# Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
## 4 Simulations
### Results and Discussion  
This Section provides the simulated graphs and their discussion of the SDS-GIoT model and other related studies. The performance is evaluated for network throughput, latency, packet drop ratio, and the maximum number of rounds in a green IoT system. The proposed SDS-GIoT model remarkably improved green computing using the dynamic structure and the least overhead. It consumes less energy in sensing, aggregation, and transmission phases with the support of intelligent decision systems. The usage of multi-stage graphs explicitly shows a better outcome for the proposed sustainable solution in the light of dynamic programming. Moreover, the authentication and security operation also protect the green system by exploring lightweight cryptographic operations. The SDS-GIoT model maintains the efficiency of IoT resources in terms of energy constraints and increases its performance as compared to existing work. Unlike the majority of the existing work, which does not consider the intelligent approaches for the identification of suitable nodes and links in the data forwarding processes, to determine the most reliable circumstances and make the IoT system more sustainable, the proposed SDS-GIoT model makes use of dynamic programming. In Figure 4a,b, the performance of the SDS-GIoT model is compared with other work in network throughput. It can be defined as successfully transmitting sensor data packets to sink nodes. Based on the experiment's results, it is clear that the SDS-GIoT model greatly increased network throughput by an average of 22% for varying IoT devices and 27% for varying data generation rates. This is because the dynamic programming is by the SDS-GIoT model and identifies the most optimal routes for forwarding the IoT systems. Its link prediction significantly increases the chosen routes' strength and improves the system's performance. Moreover, the boundary of the edges collaborates with the sink node and reduces the security overhead for limited constraint devices. Thus, it balances the energy depletion for sensors and explicitly provides the most stable communication link for the delivery of data. The performance of the SDS-GIoT model is evaluated in comparison with existing approaches. Figure 5a,b illustrates the performance of network latency, and it has been noticed that as nodes and data generation rates grow, the latency ratio is also increased. According to the experimental findings, the SDS-GIoT model has improved the latency ratio by an average of 21% in terms of varying IoT devices and 24% in terms of varying data generation rates. It results from the effective cost function evaluation and extracts the robust routes from the multistage graphs. Additionally, dynamic programming chooses nearby states that place the least burden on communication and trains the system with timely delivery. By utilizing the blockchain and authentic techniques, the SDS-GIoT model eliminates the most malicious attacks on the links and accordingly provides efficient forwarding of IoT messages. Figure 4: Scenarios for varying IoT devices and data generation rates in terms of network throughput. (**a**) network throughput and IoT devices. (**b**) network throughput and data generation rates. The performance of the SDS-GIoT model in comparison to the existing approaches is shown in Figure 5(a,b). According to the research, the ratio of communication complexity also increases when the number of nodes and data generation rates have increased. However, the SDS-GIoT model improved the complexity rate by an average of 23% and 27% in terms of varying IoT devices and data generation rates as compared to other existing work. It is due to efficiently managing network resources and training the system using dynamic programming to avoid unreliable nodes. Additionally, the nodes are not frequently generated by the routing requests until and unless the energy level crosses the predefined threshold.