Integrated sensing and communication (ISAC) is pivotal for enabling simultaneous environment perception and data transmission in intelligent transportation systems (ITS). However, mission-critical ITS management applications, such as collision avoidance and autonomous driving, require stable and reliable ISAC services. Unfortunately, dense urban canyons, with their skyscraper-induced occlusions, create persistent coverage blind zones, posing significant challenges to these applications. To address these challenges, this paper explores a novel aerial intelligent reflecting surface (AIRS)-assisted ISAC system, where multiple AIRSs dynamically reconfigure the wireless propagation environment to enhance multi-vehicle sensing and base station (BS)-to-multiuser communication. To maximize the minimum achievable communication rate while ensuring sensing performance, we formulate a joint resource allocation problem considering BS beamforming, AIRS trajectory optimization, AIRS phase shift control, and user association. Given its highly coupled and nonconvex nature, we develop an alternating optimization framework tackling each subproblem sequentially. Specifically, we employ the Lagrangian dual transform and semi-definite relaxation (SDR) for BS beamforming, the successive convex approximation (SCA) method for AIRS trajectory optimization, matrix decomposition and equivalent rank-constrained transformation techniques for AIRS phase shift design, and a penalty dual decomposition (PDD)-based approach for user association. Furthermore, considering uncertainties in the vehicle’s angle of departure (AoD) due to urban mobility, we derive a worst-case sensing performance bound and generalize the proposed algorithm to a more complex scenario. Simulations validate the algorithm’s effectiveness, demonstrating superior communication rates and sensing performance over benchmark schemes, while ensuring robustness against AoD uncertainties.

Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions.