment. As for the homogeneous environment scenarios, our algorithm achieves 9% gain over \(DCTC_{k}\). The latter reached a gain of 81% when \(MCCM\) attained 90%. As a result, our algorithm performed better coverage gain in homogeneous environment. Similar results are obtained in the heterogeneous environment. As for the displacement, Figure 8b shows the displacement made by each algorithm to maximize the network coverage. \(CMHN\) has better results than the other centralized algorithms. \(MCCM\) achieves lower average node's displacement and better coverage over the other distributed algorithms (\(DCTC_{k}\) and \(DCHGA\)). Table 3 summarizes the average overall performance of \(MCCM\) compared to the distributed algorithms \(DCTC_{k}\) and \(DCHGA\) in homogeneous and heterogeneous environments. It increases the coverage gain up to 19% and reduces the nodes displacement by 35.55% compared to the algorithms in the table. In the next section, the results obtained in Figure 8 are interpreted from the energy saving perspective. Figure 8: Validation results of our algorithms against some existing algorithms. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**HOMOGENEOUS** & \multicolumn{3}{c}{**50**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**100**} & \multicolumn{3}{c}{**125**} & \multicolumn{3}{c}{**150**} \\
**Number of nodes** & \multicolumn{3}{c}{**50**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**100**} & \multicolumn{3}{c}{**125**} & \multicolumn{3}{c}{**150**} \\
**MCCM versus** & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} \\
**Increase in coverage gain in MCCM** & 15\% & 19\% & 14\% & 14\% & 12\% & 12\% & 15\% & 16\% & 11\% & 13\% \\
**Reduction in Displacement** & 11.65\% & 35.55\% & 29.75\% & 33.31\% & 0.35\% & 15.42\% & 4.13\% & 15.12\% & 8.35\% & 15.58\% \\ \hline
**HETROGENEOUS** & \multicolumn{3}{c}{**50**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**100**} & \multicolumn{3}{c}{**125**} & \multicolumn{3}{c}{**150**} \\
**Number of nodes** & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} & \multicolumn{3}{c}{**DCTC\({}_{k}\)**} & \multicolumn{3}{c}{**DCHGA**} \\
**MCCM versus** & \multicolumn{3}{c}{50**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**70**} & \multicolumn{3}{c}{**70**} & \multicolumn{3}{c}{**75**} & \multicolumn{3}{c}{**70**} \\
**Increase in coverage gain in MCCM** & 0\% & 5\% & 3\% & 3\% & 13\% & 13\% & 13\% & 18\% & 16\% & 26\% \\
**Reduction in Displacement** & 11.86\% & 38.67\% & 1.21\% & 22.87\% & 0.13\% & 21.24\% & 5\% & 10.46\% & 0.09\% & 10.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MCCM performance over existing related works.