## 5 Challenges of NTN-Based IIoT and the Emergence of AI  
Although the spaceborne and airborne platforms have the potential to revolutionize the IIoT, they still try to attain maturity. The major challenges and influencing factors in enabling the successful and long-term operation of NTN-based IIoT are listed below.  
**Particularities of spaceborne and airborne platforms:** NTNs have distinctive attributes, including highly dynamic network topologies, orbits, and/or flight trajectories, as well as weak communication links among the network elements. Besides these, possible displacements of the aerial platforms in any direction and at a varying speed may take place due to the winds or pressure variations of the troposphere and stratosphere layers. There also exist on-board computation inefficiency and energy constraints stemming from the limited battery capacity.
* **Application of communication and networking technologies:** As far as ultra-dense NTN-based IIoT networks are concerned, it is not straightforward to adopt well established wireless standards and protocols, as well as conventional design methods of typical terrestrial networks. More importantly, conventional communication and networking technologies encompass several inherent limitations, as far as non-linear and unexpected phenomena prevail, and a massive number of devices exists. Under these circumstances, channel estimation is a complex and non-trivial process. Therefore, a lack of channel state information (CSI) is inevitable. It is well known that the knowledge of CSI controls important parameters of PHY, such as power allocation, the type of modulation, the management of resources, and the interference mitigation [89]. This issue becomes much more complex when accurate and timely CSI is required (e.g., in massive MIMO systems) or when advanced signal processing algorithms are exploited.
* **Computing offloading:** Since the spaceborne and airborne platforms represent resource-constrained devices, the provision of computation-intensive services necessitates the offloading of applications to cloud servers with centralized and sufficient computation resources. However, in remote areas, edge/cloud infrastructures are usually unavailable.
* **Inter-operability among the heterogeneous types of wireless networks:** NTNs have to deal with mutual interference due to the diverse nature of communication technologies within the same system or the coexistence of heterogeneous systems, which limits the performance and capabilities of the entire system.
* **Target detection and data acquisition:** The inspection, collection, and analysis of structured and unstructured sensor data to extract information and construct IIoT applications typically presupposes human intervention. Nevertheless, accomplishing autonomous, self-configured, and self-optimized network operations in real time within the heterogeneous and multi-dimensional NTN-based IIoT is uncommonly complex.  
To handle the aforementioned challenges and effectively respond to uncertainties, the IIoT network elements should be context-aware and learn and make decisions from the collected and exchanged data. In this respect, AI technologies, which have been adopted in several forms in terrestrial networks [90, 91, 92], are seen as promising for implementation in NTN-based IIoT, enabling the transformation of \"connected things\" to \"connected intelligence\". Although cognitive radio technologies have been investigated for around 20 years [93], the intelligence introduced in them is limited to solving problems regarding spectrum access. As the intelligence expands to service orchestration and network management, cognitive wireless communications are expected to evolve into intelligent radio communications [94, 95], where interdisciplinary approaches can be implemented from the field of communication engineering and the IT sector.