# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 4 UAV-Enabled MEC and Assisted MEC Based on AI
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & **Evaluated Performance Metrics** & **Summary** \\ \hline
[100] & 2020 & \(\bullet\) & Reduce cost & The concept of EC with UAV was used to perform mapping and lodging assessment in a rice crop without human interaction to reduce maneuvering cost and improve the quality of productivity. The process relies mainly on UAV as an edge server to execute the DNN algorithm while processing the images. \\ \hline
[101] & 2021 & \(\bullet\) & Resource allocation & In this work, AI methods have been used in a UAV-enabled MEC based on the NOMA system. This approach allows terrestrial mobile users to offload their computing duties intelligently. This is intended to increase connectivity and minimize transmission latency and power consumption. \\ \hline
[102] & 2019 & \(\bullet\) & Resource allocation & The authors intended to extend a 5G network for a video surveillance application using a flying ad hoc network consisting of UAVs and EC services. The authors aimed to increase the performance of the entire MEC aerial platform, reduce latency, and ameliorate the reliability of the system’s source usage. \\ \hline
[103] & 2021 & \(\bullet\) & Resource allocation & The authors intended to extend a 5G network for a video surveillance application using a flying ad hoc network consisting of UAVs and EC services. The authors aimed to increase the performance of the entire MEC aerial platform, reduce latency, and ameliorate the reliability of the system’s source usage. \\ \hline
[104] & 2019 & \(\bullet\) & Security & Two approaches were proposed to deal with resource allocation and power control in a UAV-enabled MEC system. The first approach was a centralized multi-agent RL (MARL) algorithm, which has been used to optimize the system’s power consumption and resource allocation. The second approach is a federated multi-agent reinforcement learning (MAFRL) algorithm, which has been proposed to guarantee security and privacy. \\ \hline
[105] & 2019 & \(\bullet\) & Security & A cyber-defense approach based on a non-cooperative game algorithm was proposed to protect a UAV-enabled MEC from network and offloading attacks. \\ \hline
[105] & 2019 & \(\bullet\) & Security & A MEC server-based authentication framework was proposed to be integrated into UAVs. This was mainly to enhance the privacy and authentication of UAVs. \\ \hline
[106] & 2020 & \(\bullet\) & Task offloading Resource allocation & A UAV-assisted multi-user MEC system based on frequency division multiple access (FDMA) under Rician’s fading channels was proposed to test task offloading and resource allocation performances. \\ \hline
[107] & 2019 & \(\bullet\) & Task offloading & A theoretical game strategy based on three types of players was proposed to formulate and solve the problem of offloading task calculations in UAV-enabled MEC networks. \\ \hline
[108] & 2018 & \(\bullet\) & Task offloading Throughput & In this work, a UAV was used as a mobile edge server to manage offloading processing tasks in real-time for ground-based users. A hybrid scheme based on a semi-Markov decision process and DL was proposed to maximize the throughput requirement. \\ \hline
[109] & 2020 & \(\bullet\) & Task offloading & Two offloading schemes for multiple UAVs-enabled MEC networks were proposed to optimize computation time and energy consumption. A game theory model was adopted to validate the proposed strategies. \\ \hline
[110] & 2018 & \(\bullet\) & Transmission power UAV trajectory & A UAV-assisted MEC environment over the social internet of vehicles (SIoV) with a three-layer integrated architecture was adopted. Total utility maximization was achieved by jointly optimizing the transmission power of the vehicle and the UAV trajectory. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)computing activities, either partially or totally, to MEC-server-mounted UAVs through direct line-of-sight connectivity.