## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Avg. Hop Distance to the Sink with No. of SNs**} & \multicolumn{3}{c}{**\% Effectiveness of Proposed FRCNWS Framework with Existing Frameworks**} \\ \hline \multirow{2}{*}{**No. of SNs \(\downarrow\)**} & **GA-Based** & **ABC-Based** & **FRGNWS** & **\% Effectiveness of FRGNWS** & **\% Effectiveness of FRGNWS** \\  & **Framework** & **Framework** & **Framework** & **Framework with ABC-Based Framework** & **GA-Based Framework** \\ \hline
25 & 12 & 11.5 & 8.5 & 26.09 & 29.17 \\ \hline
35 & 11 & 10 & 7.5 & 25.00 & 31.82 \\ \hline
45 & 9.5 & 8.5 & 6.5 & 23.53 & 31.58 \\ \hline
55 & 8.5 & 7.5 & 4.5 & 40.00 & 47.06 \\ \hline
65 & 7.5 & 5.5 & 2.5 & 54.55 & 66.67 \\ \hline \multicolumn{2}{c}{Avg. \% Effectiveness\(\rightarrow\)} & \multicolumn{3}{c}{33.83} & 41.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The average % effectiveness of the proposed novel framework for the average hop distance to the sink metric with variations in the number of SNs. Now, we will explain the reason for this outstanding performance of the proposed novel framework. The proposed novel framework utilized a hybrid adapted grey wolf sine cosine optimizer (HA-GWSCO) framework with a properly designed objective function aimed at exploring the maximum number of node-disjoint routes from all SNs to BS using the minimum number of RNs. The proposed novel framework also ensures k-node-disjoint routes from each SNs to BS with k \(\geq\) 2. The ABC-based framework considers the two factors, namely, cost and connectivity. However, this framework lacks consideration of the energy depletion rate. Therefore, the ABC-based framework results in more RNs when compared with the proposed framework since additional RNs are required in the case of disconnected SNs due to energy-depleted RNs. On the other hand, the GA-based framework considers the connectivity factor, but this framework lacks a consideration of the energy level along with coverage issues. Hence, the GA-based framework also results in more RNs, since covering all SNs require more RNs. Furthermore, considering Figure 5b, we can infer from the figure that there is a gradual decrement in the lifetime of the network with an increase in SNs. The reason for this gradual decrement is that, whenever the increase in SNs occurs in the network, there are a few specific activities that also start increasing in the network that consume lots of energy, namely, the retransmission of packets, synchronizations, and high traffic between nodes. These activities result in a high energy depletion rate of the nodes and, consequently, the lifetime of the network suffers. Again, the proposed novel framework surpasses the existing frameworks. Table 4 shows the average % effectiveness of the proposed novel framework compared to the ABC-based framework and GA-based framework as 27.83% and 43.76%, respectively. The proposed novel framework has two phases: the first phase discovers the maximum node-disjoint routes from all SNs to BS using the minimum RNs having k-node-disjoint routes from each SN to BS, therefore providing fault-tolerant capabilities; the second phase is aimed at selecting the best reliable route out of the available k-node-dis joint routes from each SN to BS, hence enhancing reliability in the network, and therefore the possibility of selecting the faulty route is low. Moreover, the routes from each SN to BS utilized the lowest number of RNs, and therefore less consumption of energy as compared with existing frameworks, resulting in an efficient lifetime of the network. Further, the ABC-based framework does not consider the energy-depletion rate of the nodes; the GA-based framework also does not consider the energy factor, instead focusing on the connectivity factor. Further, the existing frameworks do not have the capabilities of energy harvesting, so if the level of energy of the nodes is below a particular level, then network failure will start.