## 1 Introduction  
A Wireless Sensor Network (WSN) contains wide-ranging sensors associated with a wireless medium [1, 2, 3]. The WSN is employed in an assortment of appliances such as meteorological data compilation, climate monitoring, field surveillance, transport, and healthcare [4, 5, 6]. On the other hand, the SNs in WSN do not include any rechargeable storage devices and the capability of rechargeable batteries. In this way, it is difficult to sustain any system with capable power utilization. Clustering is a major method for making data transmission more effective concerning energy and power utilization. This model split the Sensor Nodes (SNs) into assorted clusters [7, 8, 9, 10]. All clusters in the network have distinguishing CH, which is liable to communicate info to other SN in its cluster. In such a scenario, the chief task is to decide on optimal CH under different constraints, such as lower energy consumption, delay, etc. [11, 12, 13]. As a result, to form clusters via the data aggregation and fusion systems, there is EE in the network as the quantity of data conveyed to the Base Station (BS) is lessened significantly [14, 15, 16]. The ageing population, safety, security, healthcare, and other social concerns that advanced nations face are significant issues. The concept of \"smart cities\" makes use of IoT technology to enhance social infrastructure and create a creative remedy for the aforementioned problems. The researcher is encouraged to employ ubiquitous intelligence by the significant advancements in wireless networks, mobile ad hoc networks, 5G, and fiber optic telecommunication [17]. Nowadays, creating an effective WSN is a key study field. A wireless sensor network (WSN) is a collection of parts that transmits data wirelessly from source to destination. Because of the locations where they are deployed, Wireless Sensor Networks (WSN) always require energy. Furthermore, although energy consumption reduction is not the only cause for clustering, in certain survey reports it has been studied from that perspective. The majority of the clustering publications examine and contrast clustering approaches and their effectiveness but neglect to analyze the strategies' goals. To the best of our knowledge, no survey research has examined the properties of WSN networks that existing clustering algorithms enable, such as heterogeneity and mobility. Thus, the cluster-oriented approaches were also affinaced in enabling the development of the life span of the network [18]. The most generally employed methods include Fuzzy C-Means (FCM) and Low Energy Adaptive Clustering Hierarchy (LEACH). Furthermore, LEACH was a cluster-oriented method, which operates in a dispersed way that prefers CH, depending on the determined likelihood. Therefore, improving the energy efficiency of the WSN is important because higher energy usage reduces the network's lifetime. Clustering is an effective tactic for lengthening the network's lifespan and reducing the amount of transmission energy used. With clustering, packet collision is eliminated, throughput is increased, and network scalability is prolonged. Clustering also decreases overall energy consumption. A set of central cluster-oriented methods have been launched so far, depending on meta-heuristic schemes. Definite wide-ranging schemes are Particle Swarm Optimization (PSO), the Harmony Search Algorithm (HSA), and so on. In addition, the challenging factors in modeling the routing schemes are the life span, QoS, and EE of the network. Mobile sinks are employed in the traditional model because of their high throughput and lower energy use, but information loss is the main disadvantage of utilizing a mobile sink [19]. When transmitting data packets from the source node to the destination node using the suggested architecture, information loss is prevented, and by employing the static node, energy consumption is also decreased. Because of its mobility, a cluster head used in combination with a mobile sink causes a larger loss of data during transmission.