# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 4 Comparison of the Optimized Clustering Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML/CI**} & \multicolumn{3}{c}{**Data Delivery Rate**} & \multirow{2}{*}{**Total**} \\ \cline{2-3} \cline{5-5}  & **Low** & & **Average** & **High** \\ \hline FL & 1 & 1 & 0 & 2 \\ FL/SI & 0 & 1 & 0 & 1 \\ NN & 0 & 1 & 0 & 1 \\ RL & 0 & 1 & 0 & 1 \\ PSO & 0 & 3 & 2 & 5 \\ ACO & 1 & 0 & 1 & 2 \\ BCO & 0 & 0 & 4 & 4 \\
**Total** & **2** & **7** & **7** & **16** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of CI’s approaches according to the data delivery rate. From our analysis, we observe that must of the presented algorithms uses the first order as radio/energy model. The first order radio model is widely used because it is simple to implement and considers only the energy spent during the transmission and the reception per bit. The energy consumed during the transmission of \(k\)-bit over a distance \(d\) is presented in (1), where \(e_{t}\) is the energy dissipated by a bit during its transmission and \((e_{d}\times d^{2})\) the energy consumed for transmitting a single bit over a distance \(d\). \[E_{tx}(k,d)=\Big{(}e_{t}+e_{d}.d^{2}\Big{)}.k \tag{1}\]  
The energy required for successfully receive \(k\)-bit is given in (2), where \(e_{r}\) represents the energy needed to receive a single bit  
\[E_{rx}(k)=e_{r}.k \tag{2}\]  
Other algorithms used either the radio model based on the real transceiver CC2420. According to this choice, we can assume that clustering algorithms which implement the CC2420 radio model (data transfer rate, path loss, energy consumption during transmitting and receiving of data) are more accurate for a real environment deployment. However, the used of first order or CC2420 models are only suitable for terrestrial WSN applications, if the sensor field is different from the ground surface, these radio models will not be appropriate. Table 7 presents the summary of the comparison between the presented optimized clustering approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML/CI**} & \multicolumn{4}{c}{**Scalability**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **Low** & **Medium** & **High** & \\ \hline Centralized & 35.7\% & 28.6\% & 35.7\% & **14** \\ \hline Distributed & 47.4\% & 31.6\% & 21.1\% & **19** \\ \hline \multirow{2}{*}{**Total**} & **14** & **10** & **9** & **33** \\ \cline{2-5}  & **42.4\%** & **30.3\%** & **27.3\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of CI’s approaches according to the nature of the approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML/CI**} & \multicolumn{4}{c}{**Energy Consumption**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **Low** & **Average** & **High** & \\ \hline Centralized & 28.6\% & 57.1\% & 14.3\% & **14** \\ \hline Distributed & 21.1\% & 57.9\% & 21.1\% & **19** \\ \hline \multirow{2}{*}{**Total**} & **8** & **19** & **6** & **33** \\ \cline{2-5}  & **24.2\%** & **57.6\%** & **18.2\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the energy consumption according to the nature of the approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML/CI**} & \multicolumn{4}{c}{**Data Delivery Rate**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **Low** & **Average** & **High** & \\ \hline Centralized & 0.0\% & 37.5\% & 62.5\% & **8** \\ \hline Distributed & 25.0\% & 50.0\% & 25.0\% & **8** \\ \hline \multirow{2}{*}{**Total**} & **2** & **7** & **7** & **16** \\ \cline{2-5}  & **12.5\%** & **43.8\%** & **43.8\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the data delivery rate according to the nature of the approach.