## 2 Related Works  
Many routing protocols, such as hierarchical, flat and location types, are designed to fit WSN features, in which energy is a critical problem. Other available routing protocols include data-centric routing protocols, such as SPIN and directed diffusion, and those for low-power and lossy networks. Signal strengths anticipate the distance between nodes, so the nodes are aware of their locations [6]. Geographical and energy-aware routing (GEAR) [7] is a technique for routing packets to a destination region that incorporates energy-aware neighbor selection. Cluster-based routing was created with scalability and communication in mind, and the formation of clusters as well as selection of CHs can extend a network's lifespan [8]. Because the energy capacity is limited [9], conserving energy is significant in determining the sensor network's lifespan. Many strategies for extending the life of a network have been presented, such as the LEACH [10] technique, in which SNs send data to a CH rather than to the base station (BS) directly. The LEACH approach has been modified numerous times and has become a core method among hierarchical routing systems. For instance, the LEACH protocol, a variation of the LEACH technique that incorporates location and residual energy characteristics, was created to address the issue of load balancing with low degrees [11]. The TL-LEACH protocol is another method for resolving the distance problem between CHs and BSs [12]. The LEACH methodology is also used for CH data gathering and fusion, although one CH uses another CH closer to BS. The power-efficient gathering in sensor information system (PEGASIS) [13] is another protocol in which nodes only communicate with their nearest neighbors. To ensure all nodes have the BS, sensing node systems use a greedy algorithm to establish transmission chains. V-LEACH [14], a highly energy-efficient variation of LEACH, is another option. In the CH selection step, the distributed EEUC method [15] considers node energy usage and distance between source and sink nodes to equalize the network's energy consumption by marking all nodes. The CUCA algorithm [16] selects the cluster head, considering the distance factor, network coverage, and node residual energy. CH in the ERA method [17] creates multi-path data transmission based on residual energy; however, inducing the energy hole to appear prematurely is trivial. Regiment Al [18] suggested a distributed fuzzy logic method based on energy perception as well as CR with non-uniform cluster heads. Yet, the non-uniformity of CHs cannot match the network's reduced energy consumption and maximum CR standards. Subsequently, a distributed multi-layer DFCR clustering technique [19] with improved big data transmission performance was suggested in 2018. The DFCR algorithm considers the nodes' degree, residual energy, distance, centrality, and other characteristics before constructing a multi-objective optimization method to address the uneven energy consumption of nodes. The target tracking issue over a filtering network with dynamic cluster and data fusion is the subject of this research [20]. The clustering technique has been further enhanced for specific real applications over the last two years and has obtained good results [21; 22].