## 5 Simulation Results and Discussion
### Simulation Setup  
Table 3 presents the summarized values of all simulation parameters setup that are used in this experiment. In SN_UAV, all sensor nodes have UAV capability embedded. However, when the agricultural field is large and a lot of sensors need to be deployed, then the associated cost will be very high. Additionally, there is no application of the concept of delayed processing of batches. Therefore, for each abnormality detected in the field, the corresponding sensor-cum-UAV has to process the task individually and fly. During the time it flies, some of the events will miss detection, leading to decreased PSD, especially when the number of sensors is very high, i.e., the agricultural field monitor is very large in size, for which a huge number of sensors have to be deployed. Moreover, since the task of UAV is performed, energy consumption is much higher. A8_GA has to be performed as well at ground stations and the Huge_no_of estimated task means more abnormalities detected. During this research, sensor-cum-UAV nodes cannot sense the environment and miss some of the detection. As far as A*-GA is concerned, a genetic algorithm is used for planning the UAV trajectory when only energy consumption is considered in the fitness function. The next destination A*-GA is the node to go to which minimum energy is consumed by the UAV. However, it does not differentiate between the areas where most of the abnormalities have been detected in the recent past (sensitive regions) and the areas where no abnormality has been detected recently (insensitive regions). UAVs should spend most of the time in sensitive regions, which is performed in SVM_UAV_ARMONT but not its state-of-the-art competitors SN-UAV and A*-GA. Giving equal importance to both sensitive and insensitive regions is a waste of both energy and time. With increased exhaustion of the UAV, it will have to be recharged frequently and during recharge, it will expectedly miss detection of some abnormalities, as shown in Figure 5. Additionally, the energy consumption of SN-UAV and A8-GA will be much higher than SVM_UAV_ARMONT as shown in Figure 6. Along with that, the time efficiency of our proposed scheme is graphically presented in Figure 7. SVAM_UAV_ARMONT chooses the next CH destination based on energy consumption, \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Parameter** & **Value** \\ \hline Coverage area & \(300\times 300\) m\({}^{2}\) \\ \hline No. of regions & \(10\) \\ \hline Min no. of devices in a cluster & \(3\) \\ \hline Max no. of devices in a cluster & \(20\) \\ \hline Number of IoT devices & \(100\), \(300\), \(500\), \(700\), \(900\) \\ \hline Number of UAVs & \(1\), \(3\), \(5\), \(7\), \(9\) \\ \hline Hovering altitude of UAV & \(6\) m \\ \hline No. of CPU cycles/s in an UAV & \(0.5\)G cycles/s \\ \hline UAV speed & \(20\) m/s \\ \hline Transmission bandwidth & \(150\) khz \\ \hline Channel gain & \(-20\) dB \\ \hline Noise power & \(-50\) dBm \\ \hline Initial value of re-classification interval & \(1\) h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Names and values of various simulation parameters. delay, amount of work that might have to be done in the new destination, and the amount of area covered by it. The destination that covers a huge area with a huge estimated number of tasks with less energy and delay is preferred the most. The huge number of the estimated task means more abnormalities are detected. In brief, the proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively, over the state of the art. Another reason behind the performance enhancement of our proposed scheme is the adaptive adjustment of the re-classification interval. After performing re-classification for the first time, if there is a change in the sensitive/insensitive label of at least one region, then the re-classification interval is halved, otherwise, it is doubled.