## 2 Background  
Building, health, ecological monitoring, security, housing, transportation (cars, aircraft, ships), and retail are just a few places where innovative technology is applied. However, just like sentient animals, smart surroundings first rely on sensory data from the physical world [20; 21]. The emergence of sensors has been made feasible by the rapid development of wireless communication systems, digital electronics, and microelectromechanical system (MEMS) technology in recent years. The tiny devices can measure the surrounding environment's temperature, pressure, humidity, water content, gas presence, and light intensity. Despite the many uses for WSNs, their sensor nodes are constrained in several ways. They have low computational power, limited memory and storage, poor range and bandwidth, and limited energy [21; 22; 23]. One sensor cannot cover large regions due to its short communication range. When many sensors are placed near and linked together, they form a Wireless Sensor Network (WSN) that can monitor a larger area. The lifespan of a sensor node, and by extension, the lifetime of the whole network, is determined by its energy consumption, making this the most crucial factor to consider when developing a WSN. For optimal network performance, it is essential to strike a balance between the sensors' energy restrictions and their resource limits [24; 25]. However, when there are many nodes in a network, conventional direct routing can be inefficient and severely shorten the system's lifespan. As a carryover from traditional wired networks, hierarchical or cluster-based routing is commonly utilized for massive WSNs due to the benefits it provides in terms of scalability, efficient communication, and fault tolerance. The entire network is broken up into smaller clusters in hierarchical systems. Cluster Head (CH) nodes are in charge of aggregating and fusing information from other nodes in the same cluster [26]. A multi-hop behavior is possible for both inter- and intra-cluster communications when using this routing method [27]. Therefore, to conserve its remaining energy, a sensor node will communicate with its nearest neighbor rather than with a neighbor that is further away. On the other hand, unsupervised learning-based clustering methods try to find a balance between the amount of data collected and the amount of data sent [28]. Probabilistic clustering methods predominate in the classic hierarchical-based algorithms. However, as pointed out by the authors, clustering on WSN remains an NP-hard optimization issue that cannot be efficiently handled using classical methods [29]. Utilizing methods based on some weighted parameters or bio-inspired Machine Learning algorithms allows for a more precise solution to the NP optimization of clustering. The term \"optimized clustering algorithms\" describes the most recent paradigm shift in clustering methods. Environmental and biological behaviors algorithms and weighted algorithms can be considered to build an efficient clustering algorithm [28]. There is always a need for clustering solutions which should outperform the vast majority of their predecessors in terms of scalability, reliability, fault tolerance, data delivery, energy consumption, better coverage of the experimental field, and extended network lifetime [30; 31]. At the same time, the type of application used dramatically impacts how much it costs to set up and run a network. Using sensors and cameras, real-time photos, audio, and videos are captured by WSNs and the data is instantaneously sent to the BS. Following this, the BS evaluates the data and provides messages, such as alerts, when detecting a disruption or incident. Multi-WSN scenarios considerably influence human life and activities [24; 25]. For example, 340,000 accidental injuries and $10 million yearly costs may be reduced using a multi-WSN scenario to identify and monitor wildfires [20].