## 2 Related Work  
When nodes lose power, WSN partitions and their network cease to communicate, which is a common concern and is defined as a hot-spot scenario where the WSN becomes disconnected due to the node's energy depletion [14]. To resolve this kind of issue, experts employ two distinct approaches. One discussion concerns only node segmentation and selection of the controller node in a single-hop network. The residual energy of each node determines the controlling capabilities of that cluster. These techniques use the unrealistic assumption that all the deployed sensor nodes and the sink have predetermined transmission spacing [15]. In the current research, clustering processes and routing strategies are also considered distinct difficulties and lead to problems at the hot spot due to uneven resource distributions between WSN nodes [16]. According to the authors, the clustering process and routing strategies must be addressed together and not as distinct concerns [17]. In a comparative study, the research work from [18] investigated the strengths and weaknesses of several clustering algorithms. The study summarized a few key topics in the cluster-building process, including the structure, kinds, and benefits of clustering. In addition, it extensively addressed some Low-Energy Adaptive Clustering Hierarchy (LEACH)-oriented protocols with concern for reactive and proactive techniques in WSNs. In LEACH, every node seems to have a fair probability of becoming a group lead (cluster head), culminating in a dissipation of energy that is more evenly distributed. The core section of the study, advantages, key features, and applications of these techniques were elaborately described and compared to other prominent protocols. The authors in [3] reviewed WSN clustering methods and categorized them, relying upon specific clustering processes and their characteristics and cluster head selection parameters. They examined the main design problems and discussed the performance of clustering protocols centered on categorizing identifiable clustering techniques, clustering methods dependent on neighborhood data, clustering algorithms with biological inspiration, and stochastic clustering strategies. The authors in [19] examined clustering methods, particularly the cluster head selection techniques of adaptive, deterministic, and mixed metric approaches. A cost comparison was made to find out how the selection of CH influenced the creation of clusters, the spread of CHs across the network, and the formation of clusters. In addition, a more extensible, resourceful, and robust clustering method for data collection in the network was presented. As significant amount of research concentrates on cluster formation alone to improve the energy consumption of nodes, including low energy adaptive hierarchical clustering (LEACH) and balanced energy-efficient network-integrated super heterogeneous (BEENISH) [20]. It evaluates the remaining energy of nodes solely on selecting CHs. Researchers in [20] discuss their findings concerning the [21] distributed energy-efficient clustering algorithm (DEEC), the developed DEEC (DDEEC) [22], and the enhanced DDEEC (EDDEEC) algorithms [23]. The findings show the optimal data transmission rate in the BEENISH [20] algorithm with the maximum number of nodes at each round. However, this technique does not work if nodes are positioned randomly or change their groupings often. This work also identified three benefits of the clustering and aggregation technique for WSNs: improved scalability, low maintenance costs, and reduced overhead. After classifying WSN segmentation patterns based on the eight characteristics of clustering, the researchers investigated and evaluated a total of six prominent clustering techniques for WSN, including power-efficient gathering in sensor information systeM (PEGASIS), LEACH, energy-efficient unequal clustering (EEUC), Hybrid Energy-Efficient Distributed clustering (HEED), and others, with many other relevant metrics [24].