# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 6 Results and Discussion  
This section provides the experimental discussion in terms of various network metrics. The evaluation metrics are considered throughput, packet drop ratio, propagation delay, and node overhead. In terms of network throughput, the proposed system performed better than the previous work, as shown in Figure 4a,b. The proposed system significantly increased network throughput by an average of 19% and 22%, respectively, as shown by the experiment's findings. This is because the proposed system uses graph theory and extracts the spanning trees based on the minimum cost. Moreover, the routes were updated by exploring the link lost rate and latency parameters. It provided the timely delivery of actual data packets with efficient bandwidth utilization. The proposed security algorithm also increased the system's strength using private keys and eliminated the false packets from the actual data. Figure 5a,b compare the performance result of the proposed system with related solutions in terms of propagation delay. It was observed that as nodes and data sizes increased, the propagation delay also increases. However, the experimental results show that the proposed system improved the propagation delay by 19% and 31% on average. This was due to the artificial intelligence approach and computing of the cost function for determining optimal routes. Furthermore, the subgraphs offered the shortest routes with link evaluation and led to a lower cost for the communication channels. Moreover, the proposed system minimized most harmful attacks from the IoT layer, and edge devices intelligently secured channels towards the SDN controller. Figure 6a,b illustrate how the SDS-GIoT protocol outperformed in contrast to other solutions in terms of the packet drop ratio. The performance results illustrate that when the number of nodes and data sizes grew, the packet loss fraction also rose. However, compared to other related work, the proposed system reduced packet loss rates by an average of 39% and 41%. This is because network resources are managed well, and the system is trained using graph theory principles. Moreover, the SDN controller continuously monitors the network flow and, whenever the performance is not satisfied, the SDN controller gives instructions to the edge device to reroute the data using various conditions. Furthermore, routing tables were updated, and those nodes were removed by the SDN controller, whose energy was less than the threshold. In comparison to the majority of the related work, the communication links were regularly monitored for latency and data loss. Whenever a harmful activity was carried out, the relevant warning messages were logged in the forwarding tables, and nodes were notified about the situation. The experimental findings of the proposed system are compared with other work in terms of nodes overhead, as illustrated in Figure 7a,b. It was observed that the node overhead increased as the number of nodes and data sizes increased. However, the proposed system balanced the energy load on the nodes and accordingly reduced the excessive overheads for the network. It was noticed that the proposed system improved the nodes' overhead by \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulation area & 5000 m \(\times\) 5000 m \\ \hline Sensors & 75–375 \\ \hline Data size & 10–50 KB \\ \hline Transmission power & 10 m \\ \hline Initial energy & 3–6 j \\ \hline Simulations & 15 \\ \hline Edge devices & 5 \\ \hline Gateway nodes & 10 \\ \hline Size of control packet & 512 bits \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. an average of 28% and 37%, respectively. This is because of the use of intelligent computing, which increased the stability of the IoT network with the collaboration of edge computing. Furthermore, the proposed system balanced network traffic load using SDN controller monitoring. The SDN controller had all the information about the network due to exploring the agents' capabilities.