## 1 Introduction
#### Related Work  
Making decisions is an important factor in all scientific fields. Using the MCDM approach to solve a problem is a very effective way to find a better solution than all other possible solutions [21]. There have been numerous studies on clustering in WSNs with successful outcomes [22; 23; 24; 25; 26; 27]. In addition, heuristic approaches for clustering in WSNs are applied [28; 29]. The clustering approach can be divided into two categories: static and dynamic. In a static approach, the cluster head is fixed during the clustering [30; 31; 32]. In contrast, in dynamic clustering, the cluster heads are changed periodically during clustering. The low-energy adaptive cluster hierarchy proposed by [PERSON] (LEACH) [33] is one of the examples of dynamic clustering. It relies on randomized CH rotation to uniformly divide the energy load among all sensor nodes in the network. Each node chooses itself as a CH using a probabilistic mechanism and announces its availability to all nearby sensor nodes. Signal intensity is the key factor between the communication nodes to determine the distance between them. The packets received from every node in its cluster are aggregated by the CH. In order to balance the overall energy consumption across the network, each node has a chance to become the CH. In comparison with other methods, LEACH comes with low algorithmic complexity, but it is not energy efficient due to uneven distribution of CHs. An energy efficient heterogeneous cluster (EEHC) was proposed in a heterogeneous environment where some nodes have a higher energy capacity than others. Based on the weighted election probability according to the residual energy, the nodes act as the cluster head. Although the idea of heterogeneity is introduced, this procedure does not take into account various aspects when choosing CHs [34]. A modified version of the LEACH protocol, called LEACH-C (centralized LEACH) [35], was introduced, which reduced the amount of energy used when transmitting data from non-cluster head nodes to their cluster heads by minimizing the total sum of squared distances between all cluster nodes and the closest cluster head. A base-station controlled dynamic clustering protocol (BCDCP) was proposed by [PERSON] and [PERSON] [36], which is a centralized routing protocol that uniformly disperses the energy dissipation among all sensor nodes to maximize average energy savings and extend network lifetime. Previous reported methodologies/algorithms used to increase the wireless sensor network lifetime with their timeline and their major weakness and strengths are presented in Table 2. \begin{table}
\begin{tabular}{l l} \hline ACO & Ant Colony Optimization \\ BCDCP & Base-station Controlled Dynamic Clustering Protocol \\ BS & Base Station \\ CERP & Cluster-Based Event-Driven Routing Protocol \\ CHs & Cluster Heads \\ CLWPA & Clustering Framework for Heterogeneous WSN \\ DEEC & Distributed Energy-Efficient Clustering \\ DMs & Decision Makers \\ EHC & Energy Efficiency Heterogeneous Cluster \\ FPCA & Fuzzy-Based PSO Clustering Architecture \\ HEED & Hybrid Energy-Efficient Distributed Protocol \\ LEACH & Low Energy Adaptive Clustering Hierarchy \\ LEACH-C & Centralized Low Energy Adaptive Clustering Hierarchy \\ MCDM & Multi-Criteria Decision Making \\ OCABC & Optimal Clustering Artificial Bee Colony \\ PAM & Partition Around Medoids \\ PSO & Particle Swarm Optimization \\ REAC-IN & Regional Energy-Aware Clustering with Isolated Nodes \\ SEP & Stable Election Protocol \\ TEEN & Threshold-Sensitive Energy Efficient Sensor Network \\ TFNs & Triangular Fuzzy Numbers \\ TOPSIS & Technique for Order Performance by Similarity to Ideal Solution \\ WSNs & Wireless Sensor Networks \\ \hline \end{tabular}
\end{table} 
Table 1: List of acronyms. A TOPSIS method centered on the election of cluster leaders using a \"Multiple Attribute Decision Making\" approach was proposed by [PERSON] and [PERSON] [37]. For CH selection in WSN, [PERSON] et al. [38] introduced an energy-efficient fuzzy approach.