## 3 Materials and Method
### System Architecture  
The system architecture consists of a large irregularly shaped rectangular field that is divided into some regions based on soil quality, types of crops, etc. Different criteria with each region containing one or more sensor nodes as shown in Figure 1. One region comprises one or more non-intersecting geographical areas and sensor nodes are deployed in such a manner that each sensor node monitors one such geographical area within a region. These sensor nodes capture pictures of different regions at different interval times and send those to the head of their respective clusters to form batches of processing requests or tasks. There is only one UAV in the system, as shown in Figure 2, which flies over the cluster heads (CHs) and as soon as the UAV arrives over one particular CH, the CH sends the task of the task queue in batches; the UAV executes these tasks, that is, analyzes  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol/Abbreviation** & **Brief Description** \\ \hline UAV & Unmanned Aerial Vehicle. \\ SVM & Support Vector Machine. \\ PSD & Percentage of Successful Detection. \\ CEC & Composite Energy Consumption. \\ ADL & Average Delay. \\ SVM-UAV-ARMONT & SVM and UAV-Based Aerial Monitoring. \\ GCS & Ground Charging Stations. \\ ACO & Ant Colony Optimization. \\ SN-UAV & Sensor Nodes of UAV. \\ A*GA & A*-Based Genetic Algorithm. \\ MULTI-UAV & Multiple Regions with One UAV. \\ CH & Cluster Head. \\ HST & History Table. \\ SRF & Sensitive Regions First. \\ region-id & Denotes the Unique Identification Number of the Region. \\ region-area & Specifies its Area of the Specific Geographical Region. \\ Session-id & Unique Identification of the Session. \\ Num-of-sensors & The Number of Sensors Deployed in Hat Region. \\ detection-state & Acquires a Value of 1 if Something Unnatural is Detected in the Region. \\ Task-arrival & The Number of Tasks that are Currently Arriving in the Task Queue of the \\  & Current Node Per Unit of Time. \\ No-of-task-evec & The Number of Tasks that are Currently Executing from the Task Queue of the \\  & Current Node Per Unit of Time. \\ \
\begin{tabular}{c} enrg\_consumed \\ res\_enrg\_UAV \\ time\_del \\ \end{tabular} & Energy that Remained in UAV after Executing. \\ Parameter C & The Amount of Time Required by the UAV to Execute these Tasks. \\  & Specifies the Penalty Parameter or the Error Term. \\  & Task Queue. \\  & length(task()) & Indicates the Simulation of Lengths of all the Tasks in the Task Queue. \\  & The Amount of Energy that May be Required to Finish a Unit-Length Task. \\  &
\begin{tabular}{c} del\_unit\_len\_bsk() \\ res\_eng \\ \end{tabular} & The Amount of Time Delay Required for Executing a Unit-Length Task. \\  & & Residual Energy. \\  & AR & Region with Area. \\  & W(Cap) & Weight of Cap. \\  & AR & Region with Area. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols, abbreviations, and their descriptions.  
all the images and takes action accordingly. This is initially performed as per [PERSON]'s shortest path algorithm until and unless each CH is visited at least ten times and the UAV stores records about all these CHs and members. These records form a history and based on this history, certain regions are classified as sensitive while others are considered insensitive. Sensitive regions are subsequently visited more frequently by the UAV, that is, their temporal resolution increases, while insensitive regions are visited less frequently to improve the percentage of successful detection. Classification of regions is performed as sensitive or insensitive based on SVM.