## 6 IoRT Systems Based on Hybrid NTN Networks
### Enhancement of Energy Efficiency  
In [125], the authors investigate the feasibility of a satellite-aerial-terrestrial integrated network to effectively support the expanding IoT landscape. The study centers on cognitive radio-based networks, leveraging their capabilities to address the critical challenge of spectrum resource management in response to the increasing demands of IoT devices. The study emphasizes the significance of spectrum sharing across satellite, aerial, and terrestrial networks, highlighting the role of cognitive networks in fulfilling IoT requirements. The authors propose a cooperative beamforming scheme designed to optimize IoT communications' security and energy efficiency under constrained energy resources. Leveraging UAV relays, ref. [126] explores energy-efficient resource allocation in a two-hop uplink communication for Space-Air-Ground IoRT networks. In remote areas \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline
[120] & LEO & Development of a collaborative task processing scheme that leverages multiple satellites to process offloaded IoRT tasks. \\ \hline
[121] & LEO & Development of a hardware and software architecture that utilizes virtualization and flexible scheduling to transform traditional satellites into space edge computing nodes. \\ \hline
[122] & LEO & Modeling computation offloading as a Markov decision process and optimizing it using deep reinforcement learning to maximize offloaded tasks while ensuring delay performance. \\ \hline
[123] & LEO & Development of a policy optimization reinforced learning algorithm, which supports delay-sensitive task offloading to achieve better data transmission rates and reliability. \\ \hline
[124] & LEO & A hybrid computation offloading architecture is proposed, consisting of LEO satellites and cloud servers, to minimize task delay and reduce energy consumption utilizing multi-agent reinforcement learning. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: IoRT systems based on space-borne NTN networks: **enhancement of edge computing capabilities**. where ground base stations lack reach, satellites and smart devices rely on UAV relays for seamless communication. By optimizing sub-channel selection, uplink transmission power control, and UAV relay placement, the proposed iterative technique maximizes system energy efficiency, addressing the challenges of IoRT networks. The Lagrangian dual decomposition approach is employed to determine the optimal sub-channel selection and power control policy based on UAV relay deployment positions. Successive convex approximations are then used to determine UAV relay placement. Numerical results demonstrate that the proposed scheme significantly improves system energy efficiency. In [127], the authors propose a novel UAV-LEO integrated data collection system that addresses the challenges posed by the network's heterogeneity and the limited mobility of UAVs and LEOs. The system aims to maximize data gathering efficiency by optimizing IoT device bandwidth allocation and UAV trajectory optimization. Optimizing the transmission powers of UAVs and selecting LEO satellites are proposed to maximize data upload volume and minimize UAV energy consumption. Simultaneously optimizing IoT-UAV data collection and UAV-LEO data transmission is achieved by considering the relay role and cache capacity constraints of UAVs. This problem is efficiently solved using sequential convex approximation (SCA) and block coordinate descent (BCD). The proposed system is shown to perform efficiently in terms of energy consumption and total upload data volume. Table 10 summarizes key research efforts in enhancing the energy efficiency of IoRT systems utilizing hybrid NTN architectures. Researchers have proposed various techniques, including cognitive network architectures, cooperative beamforming schemes, and iterative-based algorithms, to optimize energy consumption and maximize network performance under constrained energy resources.