## 6 IoRT Systems Based on Hybrid NTN Networks
### Enhanced and Timely Data Acquisition  
The study in [69] explores a satellite-based 5G scenario for massive machine-type communication, utilizing an intermediate layer of UAVs for efficient data collection from sensors dispersed across vast rural areas. Specifically, the application focuses on the timely detection of fire alarms. In the scenario under study, terrestrial internet connectivity is limited or unavailable, making UAVs the ideal solution for gathering sensor data and relaying it to a control station via satellite. The study proposes a system architecture tailored to this scenario and develops an analytical model to characterize the average delay involved in transmitting fire alarm notifications from the field to the control center. The mobile gateway framework provides an alternative for collecting data while also reducing the problem of network partition caused by static gateways. To validate the proposed approach, simulations were conducted, demonstrating its effectiveness.  
To optimize data acquisition strategies in the IoRT, a novel integrated space-air-ground network employing unmanned aerial vehicles and low-earth orbit satellites is proposed [65].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline \multirow{2}{*}{[128]} & \multirow{2}{*}{GEO-UAV} & Development of a framework for analyzing and optimizing hybrid UAV-GEO networks, addressing heterogeneity challenges and maximizing throughput while guaranteeing delay bounds. \\ \hline \multirow{2}{*}{[64]} & \multirow{2}{*}{GEO-UAV} & Development of a beamforming scheme based on the alternating direction method of multipliers to maximize the ergodic sum rate of the system. \\ \cline{3-3}  & & A study of a hybrid satellite-UAV-terrestrial network architecture for maritime on-demand coverage that employs NOMA to manage interference and maximize network sum rate. \\ \hline \multirow{2}{*}{[130]} & \multirow{2}{*}{LEO-UAV} & Development of a resource allocation scheme that maximizes system capacity by jointly optimizing connection scheduling, power control, and UAV trajectory. \\ \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: IoRT systems based on hybrid NTN networks: **enhanced throughput/transmission rate**.  
The authors differentiate between delay-sensitive and delay-tolerant data, tailoring transmission methods accordingly. For delay-sensitive data, a real-time relay via LEO satellites ensures timely delivery, while delay-tolerant data are stored on UAVs for subsequent transmission to a data center. To address the complex optimization problem, a multidimensional algorithm, Rate Demand-Based Joint Optimization (RDJO), is introduced. RDJO optimizes UAV deployment, IoRT device bandwidth allocation, and UAV transmission power, significantly enhancing IoRT data acquisition efficiency. Simulations comparing RDJO to conventional algorithms validate its efficiency.  
The research in [131] investigates the combined use of UAVs and LEO satellite networks to provide low-latency access to IoRT sensors. The authors evaluate LEO satellite-assisted UAV data collection, which utilizes two communication mechanisms: delay-tolerant data are transmitted to Earth via the carry-store mode of UAVs, while delay-sensitive data are transmitted via the UAV satellite network. Considering the limited payloads of UAVs, the authors aim to minimize the total energy cost of the UAV trajectory and transmission while meeting IoRT requirements. The efficiency of the proposed approach is proven by numerical results.  
Table 12 summarizes the work on IoRT systems based on hybrid NTN networks for enhanced and timely data acquisition. Bandwidth allocation and UAV placement strategies are employed to optimize data transmission delay under delay sensitivity constraints.