## 4 Simulation Experiments, Results, and Discussion
### Explanation  
In CEC and PEM, only the propulsion energy consumption in UAVs is considered, whereas in EETO-GA, propulsion energy in UAVs as well as energy spent in task propagation (submission and series of subsequent resubmission in case of starvation) is considered. In the fitness function of the GA in our proposed scheme, we have included a number of tasks that could be successfully completed, and an increased number of tasks means, the opportunity of arrival of UAV has been utilized close to the fullest. Otherwise, another arrival from UAE would have been required to complete those pending tasks and those tasks would have to be resubmitted from member IoT devices to the respective CHs requiring more energy consumption in those devices. This would not only lead to an increased amount of energy consumption in UAVs, as well as IoT devices. Therefore, energy consumption in both UAV and IoT devices in our proposed scheme EETO-GA, is far lesser than than state of the art, namely, CEC, PEM, and AoI-TP, as shown in Figures 3 and 4.  
High energy consumption means a lesser lifetime for devices and exhausted IoT devices would not be able to submit all the jobs they intended to submit to the CH for possible execution by the UAV. Hence lesser the number of tasks that will be submitted in CEC, PEM, and AoI-TP compare to proposed EETO-GA. That is, more tasks will be executed by the EETO-GA than its competitors as shown in Figure 5. Another reason for the improvement of our proposed scheme EETO-GA is that it considers the criteria of starvation of tasks. Based on timestamps of generation of nonstarved tasks, an ARMA-model-based prediction of timestamps of generating a new task is prescribed by CH to its members in EETO-GA, so that the probability of starving is reduced. Reduction in starvation also contributes to energy saving in IoT devices otherwise tasks would have to be resubmitted again and again, especially when the number of IoT devices is as high as 700 or 900. For this reason, as the number of IoT devices increase, the improvement of EETO-GA in terms of energy consumption, over its competitors, become bigger and bigger as shown in Figures 3 and 4.  
The average delay in the execution of a task is also an important performance parameter. As the number of starving tasks and their serving times has been consciously reduced in EETO-GA, by increasing their priorities (maximum up to _Max_pr_), so the average time required for the execution of each of these tasks, is reduced as shown in Figure 6. Tasks are submitted at suitable times prescribed by autoregressive moving average on the ARMA model, for which chances of their starvation get reduced, decreasing their average delay. Although the proposed technique is not real-time, but it tried to minimize the average delay as much as possible, however, for this particular simulation study maximum acceptable delay was set to 20 s.  
Combining together all the Figures 3-6, we can emphatically say that EETO-GA outperforms all its above-mentioned competitors as far as the matrices like energy and delay concerned.  
Figure 4: IoT Device Energy Consumed (IoTDEC) in EETO-GA and Other State-of-the-art.  
Figure 3: UAV -Energy-Consumed (UAV-E) in Proposed EETO-GA and Other State-of-the-art.