## 1 Introduction  
Hundreds of thousands of sensor nodes are deployed across the WSN to sense, analyze, and retrieve information. Such sensor nodes are less affordable and offer greater detection, computing, and communication capabilities [1]. Medical applications, defensive performance, weather prediction, and a variety of industrial and commercial uses are just a few of the applications that use WSN [2]. WSN sensors are small and run on a small battery [3]. The sensor is utilized an Analog-to-Digital Converter (ADC) to capture data, which it then processes before sending it to the primary hub, called the Base Station (BS). Across different applications, the data is evaluated at BS to make decisions. WSN sensor nodes act as a repeater, sending information to additional sinks and sensors [4]. Due to the placement of the sensor in a hostile and no-land environment, the WSN's power supply can sometimes be recharged or switched, man's it must be handled properly [5]. Various characteristics, such as energy efficiency, fault tolerance, scalability, and so on, have an impact on the construction of WSNs [6]. The energy used by the sensors in the WSN has been used in two ways: (1) environmental parameter sensing and (2) data transmission to the BS via the nodes. WSN data transmission ingests more energy than environmental sensing and data processing [7]. In the WSN, CHs are used to save energy. CH is chosen based on a set of restrictions, including the estimated distance between the receiver and fusion center, as well as residual energy [8]. For sending energy-efficient data, the CH selection of CH is significant. During the specific iterations, the CH in WSN varies to improve performance. The CH uses the Time Division Multiple Accesses (TDMA) technology to collect sensed information. This strategy eliminates redundancies by reducing and gathering information. For capturing information from multiple WSNs, dynamic clustering is the best option [9]. As a result of some aspects, such as energy-saving capability and efficient scalability, dynamic clustering has attracted the interest of various academics. Dynamic clustering is homogeneous and adapts to variations within the transmission range of the sensor. The CH operates in a variety of hop networks, in this case [10]. The periodic rearranging of clusters aids the specialist in enhancing the scalability & energy of WSNs. The spinning of CH is employed in numerous means of transport and IoT applications [11] where dynamic clustering has been used to sustain data transmission between nodes [12]. Energy constraint, in particular, is seen to be a major issue. Every one of the sensing elements requires energy to work. As a result, maximizing the network's longevity requires managing a node's energy usage [13]. One of the most well-known strategies for effectively reducing energy usage in WSNs is the hierarchical arrangement of the network in clusters.CH, the cluster's head node, is in charge of each cluster. In this regard, several unique methods have been presented, all of which have been shown to be effective [14]. Furthermore, within those processes, the direction of finding is ignored, and the heuristic function's essential choice for determining a relay node for data transmission is based solely on a single parameter [15]. Monitoring and control, surveillance systems, healthcare systems, and intelligent space have all benefited from the deployment of WSNs [16]. Furthermore, in WSNs, how to optimally employ network node energy is a worthwhile subject to investigate [17]. Cluster routing is an effective energy maintenance solution for WSNs [18], with the LEACH algorithm being the most well-known. By clustering, this algorithm splits network nodes into cluster members &cluster heads, allowing network node resources to be completely utilized and the network's life cycle to be successfully extended. To limit the number of nodes, the TEEN and PEGASIS algorithms used the clustering concept that connects directly with the base station.