## 4 History-Based Trajectory Planning  
The proposed technique SVM_UAV_ARMONT estimates the trajectory based on previous activities. As already mentioned in Section 3.2, attributes of the history table of the UAV are:  
1. region_id;
2. region_area;
3. session_id;
4. num_of_sensors;
5. detection_stat; \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Parameter** & **Value** \\ \hline C & 100.00 \\ class\_weight & None \\ HST\_tablesize & 500 \\ Cache\_size & 500 \\ coefo & 0.0 \\ decision\_function\_shape & ovr \\ degree & 3 \\ gamma & auto\_deprecated \\ kernel & rbf \\ max\_iter & \(-1\) \\ probability & False \\ random\_state & None \\ Shrinking & True \\ tol & 0.001 \\ ver\_bose & False \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Names and values of various simulation parameters of SVM. * tot_len_of_task_exec (it specifies the total length of tasks that are executed in that session);
* no_of_task_exec;
* \(\text{erng\_consumed}\);
* time_del;
* res_enrg_UAV. Suppose the UAV arrives over the CH \(C_{i}\), with residual energy res_en(i). The region_id is nothing but i, which is a unique identification of the region. Assume that \(C_{i}\) monitors a region with area AR(i) where the area of the large agricultural field is F. Up to now there was information about v number of sessions associated with \(C_{i}\), so the current session in (v+i) th session. num_of_sessions for the particular CH remains the same across all sessions. detection_stat in (v+i) th station is denoted by SVM. Where ARMA_prediction is the function that predicts task arrival rate in (v+i) th session based on tar(1), tar(2),..., tar(v). The number of tasks to be executed in the current session is the number of tasks present in task queue \(C_{i}\). It is readily available in the current session and does not have to be computed. Total energy \(entg_{i}(v+i)\) to be consumed in the current session is modeled as the average of \(entg_{i}^{1}(v+1)\) and \(entg_{i}^{2}(v+1)\) as shown in (1). \[entg_{i}(v+1)=\left\{ \begin{subarray}{c}\frac{1}{2}[\mathit{entg}_{i}^{1}(v+1) +\mathit{entg}_{i}^{2}(v+1)]\quad\mathit{if}\mathit{entg}_{i}^{2}(v+1)>\mathit{ enrg}_{i}^{1}(v+1)\\ \mathit{entg}_{i}^{1}(v+1)\quad\mathit{otherwise}\end{subarray} \right. \tag{1}\]  
where \(entg_{i}^{1}(v+1)\) is the straightforward computation i of energy from composite task length currently present in the task queue of \(C_{i}\), and \(entg_{i}^{2}(v+1)\) is ARMA model predicted consumption of energy, where prediction is performed based on actual energy consumed in previous sessions such as \(\mathit{en\_rg_{i}}(1),\mathit{en\_rg_{i}}(2),\mathit{en\_rg_{i}}(3),\ldots, \mathit{en\_rg_{i}}(v)\). \[entg_{i}^{1}(v+1)=\mathit{ent\_unit\_len\_task}(i)\times\sum_{j=1}^{|\mathit{ task\_queue}(i)|}length(task_{i}(j)) \tag{2}\]  
where \(task\_que(i)\) is set of tasks in task queue of \(C_{i}\) and, therefore, \(|\mathit{tsk\_queue}(i)|\) is number of tasks in task queue of \(C_{i}\). So, \(task_{i}(1)\) is the first task in task queue \(\mathit{tsk\_queue}(i)\) of \(C_{i}\). Similarly, \(task_{i}(2)\) is the second task of \(\mathit{tsk\_que}(i)\) of \(C_{i}\) and so on. In general, we can say that \(task_{i}(j)\) is the j-th task in the task queue of \(C_{i}\) where \(i\leq j\leq leg[\mathit{ts\_que}(i)|]\), \(\mathit{length}(\mathit{task}_{i}(j))\) specifies the length of the task denoted by the j-th task in the task queue of \(c_{i}\). So \(\sum_{j=1}^{|\mathit{tsk\_queue}(i)|}length(task_{i}(j))\) indicates the simulation of lengths of all the tasks in the task queue of \(C_{i}\). \(\mathit{en\_unit\_len\_task}(i)\) is the amount of energy that may be required to finish a unit-length task by \(C_{i}\). So, \(entg_{i}(v+1)\) is the total energy that will be required to complete all tasks in the task queue of \(C_{i}\).