This paper presents a Dual-Security-Assured Computation Offloading (DSACO) scheme for space-air-ground networks (SAGNs), which exploits the integrated sensing, communication, and computing (ISCC) capability of the low Earth orbit (LEO) satellite to support secure and efficient computation offloading. In the proposed scheme, the air-ground mode serves as the default edge-processing strategy due to its low latency and energy consumption, while the LEO satellite senses the malicious aerial eavesdropper and protects the ground device (GD)-to-UAV links through directional anti-eavesdropping jamming. When such protection becomes insufficient, the system switches to direct GD-to-LEO offloading via dedicated frequency bands. Accordingly, the secure offloading process is formulated as a worst-case average long-term energy minimization problem under sensing uncertainty. To solve the resulting dual-timescale mixed-integer nonlinear problem, we develop a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework for the joint optimization of sensing duration, UAV trajectories, computation offloading, and resource allocation. Simulation results demonstrate that the proposed scheme significantly enhances system security while maintaining offloading efficiency, and that the H-MADRL framework outperforms benchmark methods.

Backscatter communication is emerging as a promising solution for enabling low-power and large-scale IoT applications. However, it faces challenges in terms of widespread deployment, wireless resource management, and quality of service (QoS). In this paper, we first propose a secure transmission architecture to integrate the backscatter network with the existing 5G/IoT infrastructure. Next, we introduce a multi-device scheduling and time-switching strategy aimed at optimizing both capacity and secure throughput. In the time-switching scheme, BDs primarily operate in symbiotic mode without requiring additional spectrum, but can dynamically switch to opportunistic spectrum access mode when necessary, with adaptive time allocation to improve QoS. For multi-device scheduling, BDs are assigned to function as a master transmission node, cooperation node, or spoofing/jamming node, thereby enhancing the system’s resistance to proactive eavesdropping. The optimization problem is formulated to minimize spectrum resource usage while ensuring QoS and following the energy constraint. To solve this, we introduce an enumeration-based interior-point algorithm (EIA) and design a novel progressive greedy algorithm (PGA). The EIA method provides optimal solutions, while the PGA algorithm achieves high-quality suboptimal solutions with lower complexity. Extensive simulation results demonstrate that the proposed strategy stands out in ensuring QoS, enhancing security, and reducing spectrum resource usage.

Unmanned aerial vehicle (UAV) and high-altitude platform (HAP)-enabled aerial edge computing (AEC) networks facilitate diverse Internet of Things (IoT) applications. In this paper, we investigate the average task completion time and energy consumption by jointly optimizing sensing, communication and computing in cooperative AEC networks facilitated by multiple UAVs and HAP. The sensing times, multi-UAV trajectories, transmission power control, offloading strategy and communication resource allocation are jointly optimized. We transform the original optimization problem into minimizing the average task completion time while ensuring energy consumption stability by introducing Lyapunov optimization theory. The problem is then decomposed into multiple subproblems, which are solved through numerical analysis, successive convex approximation, and the Dinkelbach algorithm, respectively. These algorithms are embedded into the proximal policy optimization (PPO)-based multi-agent deep reinforcement learning (MADRL) framework to speed up the convergence performance of the MADRL model. Simulation results demonstrate that the proposed algorithm achieves superior performance in terms of average task completion time and energy consumption.

In this paper, we propose a novel integrated sensing and communication (ISAC) scheme for symbiotic radio (SR)-based IoT systems, addressing key challenges such as inter-symbol interference (ISI) and frequency-selective fading caused by wideband access. To tackle these issues, we develop a symbol-level delay alignment modulation (SL-DAM) precoding that lever-ages temporal degrees of freedom and delay-domain manipulation to suppress ISI and enhance sensing accuracy. A waveform optimization problem is then formulated to maximize sensing performance under primary and secondary communication rates and power constraints. A joint S-Lemma and successive convex approximation algorithm is proposed to solve this non-convex problem efficiently. Simulation results confirm the effectiveness of the proposed SL-DAM precoding in wideband SR-enabled ISAC systems.

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

In this paper, an age of information (AoI)-aware joint design framework of sampling, transmission, computation, and control is considered for industrial cyber-physical systems. To enhance the control performance, we investigate an edge-enabled control scheme, which allows a physical entity to select its sampling adaptively and processing strategies based on de-mand. By analyzing the impact of sampling and short-packet decoding errors, and the coupling relationship between control accuracy and AoI, the AoI -aware control metric is established. Subsequently, we formulate a joint sampling time, computation offloading, and bandwidth allocation optimization problem to minimize the system's control and energy costs. To tackle the formulated NP-hard problem, we develop a BCD-based algorithm leveraging convex and game theories to obtain a joint optimization strategy in an iterative manner. Finally, the performance of the proposed edge-enabled control scheme is verified in the simulation results.

Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.

This paper considers a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous localization and communication (SLAC) system with statistical position uncertainty, where an RIS is deployed to simultaneously enhance the quality of service. To this end, we first derive the closed-form Cramér-Rao lower bound concerning position parameters as the localization metric and also provide the achievable rate metric for communication services. Then, the joint robust design of subcarrier groups, beamforming vectors, and the phase-shift matrix of the RIS is formulated as a stochastic bi-objective optimization problem to maximize expected localization and communication metrics. Due to the nonlinearity of the multi-objective function and the coupling between optimizing variables, the resulting problem is highly non-convex. Accordingly, we transform the expected achievable rate into an analytical form and further develop a novel unified successive convex approximation (U-SCA)-based iterative algorithm to obtain a robust resource allocation strategy. In particular, we derive closed-form solutions of beamforming vectors and the phase-shift matrix of RIS to decrease the computational complexity. In addition, we also analyse the convergence of the proposed U-SCA-based algorithm. Simulation results demonstrate the effectiveness of the presented method.

In this paper, a novel reconfigurable intelligent surfaces (RIS)-assisted joint localization and communication (JLAC) scheme is presented to supply both position-sensing and data transmission functions for a multi-user system by a frequency division strategy. In particular, considering the parameter uncertainty, we formulate the robust resource design problem as a statistical mixed-integer form, aiming to maximize localization and communication performance by joint subcarrier group, beamforming, and phase-shift optimization. To tackle the formulated non-convex problem efficiently, we develop an iterative method based on the stochastic successive convex approximation technology to handle the original problem. Simulation studies are presented to demonstrate the effectiveness of the proposed JLAC scheme and method.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Joint localization and communication systems have drawn significant attention due to their high resource utilization. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided simultaneously localization and communication system. We first determine the sum squared position error bound (SPEB) as the localization accuracy metric for the presented localization-communication system. Then, a joint RIS discrete phase shifts design and subcarrier assignment problem is formulated to minimize the SPEB while guaranteeing each user’s achievable data rate requirement. For the presented non-convex mixed-integer problem, we propose an iterative algorithm to obtain a suboptimal solution by utilizing the Lagrange duality as well as penalty-based optimization methods. Simulation results are provided to validate the performance of the proposed algorithm.