## 6 Overview of Existing AI Techniques for NTN-Based IIoT
### Spaceborne-Based Intelligent IIoT  
Recently, AI solutions have been proposed to handle in an optimal or near optimal manner a wide range of communication and networking issues of spaceborne platforms in varying and unexpected operating conditions to maintain reliability and efficient utilization of the resources and accomplish specific application missions. Among these issues, (i) the power allocation, (ii) the energy consumption, and (iii) the computing offloading represent the most challenging ones. However, contradictory requirements should be also fulfilled, including the low latency and enhanced throughput and coverage as well as the vast number of interconnected devices. To enable massive access in S-IoT, a NOMA downlink system was considered in [103] involving a satellite source node and multiple ground nodes, where the nodes in the same spot beam coverage synchronously shared the same frequency. For this system, a DL-based adaptive moment estimation (Adam) algorithm was developed to jointly optimize the decoding order of SIC and the long-term power allocation. In this regard, a neural network was used, acting as an approximation function that was capable of calculating the SIC decoding order given the queue and channel states. The results underlined the accuracy and efficiency of this method requiring only a few iterations with respect to the long-term network utility, average arriving rate, and queue delay. Nevertheless, large amounts of datasets are essential for training. A DL-based multi-objective optimization of the resource allocation in CubeSats-based IoST networks was proposed in [104], using an ensemble DNN and a random hill-climbing algorithm to control the weights of the neurons. The resource-constrained CubeSats were equipped with reconfigurable plasmonic reflect arrays, whereas real satellite trajectory data of the Iridium NEXT were utilized for training. According to the results, the inter-satellite links are capable of supporting multi-Gbps throughput in low Earth orbits. In [67], an MEC-enhanced S-IoT network with limited computation and energy resources was optimized in terms of energy consumption and processing latency. This network consisted of multiple satellites acting as edge computing nodes connected to multiple satellite gateways for IoT nodes that were distributed in a remote area. In this respect, a Lagrange multiplier method was employed in dynamic environments to realize the computing and communication optimization of the resource allocation, while a DRL-based deep Q-network (DQN) was also collaboratively exploited to handle the off-loading decision. In [105], the role of edge computing and DL in S-IoT image data target detection was discussed and a reference three-layer architecture was presented. According to this architecture, the data for target detection can be collected from both the S-IoT edge and cloud nodes through appropriate sensors, while the optimization training of the DL algorithm that handles the efficient computing offloading can be accomplished in advance and then transferred to the satellites. The results placed emphasis on the reduction of the delay of acquiring images from satellites and the improvement of the performance with respect to the target detection as well as the backhaul bandwidth preservation. Based on these results, it was obvious that edge intelligent computing can intercept purposeless data transmission and processing and also enhance the utilization of bandwidth. An S-IoT edge intelligent computing architecture was also described in [106] that effectively accelerates the data processing by adopting edge computing and DL methods. More importantly, S-IoT edge computing and distributed S-IoT intelligent computing architectures were presented. The former constitutes a three-layer architecture that comprises three parts, i.e., S-IoT cloud nodes, S-IoT edge nodes, and the ground data center. In this architecture, the computing power of each layer gradually increases as the processes move from the edge to the cloud.