## 3 Materials and Methods
### Overall Flow of the Architecture  
In the meta-heuristic ideology, the term hybridization is generally defined as the process of merging the powerful characteristics of two or more algorithms to generate an algorithm based on the features of the merged concepts. The proposed hybridization of algorithms follows a two-stage process. In the initial stage, it starts with generating the wolf population. After the generation of the population, the fitness of the agents must be determined, followed by updating the wolf position. In the next stage of the process, it calculates the fitness again and updates the position using the dragonfly algorithm. During the process of updating, it calculates the position and velocity vectors for which it calculates the cosine distance with other flies. Through the continuous process of calculating the fitness and updating the positions, it can yield the optimal solution. Generally, WSNs are constrained in the factors of energy efficiency and communication ability. The proposed work mainly focuses on the design of energy and computationally efficient algorithms. It may provide continuously controllable energy to the dead sensors to resume the operation, as mentioned in Figure 1. The other significant aspect of this work is improving the operating performance through the process of the base station forming unequal size clusters and selecting cluster heads, where the clusters much closer to the base station have a smaller size. The system builds an efficient path among the cluster heads. The geographic-based routing enables adaptive load balancing and it has the responsibility of selecting the relay nodes. Through clustering in the present work, scalability is greatly enhanced and reduces the network traffic. It also helps in preventing redundant messages from propagating over the network. In a clustering network, the cluster heads play a significant role, and hence, CH selection is an important task handled by the GWO algorithm. Traditional optimization algorithms lag in providing the appropriate solution within a specified time. Hence, the proposed work uses a meta-heuristic algorithm for optimization. The proposed hybridization of the GWO and dragonfly algorithm mainly intends to provide the best solution based on the fitness function calculated, which gives out the minimum number of clusters with extensible link quality, and the CH, selected dynamically, provides high remaining. This process is described using the equations explained in the next section. The GWO algorithm performs the selection of CH by calculating the fitness function. It categorizes the wolves into four types, namely alpha, beta, delta, and omega. The leader position is nominated to the alpha category of wolves. All these estimations are calculated using these equations. By considering this optimization algorithm, the alpha solution is the highly optimal solution. The next two best solutions are the beta and delta solutions. The given equation uses the GWO algorithm and represents the encircling phase of the model. \[\overset{\rightarrow}{X}(i+1)=\overset{\rightarrow}{x}_{p}(i)+\overset{ \rightarrow}{A}\times\overset{\rightarrow}{D} \tag{10}\]  
Equation (10) gives the encircling phase, where \(\overset{\rightarrow}{x}_{p}\) is the location of the prey, \(\overset{\rightarrow}{X}\) is the location of the wolf, and \(\overset{\rightarrow}{A}\) and \(\overset{\rightarrow}{D}\) are the coefficient vectors. Here, the prey is the target node and the wolf is the control center, and the data must be transferred between the node and the control center. The consecutive nodes will be further selected to facilitate the routing between the control center and the target node.