## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhanced and Timely Data Acquisition
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Refs.** & **NTN Architecture** & **Description** \\ \hline \multirow{3}{*}{[111]} & \multirow{3}{*}{LEO} & Development of a novel access scheme for terrestrial-satellite IoT communication that maintains low access delay, robustness to channel impairments, and enables reliable data transmission. \\ \cline{1-2}  & & Development of a robust beamforming scheme for NOMA-based integrated satellite-terrestrial IoT networks to minimize power consumption and ensure reliable communication for a large number of IoT devices. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._satellite Random Access (RA) channel using the DVB-RCS2 specification. The completion time metric is critical in determining which protocol stack is preferable. The study emphasizes the advantages of the Publish/Subscribe (PUB/SUB) paradigm, particularly in satellite-based architectures, for its effectiveness in rapidly delivering new data to subscribers. The authors then propose integrating CoAP with the observer pattern and proxying functionality to optimize the PUB/SUB paradigm. CoAP outperforms MQTT on RA satellite channels, demonstrating the adaptability of application-layer tuning options. A QoE-aware satellite constellation design scheme is proposed in [117] to improve user experience in satellite IoT applications. The satellite IoT network is composed of LEO satellites and ground-based IoT devices. Quality of Experience (QoE) factors are defined to evaluate coverage performance, communication efficiency, regional demand capacity, and profitability. A Multi-Layer Tabu Search (MLTS) optimization algorithm is then used to determine optimal satellite orbits that maximize QoE. The simulation results show that the proposed constellation is effective in optimizing multiple QoE factors, resulting in a better user experience. Within the context of IoRT, the authors of [118] address the challenge of interconnecting widely dispersed IoT nodes. They propose a cooperative mechanism that employs feedback on link conditions and adaptive coding knowledge to optimize resource allocation and improve video transmission quality and fairness among IoRT nodes. The proposed methodology was validated by implementing it on an emulation platform, demonstrating its efficiency and advantages over existing methods. This cooperative mechanism has the potential to improve the performance and reliability of IoRT networks, especially in disaster recovery scenarios where seamless video transmissions from remote locations are critical. A study in [119] proposes a joint optimization strategy for maximizing long-term network utility in a NOMA Satellite IoT downlink system. The proposed approach employs two virtual queues to manage both data queuing and power consumption. Lyapunov optimization is employed in order to attain network stability and optimize resource allocation, taking into account the constraints of onboard communication resources. Moreover, the authors propose a solution utilizing Successive Interference Cancellation (SIC) decoding in conjunction with Particle Swarm Optimization (PSO) to determine the optimal resource allocation strategy. The simulation results show that the joint optimization allocation operates efficiently in terms of long-term network utility, average data rate, and queuing delay. Table 8 summarizes studies that have explored the potential of integrating space-borne NTNs into IoT networks to enhance data acquisition and improve timeliness. These studies have proposed innovative approaches for network slicing, small-satellite swarms, and QoE-aware constellation designs. They have also evaluated the performance of different protocols and resource allocation mechanisms and demonstrated that space-borne NTNs are a promising solution for enabling delay-sensitive IoRT applications in remote and challenging environments.