## 4 Results and Discussion
Considering Figure 5c, this figure reflects the efficacy of our proposed novel framework for the average hop distance to the sink metric with existing frameworks. It is a fact that if we are going to increase the SNs in the network, then there occurs a gradual decrement in the average hop distance to the sink. Here also, our proposed novel framework outrun the existing frameworks. Next, Table 5 shows the average % effectiveness of the proposed novel framework compared to the ABC-based framework and GA-based framework as 33.83% and 41.26%, respectively. Minimum RNs are utilized in the proposed novel framework for exploring the maximum number of node-disjoint routes with k-node-disjoint routes from each SN to BS. Since the average hop distance to the sink metric depends on the number of RNs, our proposed framework shows an efficient, steady performance compared to existing frameworks. The existing frameworks require a higher number of RNs, resulting in a higher average hop distance to the sink. Considering Figure 5d, this figure illustrates the fact that throughput increases with an increase in SNs. The reason is obvious: more SNs in the network try to send the sensed data to the BS using RNs as far as possible. In this entire mechanism, the existence of RNs plays a major role. The proposed novel framework outperforms the existing frameworks. Next, Table 6 shows the average % effectiveness of the proposed novel framework compared to the ABC-based framework and GA-based framework as 19.79%, and 33.71%, respectively. The proposed framework utilized the control random strategy with a non-uniform nature for deploying the RNs in EH-WSN. The minimum number of RNs is utilized in discovering the maximum node-disjoint routes in the network from all SNs to BS. Further, the most-reliable node-disjoint route is selected from each SN to BS. The proposed framework efficiently utilized the RNs and relieved the burden of SNs; in this environment, the energy level of the nodes does not deplete fast, which results in high throughput. The existing frameworks show poor results for the throughput metric. First, the existing frameworks do not have energy-harvesting capabilities. Second, the energy depletion rates of the nodes are higher in the existing frameworks. Category 2: The second set of simulation experiments were carried out under this category. We varied the network size and record the outcomes on the number of RNs, the lifetime of the network, throughput, and, finally, the average hop distance to the sink. Figure 6a-d illustrate the efficacy of the proposed novel framework (FRGNWS) with the existing frameworks, namely, the ABC-based framework and GA-based framework. Further, Tables 7-10 give more clarity on the effectiveness of the proposed novel framework by providing the average % effectiveness compared to the existing frameworks, considering each metric one by one, namely, the number of RNs, the lifetime of the network, throughput, and, finally, the average hop distance to the sink. Figure 6: The outcomes of the set of simulation experiments conducted with variations in the network size: (**a**) number of RNs with variations in the network size; (**b**) lifetime of the network with variations in the network size; (**c**) average hop distance to the sink with variations in the network size; and (**d**) throughput with variations in the network size. Tables 7–10 are given below. Considering Figure 6a, this figure clearly shows that our proposed novel framework again outperforms the existing frameworks for the number of RNs metric with variations in the network size. The network usually consists of nodes having a particular fixed range of communication; furthermore, if we are going to vary the network size, then, definitely, we require the deployment of extra RNs in order to effectively cover the entire network area. The proposed novel framework shows gradual increments in RN count and this reflects its efficient coverage capabilities.