## 5 Performance Evaluation and Simulation
### Result of Simulation  
In this experiment, we examine our strategy in various scenarios, taking into account practically every situation that can illustrate how our method is performed. We considered several scenarios in which our method was compared to other benchmarks, with or without modules such as the EH component or the TL. In addition, the method is compared in two benchmark methods in online mode and two more benchmark methods in offline mode. Besides, we are striving to demonstrate how our strategy functions in various scenarios to illustrate how our approach reacts. Therefore, criteria include delay, energy usage, computing overhead, task failure rate, system cost, and device efficiency. The proposed IoT offloading system's network performance is evaluated utilizing four edge devices. The task failure rate has been defined as the proportion of aborted computing tasks. For this reason, we compare the DO2 QIEO technique to the four methods listed below. However, we run the simulations in two parts (the system's offline and when the system turns to the online method). At first, we analyze the system in the offline method with two benchmarks (1) Energy-Efficient Dynamic Task Offloading computing (EEDTO) [34] and (2) Blockchain Storage and Computation Offloading (BSCO) [35]. Then, we provided performance evaluation in online mode with two other benchmarks: (1) Online Learning and Control for Green Offloading called (OCGO) [56]. (2) Online Distributed Offloading and Computing (ODOC) [57]. Experiments have revealed that the smallest amount of memory required for DO2 QIEO is 100 MB, which IoT nodes can readily backup. Figure 5a describes the convergence of the DO2 QIEO scheme in two modes. According to this Fig, the learning process takes roughly 1893 s to reach the best offloading techniques when TL is enabled and 2400 s when TL is disabled. It is worth mentioning that the DO2 QIEO technique in this part of the test is implemented offline in practice. Thus, the expected execution time has minimal bearing on the job offloading app's performance. Additionally, the average IoT offloading object efficiency for diverse cell densities is depicted in Figure 5b. As the number of cells increases, the performance of IoT objects degrades. The utility of the DO2 QIEO approach decreases as the number of cells increases. In addition, Figure 5c illustrates the average cost of the proposed approach with and without TL; in this mode, with increasing cells, the proposed method with TL surpasses the proposed method without TL. Therefore, this test demonstrates the significance of the TL, which plays a critical role in this scenario. Figure 6 describes the relationship between the number of cells and the performance of the network. According to Figure 6a-c, it could be seen that as the number of cells increases, so does the energy consumption, computation overhead, and task failure rate. Furthermore, when compared to other benchmark systems, the DO2 QIEO always has a  
Figure 5: The IoT device efficiency in different scenarios, over time slot (**a**), The IoT device efficiency over the number of cells (**b**), and average cost versus cell count using TL and without it (**c**). lower computation overhead. Even though incremental computation assignments use more server resources and larger blocks are created to handle more transactions, the computing overhead rises. Nevertheless, in the DO2 QIEO, the acceptable offloading decision, the selection of the cheaper servers, and the quick and efficient modification of block size greatly reduce the additional computation overhead. Assume the number of cells rises from 10 to 14. In that situation, the DO2 QIEO technique raises energy consumption, computation overhead, and the rate of IoT task failure by 16.43 percent, 26.36 percent, and 12.5 percent, respectively. With the number of cells equal to 14, the DO2 QIEO method outperforms the BSCO by 14.1 percent in terms of energy consumption, 17.81 percent in terms of computation overhead, and 16.42 percent in terms of task failure rate.