# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 4 UAV-Enabled MEC and Assisted MEC Based on AI
Relevant AI techniques include supervised learning, unsupervised learning, and reinforcement learning. For instance, DRL does not require labeled training data for resource allocation. Thus, it may enhance the process of offloading choices based on various states of the channel environment. This results in reducing the energy bandwidth and computations. To that end, an MDP method was provided to reduce the delay, energy consumption, and bandwidth in UAV-assisted MEC by analyzing the instability of energy arrival, stochastic computation tasks that are provided by wireless users, and a time-varying channel state [112]. According to the authors of [113], UAV-assisted MEC-based DL and DRL would ease the development of next-generation communications. This is particularly true for joint optimization challenges to improve system efficiency. Furthermore, UAV communication will be incorporated with 6G networks, where IoT device mobility might lead to position uncertainty. In this case, DRL, as a hybrid of DL and RL, can offer decision-making solutions and optimize handover issues in real-time. This approach allows UAVs to move dynamically and in real-time while assuring and minimizing communication delay [114]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Addressed Issues** & **AI Approach** & **Metrics** & **References** \\ \hline \multirow{8}{*}{Task offloading} & RL & Energy consumption, processing time, latency & [115] \\  & DRL & Energy consumption, latency, cost & [116] \\  & GA & Energy consumption, latency & [117; 118] \\  & DL & Security, privacy, task prediction, and & [119; 120] \\  & FI & Execution time & [121] \\ \hline \multirow{8}{*}{Resources allocation} & RL & Resources allocation, energy & [122; 123] \\  & DRL & Latency, response time, resource utilization, & [124; 125] \\  & DRL & energy consumption & [124; 125] \\  & GA & Energy consumption, latency & [126] \\  & RL-ACO & Throughput & [123] \\ \hline \multirow{8}{*}{Joint optimization issue} & RL & Security and privacy, energy consumption & [127] \\  & DRL & Cost, energy consumption, latency & [128] \\ \cline{1-1}  & GA & Energy consumption, makespan & [129] \\ \cline{1-1}  & DL & Energy consumption, cost & [130] \\ \cline{1-1}  & RL-ACO & Energy consumption & [131] \\ \hline Security & RL & Security caching & [127] \\ \hline Path planning & RL & Path planning & [35; 132; 133] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AI-based approaches used in UAV-enabled MEC. Figure 5: UAV-enabled MEC architecture. Authors in [134] have proved that the deep supervised learning (DSL) method minimizes the computation and offloading overheads and cost of mobile users in the MEC system. This technique allows terrestrial users to obtain an optimal offloading policy, with energy-saving and processing speed enhancement. The authors of [91] have investigated the challenges associated with applying AI techniques to UAV-NOMA-MEC networks. They proposed an architecture that relies on AI-based approaches, particularly on federated learning and reinforcement learning. The proposed framework was dedicated to enabling terrestrial mobile users to offload their computational tasks simultaneously, intelligently, and flexibly, thus enhancing their connectivity and reducing their transmission latency and energy consumption. The authors of [135] used RL and transfer learning algorithms to reduce the latency and energy consumption in UAV-enabled MEC networks. They proved that transfer learning, when combined with RL, could significantly enhance the system training performance when the users operated dynamically. An optimization problem based on MDP was formulated by the authors of [35] to address UAV trajectory optimization in UAV-mounted MEC networks. The approach aimed to enhance the overall system QoS, maximize the long-term system reward, and meet the QoS constraint, including the computation tasks of mobile users.