In this paper, a novel reconfigurable intelligent surface (RIS)-assisted HAP-UAV secure multi-user mixed radio frequency (RF)/free space optical (FSO) system is proposed. Specifically, the Gamma-Gamma distribution is utilized to characterize the atmospheric turbulence effect for the FSO link from UAV to HAP, while the Rayleigh and Nakagami- m distribution fading are applied to simulate the legitimate and wiretap RF links, respectively. We present the closed-form expressions for the probability density functions, the cumulative distribution functions, and the secrecy outage probability (SOP) of the end-to-end signal-to-noise ratio (SNR) in terms of Meijer’s G-function. To gain more insight into secrecy performance, we further obtain the closed-form expressions for the asymptotic SOP, the asymptotic probability of positive secrecy capacity (PPSC), the diversity gain, and the coding gain at high SNR regions. We can observe that the secrecy performance depends on the weaker channel between the RF and FSO, and is closely related to the number of RIS elements, the number of terrestrial users, the atmospheric turbulence factor, pointing error parameters, and the fading parameter of Nakagami- m distributed wiretap link. Finally, numerical results validate the derived results and demonstrate that the proposed design achieves superior secrecy performance over the benchmarks.

Urban sensing is a rapidly growing field that involves gathering real-time data from urban environments. These smart sensing systems are pivotal in enhancing urban planning, resource management, and overall quality of life in cities. However, in the context of data collection and network congestion, non-negligible latency can be experienced in urban environments. Recently, federated learning (FL) has emerged as an attractive approach to address network latency concerns. FL achieves distributing data processing tasks in urban sensing by uploading training results rather than raw data to a central server. In light of this, our paper proposes a wireless sensor air-ground integrated federated learning (AGIFL) network with covert communication. The AGIFL system utilizes a high-altitude platform (HAP) as a parameter server to provide mobility and sustainability of urban sensors and a friendly jammer unmanned aerial vehicle (UAV) to enhance information security. Our main objective is to minimize FL latency for the urban sensor network and ensure secure transmission. To achieve this, we jointly optimize the local training accuracy, interference power, and transmit power of FL sensors. To tackle the nonconvexity of the problem, we decompose the original nonconvex problem into subproblems and solve it using successive convex approximation (SCA) with alternating iterations. Simulation results show that the proposed scheme reduces latency in urban sensor networks significantly and ensures the secure transmission of information compared to benchmarks.

In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users’ power, and UAVs’ three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works.