## 5 Performance Evaluation and Simulation
### Result of Simulation
It outperforms the EEDTO method by 9.42 percent in terms of energy consumption, 8.91 percent in computation overhead, and 12.2 percent in task failure rate. Therefore, when there are more cells, the DO2 QIEO performs better. This is because the offloading decision can be dynamically adjusted using the optimal strategy trained by DO2 QIEO. The increasing computation tasks are typically offloaded to the local edge node to relieve the MC. The greater the workload, the greater the energy savings from offloading computing. Therefore, energy consumption is effectively reduced. Figure 7a represents the total network working time following various methods. As shown, the system working time lowers significantly as the quantity of cells increases. The DO2 QIEO, on the other hand, outperforms other benchmarks, as evidenced by the extended system working time. A probable explanation is that as the number of cells increases, the computation tasks become more difficult, putting additional strain on the controllers. In addition, the agent prefers to offload tasks rather than execute them locally to save energy. As a result, the system's operating time is prolonged. The system weighted cost is made up of the device's system computing overhead as well as its energy usage. In addition, Figure 7b demonstrates the relation between the system's weighted cost and the number of cells under different schemes. Figure 7 shows that the weighted system cost grows significantly as the number of cells rises in all plans. Because of the combined optimization of the controller selection, block size, offloading choice, and server selection, the DO2 QIEO always outperforms alternative baseline methods that just optimize a subset of the optimization elements. Furthermore, due to its capacity to adapt to large-dimensional optimization issues, the DO2 QIEO outperforms EEDTO and BSCO in terms of effectiveness and stability. We proposed a secure framework for integrating edge and blockchain technologies into IoT networks to ensure data security and energy efficiency. The optimization problem was then designed as an MDP by specifying the state, action, and reward function simultaneously, as well as the dynamic features of IoT systems, while taking into account the computing servers and the status of the controllers. Therefore, we developed a platform for dynamic online/offline offloading for various IoT-edge applications that used the TL method to help achieve the best offloading strategy. The EH module was used in our  
Figure 6: Consumption of energy (**a**), Computation overhead (**b**), and IoT Task failure rate (**c**) for the given number of cells. method to extend the battery's life and improve offloading performance. Results demonstrated superior performance to cutting-edge methods in terms of energy consumption, computational latency, device efficiency, and task failure rate. Therefore, we built three environmental states in our network: E = Low, Medium, and High. E = Low marks the time six pm to six am; E = Medium marks six am to nine am and three pm to six pm, and E = High marks the time nine am to three pm. We generate the environmental states and derive their related renewable power distributions using both wind and solar real-world green power harvesting traces. In addition, Figure 8a depicts the energy harvesting path over five days. The delay performance after a 20,000-slot simulation for different maximum numbers of edge nodes in online mode, as estimated automatically by simulation environments, is depicted in Figure 8b. As the quantity of cells rises, the delay for all techniques decreases. Additionally, the DO\({}^{2}\)QIEO method is faster than all benchmark methods. The suitable offloading choice, the choice of the cheaper servers, and the faster and more effective modification of block size considerably decrease the additional computing overhead in the DO\({}^{2}\)QIEO in online mode. Suppose the cell count rises from 6 to 10. In that situation, the DO\({}^{2}\)QIEO technique reduces time by 22%.