In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computingcontrol (SC 3 ) closed loops.However, the limited battery capacities of devices within these SC 3 loops constrain operational duration and degrade control efficiency, particularly in remote or postdisaster scenarios.To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC 3 closed loops.In this paper, we investigate a wireless-powered SC 3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC 3 closed loops.By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance.Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem.The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized.We recast the problem into a more tractable form and develop an iterative algorithm to solve it.For the special case of a single loop, we further analyze the properties of optimal solutions in energylimited scenarios to provide insights for practical parameter configuration.Simulation results demonstrate the performance gains of the proposed scheme.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

In emergency rescue scenarios, field robots can be dispatched to enhance rescue operations, and unmanned aerial vehicles (UAVs) can be utilized to serve field robots thanks to their flexibility and on-demand deployment. To support the robots efficiently, UAVs should be equipped with sensors, base stations (BSs), and mobile edge computing (MEC) servers. The whole process of a typical rescue task can be regarded as a sensing-communication-computing-control (SC3) closed loop. In this paper, we focus on the closed-loop performance of SC3loops, which is essential for mission-critical tasks. Specifically, we propose a joint communication and computing resource allocation problem, aiming to minimize the sum linear quadratic regulator (LQR) cost of SC3loops. We prove the convexity of the optimization problem by introducing auxiliary variables. Numerical results are provided to show that our proposed scheme can enhance the system’s control performance. Our work also shows that it is essential to jointly consider the communication, computing, and sensing capabilities in unmanned rescue tasks.

Outside the coverage of terrestrial cellular networks, non-terrestrial infrastructures, e.g., satellites and unmanned aerial vehicles (UAVs), should be utilized, to efficiently cover the remote areas. This requires a cognitive satellite-UAV network, where satellites and UAVs share the spectrum to save cost, and the network resources are orchestrated in an on-demand manner. In this paper, we focus on the physical layer security issue of the cognitive satellite-UAV networks, which is important due to the openness of both satellite links and UAV links. We formulate a joint power and channel allocation problem, using only the slowly-varying large-scale channel state information (CSI), to maximize the sum secrecy rate of UAV users. By resorting to the random matrix theory, the max-min optimization tool, as well as the bipartite graph matching algorithm, we propose a sub-optimal low-complexity solution, the superiority of which is verified by simulation results.