We investigate a time-sensitive wireless networked control system (WNCS) where multiple Internet of Things (IoT) sensors embedded with their respective local controllers send their observations to a remote controller over shared wireless channels. From an infinite-time horizon perspective, each process should be stabilized essentially to prevent the process’s states from divergence. Nevertheless, limited channel resources may not fulfill users’ stability requirements due to possibly insufficient transmission attempts. Regarding the tradeoff between stability property and channel resources, we aim to design a transmission scheduling policy that minimizes the infinite-time control cost under channel constraints. Starting with the stability condition analysis under varying scheduling policies, the applied decentralized networked control architecture shows its superiority in extending the WNCS’s scale. By approximately expressing control cost as a function of the Age of Information (AoI), the considered scheduling issue is transformed into an AoI-dependent optimization problem under channel and stability constraints. Then, we develop a control-oriented Whittle index policy where AoI, system parameters, and stability incentives construct the Whittle indexes. Numerical results demonstrate that our proposed policy outperforms the baseline policies in terms of control cost, especially in heterogeneous WNCSs. Furthermore, results show that the proposed policy containing stability factor can support more users with respective stability guarantees.

The recent advent of artificial intelligence and 6G technologies has catalyzed a novel research trend towards goal-oriented design for multi-user remote control systems. Existing works have rarely simultaneously focused on the direct characterization for effective control, and targeted schemes for multiuser scenarios, e.g., non-orthogonal multiple access (NOMA). In this paper, we propose a control-oriented NOMA system capable of intelligent transmission power control. Through theoretical derivation, we build up an age of information (AoI)-dependent function of control cost within the NOMA system framework. In pursuit of the optimal trade-off between control cost and power consumption, we formulate an optimization problem as a Markov decision process and employ the Dueling-Double-Deep Q Network (D3QN) to intelligently decide the transmission power. Numerical results demonstrate the superiority of the control-oriented NOMA system over orthogonal multiple access (OMA) in achieving high-quality multi-loop control while considering energy expenditure.

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.

This paper investigates a wireless remote control problem in cyber-physical system (CPS) with multiple processes. In the system, sensors collect the state information of each process and transmit it to the controller through wireless channels. The communication constraints, including transmission delays, packet loss, and bandwidth limitation, are taken into account. To evaluate control timeliness for each process, this paper adopts the concept of age of information (AoI) in the context of closed-loop control. Meanwhile, to strike a tradeoff between transmission delay and outage, this paper introduces an innovative cross-slot scheduling policy not covered in existing literature, which can freely allocate transmission time and occupancy bandwidth. We prove that the scheduling problem in bandwidth limited remote control is an NP-hard problem, and establish the optimization problem as a Markov decision process (MDP) problem. The Deep-Double-Dueling-Q-Learning (D3QN) algorithm is employed to approximate the optimal scheduling policy for the scenario where the channel information is unknown and the system is model-free. By extensive simulations, the proposed cross-time slot scheduling policy demonstrates superior effectiveness in allocating time-frequency resources and achieving outstanding results.

In this paper, we focus on the remote real-time close-loop control scenarios, where the state of observation process is collected by the sensor and timely transmitted to the remote control center (RCC) over an unreliable channel or network, followed by a control command generated from RCC fed back to the actuator, which is called remote control. The age of information (AoI) is widely used to capture the timeliness. We pay attention to the age outage, which is defined as the probability that the peak age exceeds a certain threshold. Due to the existence of long link delay and channel unreliability, the observation process is likely to be uncontrolled, which may degrade the AoI. We first adopt always remote control (always-RC) and analyze the impact of long delay and channel unreliability on age outage. An interesting result is that long delay and channel unreliability has the potential to decrease the age outage probability. To improve age outage, we further propose a local-assisted joint control policy by introducing the smart sensor that is capable of processing and controlling. Age outage probability under local-assisted joint control is then analyzed. Results show that local-assisted joint control policy has the effectiveness in improving the performance of age outage by setting suitable local control times.

