## 6 Conclusion and Open Research Issues  
Clustering in sensor networks is a hot research area, with a rapidly growing set of research results within recent years. While the ultimate objective behind all the protocols is to prolong the network lifetime and enhance the network performance, each protocol focuses on improving the clustering attributes in a specific phase. In this paper, we have conducted a comprehensive survey of cluster-based routing protocols for homogeneous sensor networks. We focused on the contribution of the routing protocols to each phase of clustering process including CH selection, cluster formation, data aggregation and data communication. We also provided the taxonomy of the protocols in each phase according to their objectives and strategies. Then we reviewed major issues in each phase of clustering process and proposed our suggestions for several issues that deserve more attention. We summarize the taxonomy of cluster-based routing protocols for homogeneous WSNs in Table 4, which provides a summary of objectives, characteristic and issues of every individual scheme and approach of each phase. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Phase** & **Scheme** & **Approach** & **Objectives** & **Characteristics** & **Issues** \\ \hline \multirow{6}{*}{**Category**} & \multirow{6}{*}{**Category**} & \multirow{6}{*}{Fixed Probability} & \multirow{6}{*}{distribution of energy-intensive CH role among all sensor nodes} & CH selection based on weighted parameters such as energy expenditure, density dispersion, sensing coverage, regional selectivity, node distances from the BS & \multirow{6}{*}{**Sleep probabilistic**} & \multirow{6}{*}{**Sleep probabilistic**} \\  & & & & \\ \cline{1-1} \cline{4-4}  & \multirow{2}{*}{**Sleep-organized schemes**} & \multirow{2}{*}{**Sleep-based parameters**} & \multirow{2}{*}{**Distributed selection of the optimum CH based on network parameters or type of application**} & \multirow{2}{*}{**Sleep-based parameters such as energy expenditure, density dispersion, sensing coverage, regional selectivity, node distances from the BS} & \multirow{2}{*}{**Sleep-based parameters**} & \multirow{2}{*}{**Sleep-based parameters**} \\ \cline{1-1} \cline{4-4}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the taxonomy of clustering phases. \begin{table}
\begin{tabular}{|p{42.7 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Phase** & **Scheme** & **Approach** & **Objectives** & **Characteristics** & **Issues** \\ \hline \multirow{9}{*}{**Assisted Schemes**} & & & & Fair placement of CHs, & Periodic update of the BS \\  & & & & Limiting the variance of the & with the latest information \\  & & BS Assisted & high processing & numbers of selected CHs, & by sensor nodes, \\  & & & capabilities of the BS for optimal selection of CHs & Transferring the & Centralized controlling a \\  & Assisted & & optimal selection of CHs & computing-intensive tasks to the BS side & bottleneck for the network \\  & & & & BS side & performance and scalability \\ \cline{2-6}  & \multirow{4}{*}{**Multi-factor**} & & &  
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Phase** & **Scheme** & **Approach** & **Objectives** & **Characteristics** & **Issues** \\ \hline \multirow{9}{*}{**Dataset**} & \multirow{9}{*}{**Dataset**} & \multirow{9}{*}{**Dataset**} & \multirow{9}{*}{**Dataset**} & \multirow{9}{*}{**Dataset**} & Filtering redundant data, Layered data aggregation, & \multirow{9}{*}{**Dataset**} \\  & & & & & \\  &  
The most determinant stage in network performance is the CH selection phase, thus many proposals are presented in the literature suggesting variant CH selection approaches and strategies such as fixed probabilistic, adaptive weight-based, BS or CH assisted and multi-factor evaluation schemes.