## 3 The Proposed SP-IoUAV Model
### Unmanned Aerial Vehicles (UAVs)  
Using UAVs as its backbone, the proposed model collects information and disseminates it in the IoUV ecosystem of unmanned aerial vehicles. In order to collect real-time data from their surroundings, such as video feeds, environmental parameters, and network traffic, these self-driving vehicles are equipped with sensors and communication modules. In the proposed model, UAVs play the following roles:  
* **Data Collection:** UAVs continuously gather visual and non-visual data from their environment, including images, videos, environmental parameters, and network traffic data.
* **Data Tagging:** UAVs add unique identifiers or tags to each data point before transmission to indicate their source, maintaining privacy during data sharing.
* **Data Encryption:** UAVs employ encryption techniques to secure both visual and non-visual data before transmitting the data to the data collector, safeguarding sensitive information.
* **Data Transmission:** UAVs collaboratively and securely transmit encrypted data to the data collector, contributing to the collective security framework.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(D=\{D_{1},D_{2},\ldots,D_{n}\}\) & Local datasets from \(n\) UAVs \\ \(\theta\) & Global model parameters \\ \(F(\theta)\) & Global loss function \\ \(D_{i}\) & Local data of UAV \(i\) \\ \(f_{i}(\theta)\) & Local loss function of UAV \(i\) \\ \(\theta_{i}\) & Local model parameters of UAV \(i\) \\ \(\theta_{global}\) & Updated global model parameters \\ Aggregate(\(\theta_{global},\theta_{1},\theta_{2},\ldots,\theta_{n}\)) & Aggregation process \\ \(\varepsilon\) & Privacy parameter (epsilon) \\ \(E_{i}\) & Encrypted dataset from UAV \(i\) \\ \(\Delta x\) & Sensitivity \\ \(\mathrm{Lap}(0,\Delta x/\varepsilon)\) & Laplace noise \\ \(E_{\mathrm{out}}\) & Encrypted output \\ UAV\({}_{i}\) & UAV \(i\) \\ \(D^{\prime}_{i}\) & Privatized dataset from UAV \(i\) \\ \(E_{\mathrm{out}}\) & Encrypted output \\ \(X_{\mathrm{collected}}\) & Collected data \\ \(X_{\mathrm{normalized}}\) & Normalized data \\ \(X_{\mathrm{transformed}}\) & Transformed data \\ \(X_{\mathrm{preprocessed}}\) & Preprocessed data \\ \(y_{ij}\) & Output feature map \\ \(x_{(i+m)(j+n)}\) & Input data at position \((i+m,j+n)\) \\ \(X_{\mathrm{anomalies}}\) & Detected anomalies from CNN \\ \(x_{t}\) & Input at time step \(t\) \\ \(h_{t}\) & Hidden state at time step \(t\) \\ \(C_{t}\) & Cell state at time step \(t\) \\ \(i_{t}\) & Input gate activation at time step \(t\) \\ \(f_{t}\) & Forget gate activation at time step \(t\) \\ \(o_{t}\) & Output gate activation at time step \(t\) \\ \(\dot{C}_{t}\) & Candidate cell state at time step \(t\) \\ \(\sigma\) & Sigmoid activation function \\ \(W_{xi},W_{xf},W_{xxo},W_{xc}\) & Weight matrices for input, forget, output, and candidate gate, respectively \\ \(W_{hi},W_{hf},W_{ho},W_{hc}\) & Weight matrices for input, forget, output, and candidate gate, respectively \\ \(b_{i},b_{f},b_{o},b_{c}\) & Bias vectors for input, forget, output, and candidate gate, respectively \\ \(e_{i}\) & \(i\)-th entity or intrusion identified as a threat \\ \(T_{\mathrm{intrusion}}\) & Detected Intrusion Type \\ \(P_{\mathrm{UAV}}\) & UAV Operators’ Preference \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation Table.  
* **Real-time Communication:** UAVs retain real-time communication links with one another and with the smart city control center, allowing for rapid information sharing and reaction to safety issues.
* **Autonomous Operation:** UAVs function independently, making decisions and adapting to changing situations without human interference, hence, improving monitoring and surveillance abilities.