High throughput satellite (HTS) is viewed as a promise solution for the next generation of satellite-based Internet of Things (S-IoT). Considering the onboard communication resources such as power and storage are limited, we formulate a joint network stability and resource allocation optimization problem to maximize the long-term network utility of a non-orthogonal multiple access S-IoT downlink system. First, we establish two virtual queues for both the data queueing and power expenditure, respectively. Then, a joint optimal problem can be formulated as a problem that optimizes the time average of network utility, which perfectly matches the Lyapunov optimization framework. Therefore, by taking into account the condition of successive interference cancellation decoding, we propose a practical solution under the Karush-Kuhn-Tucker conditions, and further introduce an optimal solution by using the particle swarm optimization algorithm for the joint resource allocation problem. Simulation results demonstrate that our joint optimization allocation schemes outperform the existing benchmark schemes.

The wireless networked control system (WNCS) is a closed-loop hierarchical network that enables interaction among wireless communication, computation, and control components to support various services ranging from information exchange to intelligent decision making. Different from a single communication system aiming for reliable or efficient delivery, WNCS is goal-oriented, highlighting the ultimate control performance requirement guaranteed under various limits of communication, computation, and control resources. In this article, we present a comprehensive survey of WNCS from the communication perspective. We discuss appropriate WNCS architecture, topics, such as sensing strategy design, under energy and bandwidth constraints, state estimation problems in the presence of imperfect channels, and control approaches for WNCS performance. We further pay attention to the joint design within WNCS to achieve well-performing WNCS improvements. Moreover, considering the fact that timely transmission of the measurements is of significance to precise control, we provide a review of WNCS design involving the Age of Information (AoI) that copes with the goal-oriented requirements. The challenges and new research directions are discussed at the end of this survey.

We consider the satellite Internet of Things (IoT) system, in which the IoT device observes physical processes and transmits status updates to the monitor node over an error-prone channel with non-trivial propagation delay. The freshness of status updates is characterized by age of information (AoI), a novel metric which is defined as the time that elapsed since the freshest received status update was generated. Channel coding is used to combat the burst channel errors and feedback is available through hybrid automatic repeat request (HARQ) protocols. By adopting both the simple-HARQ and incremental redundancy hybrid ARQ (IR-HARQ) transmission schemes, we study the age-optimal redundancy allocation problems under the constraint of reliability. As we put special interests on the satellite-IoT scenarios in which the propagation delays are nonnegligible, there exists a threshold of the propagation delay only below which using retransmissions is beneficial to AoI. However, characterization of such a threshold has received little attention in the literature. By formulating and solving the age-optimal redundancy allocation problems for the adopted HARQ schemes, explicit expressions of the optimal codeword length for each transmission round are derived, and then the threshold of the propagation delay for beneficial retransmissions is obtained. Extensive numerical analyses are conducted to show the effects of propagation delay and channel state on the redundancy allocation results and the optimal AoI. The threshold is also demonstrated by numerical analysis. The results shed important light on the age-optimal HARQ design for freshness-critical satellite IoT systems in the presence of non-trivial propagation delay.

This paper considers the autonomous detecting and tracking task of unmanned aerial vehicles (UAVs) in the maritime environment. Due to the high computational complexity of the image-processing task, the strict latency requirement of the tracking task, and the shortage of batteries and computational capability of UAVs, the UAVs can offload the computing-intensive task to the edge computing server (ECS) to reduce the task latency and energy consumption. Even though, the large latency and energy consumption brought by some high-definition image-processing task still make some visual tracking difficult to perform. We first investigate the impact of video frame resolution on computing task size and detection accuracy, and further on task latency and energy consumption. In addition, we optimize the transmission power, the local CPU frequency, the offloading rate of UAVs, and the bandwidth allocation between UAVs. We model the UAVs tracking system as an energy consumption optimization problem with the constraint of task latency. Although the proposed optimization problem is complex, we transform the optimization problem into several subproblems with decoupling and rigorous mathematical proof. By combining the convex optimization with the genetic algorithm, an offloading algorithm with low complexity and good performance is obtained, which is verified by comparing with some benchmark offloading strategies in the numerical results.

In this paper, we formulate a long-term resource allocation problem of non-orthogonal multiple access (NOMA) downlink system for the satellite-based Internet of Things (S-IoT) to achieve the optimal decoding order and power allocation. This long-term resource allocation problem of the satellite NOMA downlink system can be decomposed into two subproblems, i.e., a rate control subproblem and a power allocation subproblem. The latter is a non-convex problem and the solution of which relies on both queue state and channel state. However, the queue state and the channel state continually change from one time slot to another, which makes it extremely strenuous to characterize the optimal decoding order of successive interference cancellation (SIC). Therefore, we explore the weight relationship between the queue state and the channel state to derive an optimal decoding order by leveraging deep learning. The proposed deep learning-based long-term power allocation (DL-PA) scheme can efficiently derive a more accurate decoding order than the conventional solution. The simulation results show that the DL-PA scheme can improve the performance of the S-IoT NOMA downlink system, in terms of long-term network utility, average arriving rate, and queuing delay.

Recently, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, available LEO space is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6 G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create heat maps based on the actual collected data and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multivariate optimization problem to achieve the maximum coverage with minimum VLEO satellites. To solve this problem, we build a constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The constellation design process is also highly migratory for other non-terrestrial networks.