# Smart IoMT Framework for Supporting UAV Systems with AI
## 5 Discussion
### Challenges of the UAV-Based IoMT  
* **Integrity:** The system has to be calibrated and frequently checked for integrity gaps due to a lack of control of the system. These UAVs have to be called back to the base after regular intervals for maintenance.
* **Security:** The security of these deployed UAVs is of major concern. Protecting them from climatic calamities, collision with birds, and malfunctioning mid-flight are some of the major concerns. It is important to develop a predictive decision-making algorithm to protect and secure UAVs from the above security concerns. Another security issue would be a data breach, since the UAV is storing the data in the system before transmitting it to a base station, it may be susceptible to data manipulation or attacks before relaying the data.
* **Confidentiality:** It is critical to safeguard sensitive data and information interchange between the UAVs and the base station from unwanted access since this might be a source of sensitive patient information leaks or video access into other people's property. Thus, it is important to consider developing effective cryptographic methods to prevent the adversary from gaining such information.
* **Reliability:** Some failures are more critical than others during a normal mission: loss of GPS signal, loss of data stored from the patient, or loss of control over system functions are some examples. As a result, multiple degrees of rising importance to a fault must first be established. Furthermore, failures must be subdivided into sections based on the individual mission and the UAV. Then, for each scenario, appropriateness, and a preventive projected scenario, a minimum acceptable degree of reliability must be defined.  
Figure 13: Performance comparison of various machine learning algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ML Algorithm** & \multicolumn{2}{c}{**Decision Tree**} & \multicolumn{2}{c}{**Logistic Regression**} & \multicolumn{2}{c}{**Random Tree**} & \multicolumn{2}{c}{**Random Forest**} \\ \hline
**Parameter** & **Quantity** & **Accuracy** & **Quantity** & **Accuracy** & **Quantity** & **Accuracy** & **Quantity** & **Accuracy** \\ \hline P1 & 149 & 96.7532\% & 135 & 87.6623\% & 152 & 98.7013\% & 152 & 98.7013\% \\ P2 & 5 & 3.2468\% & 19 & 12.3377\% & 2 & 1.2987\% & 2 & 1.2987\% \\ P3 & 0.9283 & - & 0.7536 & - & 0.972 & - & 0.972 & - \\ P4 & 0.0216 & - & 0.0957 & - & 0.0087 & - & 0.0155 & - \\ P5 & 0.1471 & - & 0.22336 & - & 0.093 & - & 0.0968 & - \\ P6 & 6.9653\% & - & 30.8045\% & - & 2.7861\% & - & 5.0011\% & - \\ P7 & 37.1292\% & - & 56.4305\% & - & 23.4826\% & - & 24.4303\% & - \\ P8 & 154 & - & 154 & - & 154 & - & 154 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different ML algorithms.  
* **Authorization:** Data exchange in the UAV system must be restricted to authorized users only. Unauthorized users are not permitted to undertake any actions in the UAV's communication network. Furthermore, the UAV system must describe which resources an authorized user has access to. Access to such resources must be monitored using access control regulations.
* **Non-disclosure:** In the privacy requirements of the UAV, it is important to include the non-disclosure property. It is important to keep sensitive information shared between the base station and the UAV, such as photographs and surveillance video for maneuvering that has been acquired, private from 3 rd party users.