Large-scale Internet of Things (IoT) connections in dynamic low Earth orbit (LEO) satellite networks face significant challenges in uplink resource scheduling. This paper proposes a framework for optimizing spectral efficiency. The framework satisfies heterogeneous quality of service (QoS) requirements and dynamic buffer constraints under time-varying IoT traffic bursts. It integrates three critical aspects. First, it considers the spatial geometric relationship between satellites and ground user equipment (UE), which determines the connection duration. Second, it achieves service-specific QoS priorities through an adaptive weighting mechanism. Third, it addresses time-varying traffic patterns. Under time-varying resource constraints, the high-dimensional scheduling optimization problem is modeled as a multi-dimensional multi-choice knapsack problem (MMKP). A satellite selection scheme is proposed to efficiently solve the MMKP with mixed constraints. This scheme simplifies the three-dimensional knapsack problem (KP) into a two-dimensional one by taking connection duration into account. This reduction explicitly accounts for the space and time limitations of satellite-ground links. It also integrates service-specific priorities. Meanwhile, the scheme enables each satellite to handle its own computations and resource allocation independently. A binary split dynamic programming (BSDP) algorithm is developed to solve the two-dimensional KP. To compare performance, two large-scale integer optimization methods—the Lagrangian Relaxation Algorithm (LRA) and Branch and Bound (B&B)—were used to solve the KP. The results were compared with a perception-based greedy resource block (RB) allocation for the original resource allocation problem. Extensive simulations based on Starlink demonstrate the effectiveness of the proposed solution. When serving over 4000 UEs, the MMKP solution achieves a 46% gain in QoS compared to the greedy benchmark. It also achieves a 60.7% throughput gain. Additionally, BSDP performs almost as well as B&B. BSDP has approximately two orders of magnitude lower computational cost than LRA.

Satellite-terrestrial integrated network (STIN) plays a crucial role in achieving 3-dimensional full-area coverage. STIN enables the Internet of Things (IoT) industry to realize the integrated space-air-ground communication. The stability of satellite-terrestrial communication and the quality of service (QoS) in low earth orbit optical satellite networks (LEO-OSNs) need to be improved, especially for satellite-based IoT (SIoT) services. To address these challenges, we propose the heuristics multi-physical channel switching and dual Hamming distance-based routing and wavelength assignment (RWA) scheme (HMPS-DHR). Based on dual HAPs deployment architecture and link conditions-aware signal-to-noise ratio (SNR) thresholds model, the multi-physical channels can be flexibly switched among free-space optical (FSO) laser links, Ka-band and S-band microwave links to ensure the stability of the satellite-terrestrial feedback links (FLs). Meanwhile, the traffic conflict gain-adaptive and load-aware dual Hamming distance RWA (TCG-LDHR) algorithm is proposed to optimize the routing, address the RWA problem, and enhance QoS. Simulation results demonstrate that the proposed HMPS-DHR effectively guarantees the communication success rates between satellite and ground at approximately 98.9% to 99.2%, and improves the QoS metrics involving total delay, average throughput, packet loss rate, and blocking rate, by 15.6% to 56.4% compared with the Dijkstra-FF and the ant colony optimization with adaptive load balance small window strategy under hop number loose constraint (ACO-ALB-SWS-HNLC), respectively. HMPS-DHR shows acceptable robustness to synchronization deviations despite unavoidable millisecond-level timing mismatches. Although the QoS performance of the proposed HMPS-DHR is slightly lower than that of the integrated multipath network coding (IMPNC) scheme, its computational complexity is significantly reduced.

Low Earth orbit (LEO) satellite networks, as a fundamental component of 6G networks, are designed to provide full coverage, low latency, and high quality of service (QoS) for satellite-terrestrial integrated networks (STIN). Topology representations and routing computation in dynamic LEO satellite networks have become key research focuses. However, balancing network dynamics with traffic load remains challenging due to inaccurate topology representation and inefficient routing in existing studies. To address this, we propose a timeslot-adaptive and traffic load-aware routing computation (TA-TLARC) scheme for two-layer LEO satellite networks. The two-layer LEO satellite networks consist of communication layer satellites (CLS) and relay and sensing layer satellites (RSLS). TA-TLARC adaptively adjusts timeslots based on traffic variations and utilizes distributed adjacency matrices for routing computation. Simulation results show that TA-TLARC achieves better performance than existing routing schemes in key QoS metrics such as routing success rate, delay, throughput, and packet loss rate. Although routing hops and power consumption increase within acceptable limits, the routing success rate of TA-TLARC remains 99.6% to 100%. The QoS performance, including delay, throughput, and packet loss rate, is improved by 10% to 40% compared to those of the comparative schemes under different traffic scenarios. The robustness of TA-TLARC is further analyzed and demonstrated to be acceptable under various failure conditions. The results demonstrate that the proposed TA-TLARC effectively addresses routing computation challenges and significantly improves QoS performance in two-layer LEO satellite networks.

With the increasing demand for time-critical transmission in vertical industries, the deterministic network has been proposed to provide transmission services with deterministic latency and jitter. Currently, deterministic network research primarily focuses on the transport networks and physical layer of wired networks. However, there is still a lack of research on end-to-end (E2E) deterministic services covering wireless access, wired transmission, and network computing, especially in the dynamic networks. To address this challenge, we propose an efficient E2E service function chain (SFC) resource dynamic orchestration (ESRDO) framework integrating network slicing, edge computing, and deterministic network technologies. To ensure deterministic E2E latency and low latency violation rates, under the case of time-varied number of end users and dynamic channel conditions. Extensive simulation results show that our proposed ESRDO obviously outperforms traditional dynamic resource optimization methods based on the mixed integer nonlinear programming (MINLP).