# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Hybrid Techniques
#### 6.3.2 Hybrid Fuzzy  
Hybrid optimization approaches are classified into fuzzy-based and meta-heuristic-based methods [106]. Some nodes do not assume CH status because only one CH can be selected within their transmission range. At the expiration of a timer, one CH is selected by every node based on the strength of the received signals from potential CHs. After data aggregation and cluster formation by the CHs, the data are propagated to the BS following certain rules. In this section, fuzzy hybrid techniques from various studies were discussed and evaluated regarding the methodologies and features. To illustrate a general outline of the different approaches, the comparison of techniques has been presented in Table 7.  
* GA ANFIS: Increasing WSN Energy Efficiency to Choose a Cluster Head and Assess Routing In Ref. [89], GA-ANFIS was proposed, based on the GA and ANFIS, for WSN clustering. A weighted trust evaluation method was proposed in this method for the discovery of dangerous nodes in the network. The GA is first applied to form clusters considering the distance between the SNs and the BS; then, a fuzzy logic system is applied to find each SN's chances of becoming the CH based on two fuzzy inputs (node energy and node distance from the BS). The last stage is applying the trust evaluation method to detect dangerous nodes and isolating them from the system. * FAMACROW: Fuzzy and ACO Based Combined MAC, Routing, and Unequal Clustering Cross-Layer Protocol  
FAMACROW [90] was presented as a cross-layer hierarchical protocol that combined fuzzy logic, ACO, and MAC for routing and unequal WSN clustering. This protocol performs CH selection, inter-cluster multi-hop routing, and unequal cluster formation. The three phases of FAMACROW include the setup phase, the neighbor finding phase, and the steady-state phase. The setup phase involves sorting the nodes into layers, while \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref** & **Algorithm** & \begin{tabular}{c} **Optimization and** \\ **Fuzzy Methods Used** \\ \end{tabular} & **Objective** & \begin{tabular}{c} **Optimization** \\ **Objectives** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy Input** \\ **\end{tabular} & \begin{tabular}{c} **Defuzzification** \\ **Method** \\ \end{tabular} & \
\begin{tabular}{c} **Fuzzy Rule** \\ **Evaluations** \\ \end{tabular} & **Matrices** & **Simulation** \\ \hline \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & Use of ANFIS to & & & & & & & & \\  & group and select CHs & & & & & & & & & \\  & in a WSN to ensure & & Combines GA and & & & & & & & \\  & low energy usage by & Increase the & fuzzy logic to detect & & & & & & & \\  & the nodes. Harnfull & lifespan of & & & & & & & & \\  & nodes in the WSN & & & & & & & & \\  & were discovered by & & & & & & & & \\  & applying a weighted & & & & & & & & \\  & trust evaluation & & & & & & & & \\  & Fuzzy logic-based CH & & & & & & & & \\  & selection technique & & & & & & & & \\  & for selecting & & & & & & & & \\  & high-energy nodes & & & & & & & & \\  & and nodes with & & & & & & & \\  & high-quality & & & & & & & & \\  & communication links & & & & & & & \\  & and more neighboring & & & & & & & & \\  & are & & & & & & & \\  & access as CHs; and & & energy con- & & & & & & \\  & AC-based technique & & & & & & & \\  & was used for & & & & & & & \\  & “intor-cluster & & & & & & & \\  & multi-hop routing & & & & & & & \\  & from CHs to the BS & & & & & & & \\  & The Sugeno fuzzy & & & & & & & \\  & inference system uses & & & & & & & \\  & the Fuzzy C-means & & & & & & & \\  & algorithm to cluster & & & & & & & \\  & sensor nodes into & & & & & & \\  & balanced clusters & & & & & & & \\  & before selecting the & & & & & & & \\  & CH. Artificial BCA & & & & & & & \\  & was used to optimize & & & & & & & \\  & the fuzzy rules to & & & & & & & \\  & prolong the service & & & & & & & \\  & life of the network & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of studies involving fuzzy hybrid techniques.