## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhancement of Edge Computing Capabilities  
Advancements in ultra-dense satellite communications, as explored in [120], could offer an alternative approach for expanding IoRT computational efficiency and resource utilization. IoRT devices can offload their processing tasks to satellite networks or relay them to terrestrial data centers via satellite communications. However, due to resource constraints, a single satellite cannot handle computation-intensive and complex operations. This paper proposes a collaborative task processing scheme that utilizes multiple satellites in a group to simultaneously process IoRT tasks, enhancing computational efficiency and resource utilization. To maximize completion delay reduction for all IoRT devices, the optimization problem is formulated as a Winner Determination Problem (WDP), and a low-complexity cooperative computation algorithm is presented as a solution. Moreover, the growing importance of IoT in the information industry is emphasized in [121], highlighting the need to address network strain, particularly for remote IoT platforms. Edge computing is proposed as a solution for offloading processing closer to data sources. While satellite data transmission is crucial for remote IoT devices, conventional satellites lack versatility due to their specific application designs. To address this limitation, the authors propose transforming conventional satellites into adaptive space-edge computing nodes, enabling dynamic resource sharing and software management. Simulations demonstrate the efficiency of this approach compared to conventional satellite constellations, with the quality of service depending on the number of satellites, computational capacity, and task outsourcing strategies. To address the challenge of managing outsourcing path selection and resource allocation for computation-intensive and delay-sensitive tasks in dynamic network environments, a novel Ka/Q-band satellite-terrestrial integrated network for IoT in remote locations is proposed in [122]. The problem is formulated as a Markov decision process, with the aim of maximizing offloaded tasks while minimizing LEO satellite power consumption and adhering to specific delay constraints. DRL techniques are applied in making optimal decisions by leveraging dynamic IoRT device queues and taking into account time-varying channel conditions and ground station computing capabilities. Simulation results demonstrate the effectiveness of the proposed scheme. An integrated satellite-terrestrial network architecture is proposed in [123] to enhance delay-sensitive task outsourcing for IoRT applications. This architecture seamlessly integrates satellite and terrestrial networks, expanding communication resources, backhaul capacities, and coverage to address the challenges of IoRT deployment. A key focus of the proposed architecture is to optimize offloading link selection and bandwidth allocation for base stations and IoT users. To address the differentiated time granularities of various decision-making processes, a two-timescale stochastic optimization problem is formulated. The authors develop a Hybrid Proximal Policy Optimization (H-PPO)-based algorithm to efficiently address the subproblems involved in the optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme, particularly in scenarios characterized by limited spectrum resources and heavy traffic volumes.