## 5 Performance Evaluation and Simulation
### Result of Simulation
In terms of latency, the DO\({}^{2}\)QIEO technique surpasses the OSGO method by 7.6% and the ODOC method by 9.1% with 14 cells. As a result, when there are more cells, the DO\({}^{2}\)QIEO performs better. This is because the offloading decision can be adjusted dynamically using the optimal approach proposed by DO\({}^{2}\)QIEO. Additionally, Figure 9a depicts the task failure rate in online mode after 1000 time slot simulations with different degrees of green energy supply. The study revealed that when the green power supply grows, the task failure rate lowers for all three approaches used in the experiment. This is because with a greater amount of green energy supply, backup power is prevented, and more servers might well be switched on, lowering both backup  
Figure 8: DO\({}^{2}\)QIEO technique performance in online mode (a) energy harvesting performance over time (b) delay of IoT tasks for given number of cells with EH. Figure 7: (**a**) The total working time of the system (**b**) and the system’s weighted cost for the given number of cells. power and delay costs. Furthermore, the DO2 QIEO has the lowest cost across all stages of the green energy supply. Figure 9b depicts the effect of computing task size on overall computational overhead in online mode ranging from 50 to 300 KB in all approaches; as the size of the calculation task grows, so does the total computational cost. The growing quantity in the ODOC, in particular, is greater than that in the other systems. It is because, unlike previous methods, ODOC performs its functions without the assistance of an energy harvester. Moreover, we could see that the DO2 QIEO has a lower total computational overhead than other techniques. It is, in particular, extremely near the result obtained using the OSGO algorithm. Additionally, we studied two computing situations to demonstrate the efficiency of the system in online mode, which also included local processing and offloading to the edge for IoT data processing. The IoT data are performed locally in the first case. In the second case, we use our settings to offload IoT data for execution on edge computing. The suggested technique was evaluated using a collection of sensor data files ranging in size from 50 kB to 250 kB. Additionally, every IoT data input is run ten times to generate an average result. As seen in Figure 10, we analyzed the outcomes using two performance metrics: processing time (a) and energy consumption (b). In the situation of task offloading to the edge node, the processing time comprises offloading time, execution time, downloading time, and so on. With each IoT data file size, the average processing time of local computation is greater than that of edge computing in Figure 10a. For instance, the offloading system could save up to 38% time when finishing the computation of a 50 kB file and up to 21% time when completing the computation of a 200 kB file, demonstrating the benefits of the offloading method. Because resource-intensive computational processes are offloaded to the edge, IoT data tasks require less energy when done using the offloading strategy, as shown in the results obtained for battery consumption in Figure 10b. Offloading the 150 kB file, for instance, takes below 16% of the energy of local computing. Additionally, when the data size increases, the energy utilization of the offloading system becomes more effective. While running a 150 kB and 200 kB file, for example, offloading the operation to the edge could save 19.33 percent and 21% energy, respectively, when compared to local execution. Figure 9: (a) Task failure rate performance with backup energy harvester module. (b) The impact of computing task size on total computational overhead in online mode. Finally, the cost compositions of the online and offline algorithms in 20,000-time slots are shown in Figure 11a,b. The online system considerably reduces costs by taking conservative actions at low battery levels and avoiding backup power.