## 6 Overview of Existing AI Techniques for NTN-Based IIoT
### Airborne-Based Intelligent IIoT
In addition, a DRL algorithm was proposed in order to effectively optimize the path planning of the UAVs, and a CNN Q-network that enables action rewards prediction was trained using UAVs' observations of the surrounding environment. The simulation results with regard to data delay and power consumption demonstrated that the proposed algorithms can notably enhance service coverage. However, further investigation may be devoted to scenarios with intense mobility and coexistence with terrestrial cellular networks. To surpass the limitations of the computing power of ground IoT devices, an IoT system with multiple flexible and cost-effective UAV-based MEC mobile nodes and strong LoS propagation conditions was proposed in [114] that facilitates task offloading services. Aiming at optimizing the load balance among the UAVs while attaining adequate coverage and QoS, a differential evolution (DE)-based algorithm was developed to find a near-optimal UAV placement, whereas the access problem was formulated as a generalized assignment problem (GAP). Besides the above, a DRL-based task scheduling scheme was presented that improves the task processing efficiency of an individual UAV. The simulation results depicted that the proposed optimization framework can achieve superior performance compared to other benchmark solutions. The joint optimization of the multi-UAV three-dimensional (3-D) trajectory and the time resource allocation that leads to throughput maximization in a WPC network was handled in [115]. In this respect, a multi-agent deep Q-learning (DQL) approach was presented. Specifically, the WPC network consisted of UAVs acting as base stations providing energy signals in the downlink to wirelessly charge IoT devices. These devices sent data information in the uplink by exploiting the gathered energy. The numerical results verified the efficacy of the proposed learning scheme in terms of the maximization of the minimum throughput. Additionally, an intelligent IoT data harvesting scheme based on resource-constrained aerial base stations and multiple ground IoT sensors was proposed in [116] and a map-compression based ML solution was used. In this work, the propagation parameters in an urban area were assumed to be unknown and the UAV trajectory was optimized to increase data throughput based on 3-D map data, provided that the optimal channel parameters were adequately learned. As effective tracking systems can fetch the trajectory of UAVs and detect abnormal behavior, the trajectory tracking of UAVs in 5G-IoT networks was optimized in [117], using a probability hypothesis density (PHD) filter along with an ML technique based on k-nearest-neighbor (kNN) and k-means clustering algorithms. The results in terms of the optimal subpattern assignment (OSPA) distance underlined that the proposed ML-based approach outperforms the conventional Gaussian mixture (GM) PHD filter [118], which cannot accurately track the dynamics of UAV targets and unfortunately generates false alarms. Nevertheless, tracking scenarios, where measured data from multiple sensors are used, should be also investigated. An intelligent UAV-based real-time monitoring and control system for an IIoT environment that leverages fog and cloud computing was proposed in [119], where the visual recognition of the photos captured by the UAV's camera was instantly performed in the cloud. To verify the reliability and efficiency of this system, a case study involving the visual supervision in a bulk concrete production plant via drones was described, where the International Business Machines (IBM) Watson DNN-based visual recognition service was exploited, and fog computing together with the Node-RED programming tool were used as the bridge among the layers of the IIoT. In [120], an intelligent UAV-based autonomous vision-based system for the inspection of power grids was proposed.