## 4 Comparative Analysis and Results
It  
Figure 5: Comparison throughput analyses. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Various Methods and** & **500** & **1000** & **1500** & **2000** & **2500** \\ \hline CHIRON & 0.3 & 0.9 & 1 & 1.1 & 1.1 \\ \hline LEACH-C & 0.5 & 1 & 1.5 & 1.7 & 1.7 \\ \hline Optimal-CBR & 0.9 & 1.8 & 2.4 & 2.5 & 2.4 \\ \hline K-mean & 0.7 & 1.5 & 2.1 & 2.1 & 2.1 \\ \hline Proposed & 1 & 1.9 & 2.5 & 2.8 & 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Differentiation of throughput in several methods. can be observed that the transmission rate reduces with the mobility. The earlier research models are lower than the proposed model and SEORMP model. The amount of data transfer that occurs shows the ability of the model to provide stability for a longer lifetime. For the proposed model, the transmission rate was higher at the initial stage, where the mobility was 1 m/s, and it was measured to be around 99.30, which is similar to the model EATSRA [27]. During the packet transmission, the status of nodes is mobile and there is a decrease in the energy level. According to many researchers, every node will maintain a routing table to recognize the transmitted node or nearby node. The maintenance of the routing table to create an effective protocol is the greatest challenge in WSN. Therefore, this comparative analysis is carried out to find an optimal routing protocol. The optimization of the routing protocol is carried out with the aid of the gravitational search technique, considering indirect and direct trust in nodes and concern over energy saving. This routing approach is termed ETGSA (Energy-Aware Trust-Based Gravitational Search Approach). It is proved that the ETGSA algorithm maximizes the reliability to resist certain attacks in a network and decreases the computing overhead, and NS-2 is used for the simulation process [28]. With the help of the DT algorithm and the spatio-temporal constraint, wireless sensor networks can identify malicious users and choose safe paths. This algorithm develops security and a suitable delivery ratio, and to choose efficient decisions, certain kinds of constraints called spatio-temporal constraints are introduced in this model [27]. After the experimental analysis, the overall production of all systems is presented graphically in Figure 6. The ratio of transmission rate in a packet along with motility speed is calculated, and this criterion is analyzed with many systems and the proposed approach. Among existing approaches, the mobility of a node in the path construction is available in the Secured Energy Optimal Routing with Mobility Prediction (SEORMP)  
Figure 6: Comparison of packet transmission rate depending on mobility speed. approach. By analyzing the total trust of a node and its instant trust, SEORMP builds an efficient and secure route. The energy consumption in a node is modified after the packet transmission maximizes throughput and reduces the time delay in the packet transmission. However, the proposed method, in terms of packet transmission rate, performs better than the SEORMP technique. Many available studies, while suggesting an optimal path, do not take the mobility speed into account. On the basis of security concerns, a path is created given the path and energy level in a node. If mobility is considered, then automatically, time concern is dispersed. The comparative analysis of several systems along with the proposed system in terms of the transmission rate of the packet depending on the speed of mobility is categorized in Table 6.  
Table 6 shows that the packet transmission rate of the proposed system is higher than other methods such as Energy-Aware Trust-Based Gravitational Search Approach (ETGSA), Taylor-Based Cat Salp Swarm Optimization Algorithm (TC-SSA), Enhanced Fuzzy C-Means and Adaptive TDMA Scheduling (ECATS), and Energy-Aware Trust-Based Secure Routing Algorithm (EATSRA).