# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 4 Simulation Results  
The proposed work was simulated using MATLAB 2018a. The list of simulation parameters is given in Table 1. The associations of the \(\lambda\) values in the first experiment are shown with the targeted parameters. The placements of 100 nodes can be seen in Figure 11. All the nodes were distributed evenly, while the location of the sink, labeled as BS, was kept at the center of the simulation space. We observed that the nodes were densely deployed in some places, while some nodes had low neighbor density according to their location. The nodes' placements highly affected the network throughput, especially the availability and lifetime of the routes. In the experiment scenario of Figure 11, the \(\lambda\) values for a sample of 12 nodes were derived, as shown in Table 2. The node that had an ID = 10 with a higher number of CNs had a comparatively higher \(\lambda\) value. This is because nodes with multiple CNs will get more route requests than others. Since their elimination from the network will not affect much due to the presence of multiple CNs, such nodes will be frequently utilized. Moreover, a node that had multiple CNs but less energy compared to its CNs had a lower value. For such cases, the node ID = 6 can be compared with the node ID = 7. Both had an equal number of CNs but different levels of energy. Therefore, different \(\lambda\) values were assigned to ID = 6 and ID = 7. Figure 12 shows a clear relationship among the nodes' energies, their CNs' energies, and the computed values of \(\lambda\). This work was further compared with some other protocols such as the PDORP, PRRP, DSR, and LEACH [44]. Experiments were performed to check the energy consumption, network life, throughput, and delays. Figure 13 shows the comparative results for energy consumption in all the experimented protocols. LEACH is not very sophisticated compared to modern protocols, but it is very famous for creating a baseline for other protocols. Many studies adopt LEACH as a base protocol for designing and comparing their work. In our results, its performance decreased with the increased number of nodes. The PDORP and PRRP obtained consistent results in terms of energy efficiency. The authors who developed the PDORP claimed to obtain encouraging results by using the genetic algorithm with a modified DSR. The PRRP was better than the DSR and LEACH but could not compete with the others. The key reason for this is the incorporation of a typical GPS in the nodes. The proposed mechanism did not perform well with a lower number of nodes, such as 50, because the NSSROP operates \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 50 to 250 \\ Area & \(500\times 500,750\times 750\), and 1000 \(\times\) 1000 m\({}^{2}\) \\ Max propagation & 100 meters \\ Max RSSI & 100 \\ CN threshold & 50 \\ Distance for identical nodes & 25 \\ Location of sink & 250, 250 \\ Energy max & 100 \\ Base protocols & DSR, OLSR \\ Node distribution & Random \\ Rx power & 0.6 W \\ Tx power & 0.6 W \\ Movement trace & Off \\ Comparisons & LEACH, DSR, PDORP, PRRP, NSSROP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of simulation parameters. on the scores that are based on the nodes' neighborhoods and densities. In the case of a smaller number of nodes, there were fewer or no CNs and identical CNs. Therefore, the proposed mechanism failed to obtain distinctive features from its key parameters with a lower number of nodes. However, a WSN-based IoT network mostly consists of a large number of devices. In such a dense network, therefore, the NSSROP worked much better than the other protocols; the NSSROP outperformed the other protocols with a number of nodes greater than 100.