## 3 Materials and Methods
### Hybrid K-Medoids with IABC-Based CH-Selection Algorithm  
The proposed K-IABC method employs the ABC algorithm with k-medoids to improve exploitation and exploration rates during CH selection. Algorithm 1 shows algorithm for clustering using k-medoid with IABC. The k-medoid-based usage parameter in the suggested K-IABC method avoids the ABC algorithm's traditional drawback of rapidly falling into the local optimum for CH selection, and is depicted in Figure 3.  
``` Initialization of network: Step 1: Initialize the sensor network. Step 2: Locate BS at coordinate. Step 3: Place all the SNs randomly. Cluster formation using k-medoid and selection of CH using IABC: Step 4: Number of nodes, N, is divided into number of clusters, C. Step 5: Every cluster has N number of nodes and each node is related to its nearest CH. Step 6: Randomly select the first CH by selecting a random first medoid from N in C cluster. Step 7: A three-dimensional coordinate (x, y, z) is generated by every normal node for CH. Step 8: K-means distance calculation is performed by CH. Step 9: Improved-ABC-optimization algorithm is used to select the new CH. Step 10: Repeat steps 7 to 9 until the node in the real center is found. EndThe proposed IABC scheme plays an essential role in optimizing the election of the CH process by leveraging properties derived from intelligent-bee-hunting behavior. To identify the viable CH, the employee, onlooker, and scout-bee stages were integrated into the suggested K-IABC protocol. We used search-space partitioning to generate the initial population, and used new search equations in the employed-bee and onlooker-bee stages to increase the chances of the onlooker bee finding a better solution, and some worst-case positions. With a new position based on the best non-updated position, Scoutbee has been improved. For initialization, the population is generated based on the search space partitioning (SSD) proposed by [PERSON] et al. [24]. In the employed-bee stage, we enhance the ABC search formula and gradually use the information of the optimal solution to speed up the search. In the observation-bee phase, 25% of recruited bees were selected with equal probability to perform additional search actions. When many optimal solutions are found to be multidisciplinary, the current optima- solution information and the number of optimal solutions are used to provide a long-distance move, replacing the 10 worst positions with 5% new positions to build the function. Figure 3: Overall process of the K-IABC algorithm. ### Corp

The cross-layer based opportunistic-routing-protocol (CORP) algorithm increases the stability of communication networks and reduces delays. Conventional cross-layer routing protocols have some flaws. These are as follows: (i) because WSNs have limited computing power, the computational complexity of data transmission increases with the number of limitations; (ii) they are challenging to integrate, and (iii) they consume a lot of power. The proposed CORP technique addresses several existing cross-layer routing-protocol shortcomings in communication delay, energy consumption, throughput, and packet-loss rate. Initialization, deployment, CH selection, and transmission of data to the receiver are the steps required in this work to coordinate the proposed protocol's algorithm [16]. Because CH nodes are based on residual-power data, the network layer chooses nodes with higher energy. In routing, the network system uses the physical layer's remaining power data and the link layer's link-quality instructions to generate routing options and select the best path. The network layer uses routing to identify the next hop node to the physical layer before forwarding data. To ensure network connectivity, the physical layer calculates the effective transmission energy and transfers this data with a lower transmit power. The link layer employs error-control techniques to confirm data transmission based on the channel status and link quality.