# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 3 UAV-Enabled and Assisted MEC State of the Art
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Evaluated Performance Metrics** & **Summary** \\ \hline
[91] & 2020 & \(\bullet\) Latency & A UAV-assisted MEC method was proposed, in which the UAVs acted as intermediate devices between the ground-based users and MEC servers. This method leveraged airborne computing and storage facilities to minimize the execution time of offloaded tasks for IoT users. Therefore, the task scheduling and flight path of the UAVs were jointly optimized. \\ \hline
[92] & 2020 & \(\bullet\) Latency & The authors proposed a low-complexity iterative algorithm to optimize security and privacy, subject to latency, offloading, and energy constraints. This method was proposed to optimize the UAV location, the user’s transmission power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. \\ \hline
[93] & 2018 & \(\bullet\) Latency & An air/ground framework for MEC was proposed to combine the capabilities of ground vehicles with UAVs in terms of communication, computing, and storage. \\ \hline
[11] & 2020 & \(\bullet\) Limited offloading tasks & An automatic offloading approach based on the MEC architecture has been proposed to deal with the limited processing capabilities of MEC servers and ground-based users. The UAVs have been used to cache the generated data from the IoT devices and then send it to the MEC servers, which operate in a private blockchain network. \\ \hline
[94] & 2017 & \(\bullet\) Network connectivity & A UAV-based MEC infrastructure was proposed to improve the network connectivity in uncovered areas. The system helped terrestrial users to compute their tasks in circumstances such as natural catastrophes or in rural locations without communication coverage. \\ \hline
[95] & 2019 & \(\bullet\) Offloading tasks & UAVs were used as MEC-aided systems in wireless communication systems to ensure high QoS for ground-based users. The UAVs flew around the users to provide computing services in an orthogonal way over time. \\ \hline
[96] & 2021 & \(\bullet\) Path planning & A multi-UAV-enabled MEC platform was investigated to assess RL QoS and path planning. The study studied the autonomy and self-hovering ability of a network of UAVs relying on RL algorithms. \\ \hline
[97] & 2019 & \(\bullet\) Power consumption & Mobile peripheral computing was deemed a promising technique to address computationally intensive issues. UAV-assisted MEC based on NOMA (non-orthogonal multiple access) can provide flexible computing services for mobile terminals (MTs) in large-scale access networks, as NOMA methods can be adaptive to massive connectivity. In this work, an optimization approach was presented to minimize the power consumption of MTs by jointly optimizing trajectory, task offloading, computing, and resource allocations. \\ \hline
[98] & 2018 & \(\bullet\) Propagation losses & The authors proposed a UAV-enabled MEC and wireless-powered architecture to tackle propagation packet loss in the IoT era. \\ \hline
[8] & 2019 & \(\bullet\) QoS & A joint architecture using the edge and cloud models based on UAV swarms was proposed to assure high service qualities in resource-intensive and real-time applications. \\ \hline
[10] & 2018 & \(\bullet\) Radio access networks & An AGMEN (aerial–ground integrated mobile edge network) architecture was proposed to address many EC network issues, such as communication, computing, and caching. The objective of this approach was to optimally allocate computing and storage resources. The authors deployed a set of UAVs to ensure spatial and temporal coverage, as well as ensuring data delivery for mobile IoT users. \\ \hline
[99] & 2021 & \(\bullet\) Real-time & A traffic monitoring system based on the multi-EYE method was presented to detect and estimate the velocity of unmanned vehicles using aerial image tracking. The image processing was executed in real-time on an embedded edge-computing platform installed on the UAV. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._