In this paper, we consider a networked control system (NCS) with bidirectional network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the remote controlled UAV adopts the event-triggered mechanism, and the control center uses the adaptive dynamic programming (ADP) method to generate control actions. The application of ADP method to NCS brings new transmission options, that is, transmitting control action or neural network (NN) model. There exists a fundamental tradeoff between different transmission options with different transmission energy consumption and tracking cost, which still receives little attention in the NCS design. To fill this gap, we propose a cost-based transmission strategy that can balance the average energy consumption and the average tracking cost. By deliberately making decisions on whether to transmit the control action or the NN model, the weighted sum of the average energy consumption and the tracking cost is minimized. Simulation results show that compared with the benchmark strategies, the proposed strategy can achieve a better compromise in the long-term average energy consumption and long-term average tracking cost, and can obtain better performance in a specific weight range.

In this paper, we consider a cyber-physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different processes and servers. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average Mean Square Error (MSE) of the entire system. To address this issue, we formulate a Markov Decision Process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.

In this article, we consider a cyber–physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers, and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different servers and processes. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average mean square error (MSE) of the entire system. To address this issue, we formulate a Markov decision process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. Furthermore, the processing preemption mechanism is considered to schedule the status updates more flexibly, and its consistency property is proved. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.

In this article, we investigate the freshness of the vehicular status updates in space–air–ground-integrated networks (SAGINs), where the status updates are generated by sampling a fixed-rate dynamic Markov process and delivered to the monitor over an unreliable channel instantaneously. The Age of Information (AoI) is adopted to capture the timeliness of the status updates. Two hybrid automatic repeat request (HARQ) schemes, namely, classical HARQ scheme and incremental redundancy HARQ (IR-HARQ) scheme, are taken into consideration to combat the errors occurred in the transmission. In this setting, once an update is not decoded successfully, one should carefully decide how to schedule the updates for optimizing the AoI. Especially, differential encoding scheme is introduced in the considered system to exploit the temporal correlations of the source. By differential encoding, each update can be actual or differential, based on the differential encoding level. To minimize the long-term average age, we formulate a Markov decision process (MDP), and prove that the optimal transmission policies for classical HARQ scheme and IR-HARQ scheme behave differently in threshold structures. Furthermore, we jointly optimize the codeword length, differential encoding level, and retransmission times to minimize the AoI. The performance comparison shows the advantages of the IR-HARQ scheme over the classical HARQ scheme from the age perspective.

In this paper, we investigate the timeliness of the vehicular status updates in space-air-ground integrated networks (SAGIN) for intelligent transportation systems (ITS). The Age of Information (AoI) is introduced to capture the timeliness of the vehicular status updates. To overcome the inherent end-to-end latency taken by the long-distance communications in SAGIN for ITS, prediction has attracted extensive attention in the existing literature and shown its superiority. Nevertheless, it is not clear whether prediction is beneficial to the AoI. Inspired by the motivation, we first formulate a model of a real-time vehicular communication link with prediction, where the generated update can be predicted and transmitted to the receiver in advance. Then, we derive the explicit expression of the average age and show that the prediction is not always beneficial to the AoI. Instead, prediction is more applicable for the short-distance communications than long-distance communications. Further, to improve the AoI performance, a MDP framework is presented to obtain a switching structure of the optimal prediction policy. The results show the advantage of the optimal prediction policy over the policy of predicting all the time or with no predicting.

To accommodate the surge of data traffic in unmanned aerial vehicle (UAV) applications, software defined air-ground integrated networks (SD-AGNs) hold great potentials for efficient resource allocation and intelligent security countermeasures for UAVs. In SD-AGNs, virtualized bandwidth, computing and security resources owned by terrestrial mobile edge computing (MEC) nodes can be dynamically allocated to satisfy UAVs' diverse demands in data transmission and security protection. However, with complicated cooperative interactions among MEC nodes and competition among UAVs, it is of great challenge to allocate both the security and wireless resource in SD-AGNs. In this paper, we propose a security-aware resource sharing scheme for UAVs to jointly allocate bandwidth and security resource in SD-AGNs, using a game-theoretic approach. Specifically, we first investigate a software-defined collaborative mechanism to promote resource utilization for MEC nodes through coalition formation and resource sharing within each coalition. Then, a coalitional game model is presented to construct the Nash-stable coalition structure for MEC nodes. Furthermore, by modeling the interactions among UAVs as a non-cooperative game, their optimal demands of wireless and security resource, as well as the Nash equilibrium, are analyzed in the competitive environment. Simulation results show that the proposed scheme can effectively improve resource efficiency and reduce average delay.

