## 1 Introduction
Each cluster in the WSN is divided into a controller (or \"cluster head\") that aggregates all the data from the sensor nodes in that cluster and sends it to the source (see Figure 1). In every round, the cluster head sends out a message, and the node that receives the message joins the cluster. If there are many nodes in the cluster, the CH turns down the intensity of the message so that fewer nodes can receive it. In the other scenario, if the cluster size is very small, then the cluster head increases the message strength so that more nodes can receive the message. In accordance with the coverage requirement, sensor nodes are deployed very densely, which allows some nodes to be idle which will increase the lifetime of the network. The selection of cluster heads is performed randomly or on the basis of several criteria, such as highest residual energy, maximum number of neighbour nodes, the distance between the cluster head and the base station, rate of charge or discharge over sending data and average distance of cluster nodes. The selections of CHs are very complex procedures and sometimes tedious, which can be reduced by using multiple attribute decision-making (MADM) approaches. The K-medoids clustering algorithm, which promotes energy efficiency in WSNs, is the foundation for cluster selection. Clustering is one of the most challenging works in WSNs. The partitioning around the medoid (PAM) method is one of the successful techniques for creating a cluster. In this research, the K-medoids algorithm, which is an example of the PAM method, is used to divide the entire network into several clusters. Before using segmentation-based approaches, huge scales of data must initially be clustered. Due to its computational effectiveness and directness, the K-means algorithm is the most widely used clustering algorithm across the several researchers in the field of computational intelligence. K-means [15] depends on finding the cluster head in a huge number of datasets. Using a metric based on Euclidean distance, it alternates distinct positions of the entire datasets with the cluster head and re-calculates the cluster head until a convergence condition is satisfied. However, there are a lot of problems with the K-means algorithm, including the fact that Euclidean distance is the only distance metric utilized in K-means clustering. It has been observed that alternative distance metrics, such as city block distance and cosine distance, can enhance the outcomes of sparse data. When used in applications, such as image processing and other applications, the K-means clustering algorithm has a significant disadvantage that the cluster head need not come from the datasets. When creating clusters, K-means clustering seeks to minimize the distance within the cluster, whereas K-medoids seeks to reduce the dissimilarities between the cluster head and the non-cluster node [16]. On the other hand, reliability, residual energy and distance from the sink node are also dependable factors of WSNs. In this research, key factors, such as distance from base station, average distance of nodes in a cluster and reliability, are also considered with residual energy for CH selection. The entropy method is applied to calculate the weight of each parameter. The TOPSIS technique is applied to each cluster for selection of CHs from each cluster for the first simulation. The proposed algorithm which also takes multiple criteria into account is applied to select the next CHs for the next rounds of simulation. A novel algorithm is introduced to find the cluster head in each round depending on several criteria. Moreover, due to the irregular/hazardous natural circumstances, parameters related to WSNs, such as initial energy of nodes, the distance between sensor nodes and base stations, the size of data packets, measurements of voltage and transmission energy and other factors linked to sensor nodes, is also completely unexpected. Triangular fuzzy numbers (TFNs) are used in this method to express uncertainty or unexpected data [12; 13; 17; 18].