## 2 Literature Review
### Related Works
An essential component of Wireless Sensor Networks is energy usage (WSNs). In our approach, which is based on the LEACH (Low-Energy Adaptive Clustering Hierarchy) routing protocol and the neural network's tool, we introduce the criterion of the consumed energy for the process of the election of the Cluster-Head (CH). In 2022, [PERSON] et al. [PERSON] et al. (2022) created a group with neighboring sensor nodes, in which the clustering process extends the lifespan of WSNs. For handling the data transfer from the other clustering groups to the fundamental sensor nodes, a principal node known as the cluster head is chosen from the group. Additionally, the cluster heads are positioned to send the signal gathered from its sensor nodes to the base stations. Many WSN applications find it difficult to select an ideal node to serve as a cluster head. The neural network algorithm in the proposed work is used to select a cluster head by examining the base station distance, neighbor sensor counts, and residual energy. In 2021, [PERSON] et al. [PERSON] et al. (2021) provided a method for finding MNs in WSN using the parameters of each SN. In addition to addressing security, this innovation also made data transmission (DT) energy-efficient by selecting a Cluster Head (CH) that was centered on the sensor's remaining energy. In the Malicious Nodes Detection (MND) phase, the Improved Deep Convolutional Neural Network (IDCNN) locates the MN and isolates it into the malicious list box. The Extended K-Means (EKM) algorithm clusters the Trusted Nodes (TN) in the energy-efficient DT phase, and the t-Distribution-based Satin Bowerbird Optimization (t-DSBO) algorithm chooses a single CH for each cluster that is centered on the residual energy of those nodes. Through the CH, the cluster's data are sent to the base station (BS). In 2022, [PERSON] et al. [PERSON] et al. (2022) deployed energy-conscious data gathering in robot network clusters. Due to its limited battery life, a UAV collects data from CH robots by visiting a subset of them, whereas a cluster head (CH) robot in each cluster assigns one collaborative job to each cluster member (CM) robot and collects data from the CMs. A UAV's decision to visit a subset of CHs is based on several considerations, including the battery's remaining capacity as well as the locations and data quality of each CH robot. CH robots serve as relay nodes for data transmission for non-visited CH robots. In 2020, [PERSON] et al. [PERSON] et al. (2022) incorporated visiting an optimal number of CH robots while minimizing the total energy consumption of the UAV and the cost of data collection from CH robots. The UAV determines which subset of CH robots to visit by taking into account both its battery capacity and the locations of all CH robots. If the UAV is unable to visit every CH robot, the CH robots that the UAV does not visit relay their data to another CH robot, which then forwards it. The cost optimization process includes choosing the transmission channels for transmitting robots. By taking into account the continuous battery capacity for the UAV, our contribution goes beyond the prevailing paradigms in the literature. Table 1 exhibits the conventional routing models in WSN. Initially, the FCR used in [PERSON] et al. (2022) conserved energy in a capable manner with negligible delay; however, computational time was not examined. The RCSO used in [PERSON] et al. (2022) offered improved throughput with negligible delay, but cost metrics must be considered. The MOFPL used in [PERSON] et al. (2022) offered high network energy with lesser simulation time, but resource management is not analyzed here. In addition, the Taylor KFCM used in [PERSON] et al. (2022) provided high throughput with negligible delay; nevertheless, there was no deliberation on real-time experiments. Likewise, the FF-based model used in [PERSON] et al. (2022) improved EE efficiently with enhanced EE. However, FF suffered from local search issues. In addition, the MEACBM model used in [PERSON] et al.