## 4 Results and Discussion
Although the network lifetime gradually decreases with an increment in the network size, due to the higher consumption of energy, the proposed novel framework still shows excellency in these circumstances. Multiple objective functions are considered while designing an efficient, novel fitness function for selecting the most-reliable node-disjoint route from each SN to BS, namely, the remaining available energy of each node in the route, the distance of the edge between adjacent nodes in the route, the energy consumption of the edge between adjacent nodes in the route, delay in communication for the node in the route, relay hops for the packet in the route, and finally the reliability index (RI) of the particular route. Therefore, energy consumption is lower in the proposed novel framework when compared with the existing frameworks. Considering Figure 6c, this figure elaborates the fact that the average hop distance to the sink increases with an increase in the network size, since the large network size requires more RNs to effectively cover the entire area. The proposed novel framework dominates the existing frameworks for the average hop distance metric. Table 9 exhibits the average % effectiveness of the proposed novel framework compared to the ABC-based framework and GA-based framework as 32.49% and 43.40%, respectively. The proposed novel framework is has excellent coverage capabilities. The minimum number of RNs is utilized for exploring the maximum number of node-disjoint routes from all SNs to BS. The existing frameworks do not have effective coverage capabilities and a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Throughput (Kbps) with Variations in the Size of the Network**} & \multicolumn{3}{c}{**\% Effectiveness of Proposed FRONWS Framework with**} \\ \hline \multirow{2}{*}{ \begin{tabular}{c} **Size of** \\ **Network \(\downarrow\)** \\ \end{tabular}  } & **GA-Based** & **ABC-Based** & **FRONWS** & \ \begin{tabular}{c} **\% Effectiveness of FRONWS** \\ **Framework with** \\ **ABC-Based Framework** \\ \end{tabular}  & \begin{tabular}{c} **\% Effectiveness of FRONWS** \\ **Framework with** \\ **GA-Based Framework** \\ \end{tabular}  \\ \hline
400 \(\times\) 400 & 2500 & 2600 & 2800 & 7.14 & 10.71 \\ \hline
600 \(\times\) 600 & 2800 & 3120 & 3650 & 14.52 & 23.29 \\ \hline
800 \(\times\) 800 & 3100 & 4000 & 4980 & 19.68 & 37.75 \\ \hline
1000 \(\times\) 1000 & 4050 & 4930 & 5760 & 14.41 & 29.69 \\ \hline
1200 \(\higher number of RNs are required for covering large network areas. Therefore, existing frameworks result in a higher average hop distance to the sink. Considering Figure 6d, this figure displays the fact that throughput increases with an increase in the network size, and our proposed novel framework outrightly performs well, considering the throughput metric compared to existing frameworks. Table 10 exhibits the average % effectiveness of proposed novel framework compared to the ABC-based framework and GA-based framework as 16.05% and 26.71%, respectively. Throughput depends on various factors in the network, but the primary factor is the remaining energy level of the nodes in the network. This factor is effectively considered in the proposed novel framework while selecting the best reliable route from each SN to BS. The proposed novel framework shows energy efficiency, and therefore the throughput gradually increases with an increment in the network size, even though a large network area results in a higher consumption of energy. The existing frameworks lack energy efficiency, and therefore show poor results when comparing with the proposed novel framework. Category 3: The third set of simulation experiments were carried out under this category. We vary the harvested energy supply uniformly and also randomly both ways. Further, we recorded the outcomes on the lifetime of the network metric. Figure 7a,b illustrate the efficacy of the proposed novel framework (FRGNWS) with the existing frameworks, namely, the ABC-based framework and GA-based framework.