In this paper, we consider a status update system, in which the source monitors a dynamic Markov process. The status updates are generated with a fixed rate, and delivered to the receiver over an unreliable channel instantaneously. The timeliness of the status updates is characterized by a recent metric, age of information (AoI). In this setting, error would occur in the transmission, deteriorating the reliability of updates. Thus, once an update is not decoded successfully, one should decide whether to retransmit the stale update or switch to transmit the newly generated one. Especially, differential encoding scheme is applied to the considered system to exploit the temporal correlations of the source. By differential encoding, each update can be actual or differential, based on the differential encoding level. To minimize the long-term average age, we formulate a Markov Decision Process (MDP). We prove that the optimal transmission policy has a threshold structure. We also show the existence of the optimal differential encoding level that minimizes the long-term average age under the optimal transmission policy. Numerical results are provided to validate our analytical results. Furthermore, numerical results show that the optimal differential encoding level is decreasing with higher erasure probability of the channel.

As a newly invented type of rateless codes, Spinal codes can be capacity-achieving with short message length and thus hold great prospects for the design of Ultra-Reliable Low-Latency Communication (URLLC) systems. However, the error probability of Spinal codes over Binary Symmetric Channel (BSC) in the finite-length regime lacks explicit analysis in the literature, which in turn hinders efforts to the analytical design of high-efficiency associated techniques, such as the puncturing strategy. In this paper, with the bound on the number of erroneous bits in the Maximum Likelihood (ML) decoding result, we derive the asymptotically tight bound on the Bit Error Rate (BER) of Spinal codes over BSC. Based on this result, we then design the optimal puncturing strategy for Spinal codes over BSC by formulating a rate maximization problem under the constraint of low error probability. In addition, we carry out extensive simulations to verify the correctness of the error probability analysis and the effectiveness of the puncturing strategy design.

In this paper, we consider a long-distance point-to-point communication system with only a single buffer in which the source generates status updates with rate λ and can only transmit one update at a time to the receiver. The timeliness of the status updates is evaluated by the age of information (AoI). In this setting, two scheduling policies, namely preemption and non-preemption respectively, are adopted to minimize the AoI. Specifically, we investigate the priority of the two scheduling policies in the presence of non-trivial propagation delay, which has received little attention in the existing work. Utilizing the evolution of AoI, explicit expressions of the limiting average age for the two scheduling policies are derived, based on which we theoretically prove that for given λ, there exists a threshold of the propagation delay, within which preemption policy outperforms non-preemption policy from the perspective of the limiting average age. We further formulate an optimization problem minimizing the limitng average age under the constraint of decoding failure probability for the two scheduling policies and determine the optimal codeword length. Numerical results are provided to validate our theoretical analysis.

In this paper, we study the timely transmission of status updates from multiple sources to an interested receiver. The sources generate updates independently at Poisson rate, which are then coded and transmitted through a shared block fading channel. Age of information (AoI) is an effective indicator to describe the timeliness of transmission by depicting the freshness of information the receiver knows about the interested source. An intuitively age-oriented transmission scheme is sequentially independent transmission, i.e., the transmitter submits the updates one-by-one without waiting. However, this scheme does not always minimize the age. Aiming to improve the age performance of the considered system, we propose a waiting-and-batching transmission scheme. Specifically, earlier generated updates are waiting for late ones and then batching into a long packet before being coded and transmitted. We derive the close-formed expression of the average AoI of the proposed scheme, and conduct extensive numerical comparisons with the independent transmission scheme. Results reveal that our waiting-and-batching scheme can be more beneficial to AoI if: (1) the update generation rate is high; (2) the propagation delay is non-trivial.

In this article, we consider the satellite Internet of Things (IoT) system, in which the IoT device observes physical processes and transmits the status updates to the monitor node over an error-prone channel with nontrivial propagation delay. The freshness of status updates is characterized by Age of Information (AoI), a novel metric that 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 HARQ (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, the 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 analysis is 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 nontrivial propagation delay.