# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 4 Comparison of the Optimized Clustering Approaches
However, the solutions based on the BCO, PSO, and FL have a lower energy consumption than the others paradigms. Thus, we may say that algorithms based on SI and on FL consume less energy that GA, NN, and RL based algorithms. This can be explained knowing that clustering algorithms based on GA and NN are mostly complex to implement and need more node computations. Due to their limited resources, nodes would waste their energy faster. Table 2 compare the scalability of the presented algorithms by the CI-ML used. We note that the optimized strategies based on FL, NN, or GA have less scalability than the others do. However, solutions which used SI are the best in terms of the scalability. The higher scalability of the SI based approaches is due to the fact that the SI considers the collaboration between a large amount of agents in a population. However, on FL, NN, and GA when the number of node increases, the complexity of \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Energy Consumption**} & \multirow{2}{*}{**Total**} \\ \cline{2-3}
**ML/CI** & & **Low** & **Average** & **High** & \\ \hline FL & 3 & 3 & 3 & 9 \\ FL/SI & 1 & 0 & 0 & 1 \\ GA & 1 & 2 & 1 & 4 \\ NN & 0 & 2 & 0 & 2 \\ RL & 1 & 1 & 0 & 2 \\ PSO & 1 & 4 & 1 & 6 \\ ACO & 0 & 4 & 1 & 5 \\ BCO & 1 & 3 & 0 & 4 \\
**Total** & **8** & **19** & **6** & **33** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CI’s approaches according to the energy consumption. algorithms increases, the computations made by the nodes or the BS require more time and resources (microcontroller, memory, etc.). The data delivery rate which fixes the amount of data received by the BS is an important parameter. From Table 3 below, we observe optimized clustering approaches based on BCO, ACO, and PSO have the better data delivery rate than the others CI, it proofs then that solutions based on SI are more adapt for applications design on the amount of data to be delivered at the BS. This difference of data delivery rate can show how the approaches based on the GA, NN, or RL lose data than SI based paradigms which can be considered as more reliable. Since the main advantage of using the clustering approach is its high scalability, however, it varies in function of the CI approaches. Tables 4-6 present respectively the comparison between the approaches which can be either centralized or distributed in terms of the energy consumption, the data delivery rate, and the scalability. From these tables, optimized clustering which has a centralized approach outperform solutions with a distributed skeleton. Most of the centralized optimized clustering algorithms consume less energy than the distributed strategies. However, for an average consumption, both have approximately the same energy consumption. Centralized algorithms outperform distributed algorithms because the major part of the operations are done at the BS in centralized approaches, each node within the sensor field usually sends only the necessary elements (its position, remaining energy, sensed data, etc.) to the BS most of the time without any additional computing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML/CI**} & \multicolumn{3}{c}{**Scalability**} & \multirow{2}{*}{**Total**} \\ \cline{2-3} \cline{5-5}  & **Low** & & **Medium** & **High** \\ \hline FL & 6 & 3 & 0 & 9 \\ FL/SI & 0 & 1 & 0 & 1 \\ GA & 2 & 2 & 0 & 4 \\ NN & 2 & 0 & 0 & 2 \\ RL & 1 & 1 & 0 & 2 \\ PSO & 1 & 0 & 5 & 6 \\ ACO & 2 & 1 & 2 & 5 \\ BCO & 0 & 2 & 2 & 4 \\
**Total** & **14** & **10** & **9** & **33** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of CI’s approaches according to the scalability.