## 6 Overview of Existing AI Techniques for NTN-Based IIoT
### Practical Limitations and Open Issues
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Platform** & **Optimization Target** & **AI Solution** & **Performance Metrics** \\ \hline \multirow{3}{*}{[PERSON] et al. [103]} & \multirow{3}{*}{Satellite IoT (S-IoT)} & Decoding order of successive interference cancellation (SIC) and long-term power allocation & Deep learning (DL)-based Adam algorithm & Utility, data rate, and queue delay \\ \hline \multirow{2}{*}{[PERSON] et al. [104]} & \multirow{2}{*}{Cubesat} & Resource allocation & Deep neural network (DNN) & Bit-error-rate (BER) and data throughput \\ \hline \multirow{2}{*}{[PERSON] et al. [67]} & \multirow{2}{*}{S-IoT} & Latency and energy consumption & \multirow{2}{*}{Deep Q-network (DQN)} & Total cost and proportion of tasks \\ \hline \multirow{2}{*}{[PERSON] et al. [105]} & \multirow{2}{*}{S-IoT} & Image data target detection & DL-based & Connectivity, coverage, and processing delay \\ \hline \multirow{2}{*}{[PERSON] et al. [106]} & \multirow{2}{*}{S-IoT} & Data processing & DNN & Connectivity, coverage, and processing delay \\ \hline \multirow{2}{*}{[PERSON] et al. [107]} & \multirow{2}{*}{SAGIN} & \multirow{2}{*}{Computing offloading} & Deep reinforcement learning (DRL) & Delay, run-time, total cost, and energy consumption \\ \hline \multirow{2}{*}{[PERSON] et al. [108]} & \multirow{2}{*}{Unmanned aerial vehicle (UAV)} & Energy efficiency & Decision tree classifier & Energy consumption \\ \hline \multirow{3}{*}{[PERSON] et al. [109]} & Low-altitude platforms (APs) and high-altitude platforms (HAPs) & Link budget, energy efficiency, and connectivity & Radial basis function (RBF) & BER, probability of blocking, artificial neural network (ANN) & probability of a call being delayed \\ \hline \multirow{3}{*}{[PERSON] et al. [110]} & \multirow{2}{*}{UAV} & Lifetime radio frequency (RF) energy harvesting and connectivity & Machine learning (ML)-based & Consumed energy, harvested energy, energy availability \\ \hline \multirow{2}{*}{[PERSON] et al. [112]} & \multirow{2}{*}{UAV} & Energy efficiency, charging policy, and interference mitigation & Echo state networks (ESNs) and k-means algorithm & Packet loss rate, signal-to-interference-noise ratio (SINR) \\ \hline \multirow{2}{*}{[PERSON] et al. [113]} & \multirow{2}{*}{UAV} & Path planning and action rewards prediction & DRL & Data delay and power consumption \\ \hline \multirow{2}{*}{[PERSON] et al. [114]} & \multirow{2}{*}{UAV} & \multirow{2}{*}{Computing offloading} & DRL & Reward for task scheduling and average slowdown of offloaded tasks \\ \hline \multirow{2}{*}{[PERSON] et al. [115]} & \multirow{2}{*}{UAV} & Data throughput & Deep Q-learning (DQL) & Minimum data throughput \\ \hline \multirow{2}{*}{[PERSON] et al. [116]} & \multirow{2}{*}{UAV} & Trajectory & Map compression based & Data throughput \\ \hline \multirow{2}{*}{[PERSON] et al. [117]} & \multirow{2}{*}{UAV} & Trajectory tracking & k-nearest-neighbor (ANN) & Optimal subpattern assignment (OSPA) distance \\ \hline \multirow{2}{*}{[PERSON] et al. [119]} & \multirow{2}{*}{UAV} & Visual recognition & DNN & Latency \\ \hline \multirow{2}{*}{[PERSON] et al. [120]} & \multirow{2}{*}{UAV} & Image recognition and object detection & Deep residual networks (ResNets) & Detection of common faultson power line components \\ \hline \multirow{3}{*}{[PERSON] et al. [122]} & \multirow{2}{*}{UAV} & Tracking ground targets & Faster region-based convolutional neural network (BCNN) & Processing time and speed \\ \hline \multirow{2}{*}{[PERSON] et al. [124]} & \multirow{2}{*}{UAV} & Delivered expected immersion fidelity & RL & Network capacity, network rate mismatch, and packet loss rate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Synopsis of the research work on AI-enhanced NTNs for IoT scenarios. graphics processing units (GPUs) together with libraries for DL [125; 126; 127] enable real-time parallel data processing, while clusters and cloud computing reduce the training time required by DL.