# 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
[81] & 2021 & \(\bullet\) & Energy & The energy management of UAV-enabled MEC was investigated in the context of a realistic autonomous delivery network. A computational management solution was designed explicitly for MEC-based task offloading and scheduling strategies. The integrated solution includes both static task offloading and dynamic resource scheduling. The experimental results have revealed that the system can handle a greater UAV payload while using less energy. \\ \hline
[82] & 2021 & \(\bullet\) & Energy consumption & The authors addressed the issue of the energy consumption of IoT devices in UAV-enabled MEC networks. The energy and offloading requirements have been improved by optimizing the UAV trajectory planning, communications, and computing resource allocation. \\ \hline
[83] & 2020 & \(\bullet\) & Energy consumption & A UAV-assisted MEC system, in which the UAVs acts as edge servers, was designed to provide computing services for IoT devices. The proposed approach is based on a k-means clustering algorithm to minimize the energy consumption of the system by planning the trajectories of UAVs efficiently. \\ \hline
[84] & 2020 & \(\bullet\) & Energy consumption & The Lagrangian duality method and successive convex approximation techniques were proposed to reduce UAV-assisted MEC computational complexity. The proposed approach aimed to minimize total energy consumption, including communication-related energy, computation-related energy, and UAV energy. This was achieved by optimizing bits allocation, time-slot scheduling, power allocation, and UAV trajectory design. \\ \hline
[85] & 2019 & \(\bullet\) & Energy consumption & The authors proposed a UAV-enabled MEC architecture in which the UAVs were considered as MEC servers. The objective of this approach was to minimize the energy consumption of both UAV and ground-based users by scheduling computation resources and optimizing the UAV trajectories. \\ \hline
[86] & 2019 & \(\bullet\) & Energy consumption & The authors have investigated a UAV-enabled MEC system based on the time division multiple access (TDMA) model. A TDMA-based scheme was proposed to minimize the user’s energy consumption by optimizing the UAV coordinates, time-slot allocation, and task partitioning. \\ \hline
[87] & 2020 & \(\bullet\) & Energy consumption & A two-layer optimization method was provided to address deployment and task-planning issues in a UAV-enabled MEC system. This approach proved efficient for power consumption optimization. The proposed method was based on a differential evolution algorithm with a removal agent. \\ \hline
[88] & 2021 & \(\bullet\) & Energy minimization & A multi-UAV architecture was proposed in which the UAVs act as computer servers to process the ground-based user’s data and to minimize energy consumption. In this approach, a two-layer strategy was used to optimize the UAV’s task scheduling based on dynamic scheduling-based bidding, whereas the second layer addressed bits allocation and the UAV’s flight path. \\ \hline
[89] & 2021 & \(\bullet\) & Energy optimization & UAV-enabled MEC architecture based on the Markov decision process (MDP) was proposed to optimize mobile users’ energy demands and task offloading. In this work, the UAVs were considered intelligent mobile users. \\ \hline
[90] & 2020 & \(\bullet\) & Energy-saving & An algorithm based on block coordinate descent and successive convex approximation techniques was proposed to optimize data offloading. By considering a single UAV, the proposed system improved the tasks of offloading and energy consumption. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._