## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhanced and Timely Data Acquisition  
The study in [113] explores the use of CubeSats as low-cost satellite network building blocks for the Internet of Space Things. To enable seamless functionality for a variety of use cases and stakeholders, an automated network slicing methodology is proposed for space-ground integrated networks. Designed for extremely dense CubeSat networks, the system focuses on effective route computation and resource allocation while ensuring Service Level Agreement (SLA) adherence. Notably, it employs an SLA-based methodology, eliminating the need for prior knowledge of slice resource requirements. Furthermore, a case-driven evaluation scenario was employed to evaluate the adaptability and effectiveness of the proposed framework. The study in [38] addresses the challenge of establishing IoT connectivity in remote areas with restricted communication infrastructure, such as the Arctic. The research presents an alternative solution involving a swarm of small, freely drifting satellites and industry-standard protocols. The authors validate the feasibility of this approach by simulating networking protocols and link characteristics across different satellite orbits and ground nodes. The findings indicate that small-satellite swarms can significantly minimize communication overhead and end-to-end request latency, enabling IoT deployment even in the most remote settings, like the Arctic. The researchers in [67] explored the use of a satellite-enabled IoRT network to collect data in remote areas with no internet connectivity. Specifically, the Xingyun satellite constellation was employed to gather environmental data from the Tibetan Plateau. The monitoring system consisted of terrestrial, ground-based terminals equipped with satellite transceivers and environmental sensors. Five such terminals were deployed in challenging regions to monitor air temperature, relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation. Field experiments evaluated the performance of the proposed system, revealing its efficiency. The research in [114] focuses on the problem of maintaining fast data updates in satellite-integrated IoT networks for applications like animal tracking and environmental monitoring. To address this, the authors propose Spatially Temporally correlative Mutual Information (STI), a new metric that takes into account correlations between the most recent update message and the current state of the data source. They optimize channel slot allocation using a Markov decision process framework by maximizing the averaged STI over a specified updating period. The simulation results show that the proposed strategy outperforms traditional scheduled access schemes such as slotted ALOHA and Threshold-ALOHA. In [115], a packet scheduling algorithm designed specifically for IoRT devices operating in 5G satellite networks is proposed. The algorithm achieves its objectives through the implementation of a cross-layer design approach. The principal objective of this packet scheduler is to optimize system throughput while ensuring a reasonable degree of fairness for both delay-sensitive and delay-tolerant IoRT services. The simulation results indicate that the proposed packet scheduler demonstrates better performance in terms of throughput, spectral efficiency, and fairness index when compared to previously proposed schemes. The expanding M2M/IoT traffic and its potential for satellite delivery are examined in [116]. The emphasis is on comparing two major M2M/IoT protocol stacks, Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT), on a