## 5 Results and Discussions  
This section is dedicated to discussing the numerical results and analyzing the performance of the ECSO-OBL algorithm for sensor node deployment and their coverage. The proposed algorithm and its counterparts are implemented in MATLAB version 2024b 64-bit, and the computing device specifications are reported in Table 2. The efficiency of the ECSO-OBL algorithm is validated through a performance comparison in terms of coverage rate, number of iterations, and computational time. Table 3 provides specific network parameters and values for the network coverage simulation experiments. The network size and dimensions are varied in various cases, as presented in Table 3, and the communication radius is set to least twice the sensing range. For the performance comparison, the initial parameters of the various algorithms and their values are also defined as shown in Table 3. _Coverage area_ can be defined as the sum of the joint sensing probabilities of all sensor nodes in the WSN. _Coverage rate_ is computed as the \begin{table}
\begin{tabular}{c c} \hline Processor configuration & 12 th Gen Intel Core i7-12700, 2.1 GHz \\ \hline Operating system & Windows 11 (Microsoft) Enterprise \\ System model & HP Pro Tower 400 G9 PCI \\ System type & x64-based PC \\ Physical RAM & 16 GB \\ \hline \end{tabular}
\end{table} 
Table 2: Hardware specifications of the computer system used for conducting simulations. ratio of the coverage area to the total area of network terrain. The _coverage ratio_ in the WSN is defined as the ratio of the total covered area in the ROI to the dimensions of the ROI, as given in the following equation. The maximum value of coverage ratio is defined as the objective function in the proposed algorithm, and it is given in Equation (17). \[F_{Objective}=maxC=\frac{\sum_{i=1}^{N}P\big{(}S,T_{j}\big{)}}{W\times L} \tag{17}\]  
where \(W\times L\) is the area of the ROI and \(P\big{(}S,T_{j}\big{)}\rightarrow\) probability that the given target is covered by one or more sensor nodes. The ECSO-OBL based optimization experiments were conducted on eight test functions, as shown in Table 4, and it was proved that the proposed algorithm improves optimization in terms of dimensionality, stability, and convergence accuracy and speed. Figure 4 represents the sensor nodes' random distribution before and after ECSO-OBL optimization. It is very clear that the coverage holes are eliminated to the maximum possible extent and that the redundancy in the coverage area is minimized compared with the other recent algorithms under the same test conditions. To evaluate the feasibility of the ECSO-OBL algorithm, eight benchmark functions are selected, as shown in Table 4. Figure 5 represents the average convergence curve of the ECSO-OBL algorithm for the benchmark functions, independently optimized in 30-dimensional space, and the best function value is defined. It is observed that the convergence curve shows significant differences over the iterations and is quick at converging. The fitness function is horizontal at certain iterations, and overall, it has a ladder shape. In the existing coverage algorithms, the fitness function is not derivable under the boundary conditions, and it is difficult to find the global optimal solution. The stepwise decrease in the fitness function indicates that the ECSO-OBL algorithm fell into local optimal solution multiple times because each dimension has been updated one by one. However, the integration of the ECSO-OBL algorithm with PSO leads to the best optimal and stable results. It is observed that the adaptive mutation probability of ECSO-OBL balances its global and local search abilities. The solution space in the existing algorithms is highly complex at higher