## 6 Overview of Existing AI Techniques for NTN-Based IIoT
### Airborne-Based Intelligent IIoT  
One of the main potential barriers of implementing airborne-based IIoT systems is the limited on-board capabilities of the aerial platforms that restrict their endurance, communication, sensing, and computation capabilities. In this sense, the majority of previous works on the adoption of AI techniques have studied the separate or joint optimization of (i) energy consumption that involves energy harvesting and wireless power transmission (WPT), allowing for remotely recharging platforms; (ii) task offloading based on edge computing; (iii) path planning, trajectory, and horizontal and/or vertical placement with respect to ground or flying objects, and (iv) detection, identification, and tracking of objects. The energy efficiency is a serious issue for disaster management through aerial platforms, as indicated in [108]. Thus, the optimization of the flight route of a UAV via a genetic algorithm was suggested during data collection from ground IoT nodes that intends to minimize the energy consumption. A decision tree ML-based classifier was also proposed to be capable of predicting the health risk status with high precision using vital signs data gathered from devastated areas, whereas the collected data were analyzed at the monitoring center in order to reduce energy consumption. To further enhance the energy efficiency, multiple UAVs can be employed, with individual tasks assigned to each of them. The use of aerial platforms flying in varying altitudes and elevation angles for the rapid deployment of a communication network in emergency situations was proposed in [109]. More specifically, an A2G physical propagation model along with a ML-based method based on a radial basis function (RBF) ANN were presented, aiming at providing optimized link budget performance and energy efficiency as well as enhanced LoS connections with rescue teams randomly distributed in an urban area. However, the inclusion of geolocations and relevant environmental information would offer more insights into the optimization process. A UAV-enabled communication scenario for a public safety network (PSN) was proposed in [110]. In this scenario, NOMA techniques were employed and the batteries of IoT nodes were recharged using wireless powered communication (WPC). To optimize the network lifetime radio frequency (RF) energy harvesting as well as overcome shortcomings related with centralized cellular connectivity, ML methods were adopted. Moreover, the IoT nodes were associated with particular roles, i.e., coalition heads operating as gateways or coalition members in a distributed manner by using the minority games (MG) theory [111]. To form coalitions among the IoT nodes while attaining energy efficiency and autonomous operation, an RL technique was applied that considers the member nodes as stochastic learning automata. Although extensive simulation results were provided, verifying these results using real-world testbeds is necessary in order to realize energy-efficient and self-adaptive PSNs. In [112], the wireless powered IoT (WP-IoT) was envisioned and a WPT system was presented that involved energy-constrained IoT sensors and a UAV serving as an RF energy transmitter. To determine the charging policy for these sensors and decrease the data packet loss rate, an ML framework of echo state networks (ESNs) combined with an improved k-means clustering algorithm was suggested, whereas the problem of mitigating interference among the IoT sensor nodes and optimally controlling the power was modeled as a mean field game (MFG). Motivated by the benefits offered by edge computing, a three-layer MEC-based online big data processing IoT network was envisioned in [113], where hovering UAVs were deployed as edge servers. In addition, distributed sensors generating raw datasets and center clouds were included. The online optimization of the edge processing scheduling was realized through an algorithm based on Lyapunov optimization.