## 3 System Model
### Ground Sensors Energy Model  
It is assumed that \(\mathcal{S}\) has two states namely active and passive. \(E_{sl}\) and \(E_{ac}\) are the energies consumed in the passive and active states, respectively. Therefore, the total energy consumed \(E_{t}\) is given in Equation (1) [64]:  
\[E_{t}=E_{sl}+E_{ac} \tag{1}\]  
\[E_{sl}=P_{sl}\times T_{sl} \tag{2}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Authors** & **Env * & **Int *** & **M** & **Intg *** & **Supported** & **UAV Data** & **UAV Energy** & **U2U * \\  & **S/R *** & **Det *** & **L *** & **Env *** & **Application** & **Collection** & **Model** & **Com * \\ \hline [PERSON] [22] & R & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Monitoring System & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [23] & R & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Vineyard Monitoring & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [29] & R & \(\times\) & \(\checkmark\) & \(\times\) & Weed Detection & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [31] & R & \(\times\) & \(\checkmark\) & \(\times\) & Crop Monitoring & \(\checkmark\) & \(\times\) & \(\times\) \\ \hline [PERSON] [32] & R & \(\times\) & \(\times\) & \(\times\) & Intelligent Monitoring & \(\checkmark\) & \(\times\) & \(\times\) \\ \hline [PERSON] [38] & S & \(\times\) & \(\times\) & \(\times\) & Delay Aware & \(\checkmark\) & \(\times\) & \(\times\) \\ \hline [PERSON] [40] & S & \(\times\) & \(\times\) & \(\times\) & Intelligent Monitoring & \(\checkmark\) & \(\times\) & \(\times\) \\ \hline [PERSON] [41] & S & \(\checkmark\) & \(\checkmark\) & \(\times\) & Classification System & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline [PERSON] [46] & S & \(\checkmark\) & \(\checkmark\) & \(\times\) & Classification Model & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [47] & S & \(\times\) & \(\checkmark\) & \(\times\) & Anomaly Detection & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [61] & S & \(\checkmark\) & \(\times\) & Cloud & Artificial Neural Network & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] kumar [62] & S & \(\checkmark\) & \(\checkmark\) & Fog & Smart Agriculture & \(\checkmark\) & \(\times\) & \(\times\) \\ \hline [PERSON] [63] & S & \(\checkmark\) & \(\checkmark\) & \(\times\) & Stacked Ensemble Model & \(\times\) & \(\times\) & \(\times\) \\ \hline Proposed Work & S & \(\checkmark\) & \(\checkmark\) & Fog & Smart Farming & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}  
* Env: Environment, S: Simulator, R: Real, Int: Intrusion, Det: Detection, ML: Machine Learning, Intg: Integrated, U2U: UAV to UAV, Com: Communication.  
\end{table}
Table 1: Cont.  
Here, \(P_{sl}\) is the power usage, and \(T_{sl}\) is the amount of time spent in sleep mode. Likewise, the total energy used when in the active state \(E_{ac}\) is computed in Equation (3).  
\[E_{ac}=E_{m}+E_{d}+E_{p}+E_{t}+E_{r} \tag{3}\]  
In this case, \(E_{m}\) represents the energy consumed due to mode change, and \(E_{d}\) represents the energy consumed during data collection. \(E_{p}\) represents data processing energy and \(E_{t}\) and \(E_{r}\) represent the data transmitting and receiving energy, respectively.