The development of Internet of Things (IoT) systems has given rise to security concerns stemming from the exposure of wireless channels and the exponential growth of connected devices. The security challenges can be severer in the next-generation IoT systems that can disperse over large areas under a cell-free (CF) network setting. In this article, we propose a novel covert downlink transmission scheme that jointly optimizes beamforming and artificial noise (AN) vectors to obscure critical transmissions at an eavesdropper in CF IoT Networks. We classify access points (APs) as information APs (IAPs) and noise APs (NAPs) based on their proximity to the IoT devices. IAPs transmit information while NAPs generate AN to prevent eavesdropping. We derive a closed-form solution for the detection error probability. By using the Lagrangian dual algorithm, the complex logarithmic problem is transformed into sum-of-ratios form. Then, we use semidefinite relaxation (SDR) to maximize the covert transmit rate. Numerical results show that the proposed scheme outperforms the state of the art, i.e., the suboptimal Rand- AP scheme, by increasing the transmission rate by more than 23% while maintaining covertness and is better than the rest of the benchmark schemes.

User privacy protection is considered a critical issue in wireless networks, which drives the demand for various secure information interaction techniques. In this article, we introduce an intelligent reflecting surface (IRS) aided security classification wireless communication system, which reduces the transmit power of the base station (BS) by classifying users with different security requirements. Specifically, we divide the users into confidential subscribers with secure communication requirements and general communication users with simple communication requirements. During the communication period, we guarantee the secure rate of the confidential subscribers while ensuring the service quality of the general communication users, thereby reducing the transmit power of the BS. To realize such a secure and green information transmission, the BS implements a beamforming design on the transmitted signal superimposed with artificial noise (AN) and then broadcasts it to users with the assistance of the IRS’s reflection. We develop an alternating optimization framework to minimize the BS downlink power with respect to the active beamformers of the BS, the AN vector at the BS, and the reflection phase shifts of the IRS. A successive convex approximation method is proposed so that the nonconvex beamforming problems can be converted to tractable convex forms. The simulation results demonstrate that the proposed algorithm is convergent and can reduce the transmit power by 20% compared to the best benchmark scheme.

Cell-free (CF) networks are promising for solving the inter-cell interference and switching problems resulting from ultra-dense small cell deployments. This paper considers a downlink secure transmission scenario, in which the information is exposed to active eavesdroppers. A new access points (APs) classification-based transmission scheme is presented to enhance the system performance by strategically assigning different roles to the APs in CF networks. Specifically, the APs serving user equipments (UEs) are classified into service APs (SAPs) responsible for communication, and noisy APs (NAPs) responsible for defending confidential signals by emitting artificial noise. By designing beamforming and artificial noise vectors separately, we aim to maximize the secrecy sum rate while suppressing information extraction for eavesdroppers. We apply semidefinite programming and Lagrangian duals to decouple the nonconvex optimization problems, and hence, resulting in tractable formulations. The suboptimal solution is finally obtained via eigenvalue decomposition with Gaussian randomization. Numerical results demonstrate that the suboptimal solution can closely approach the optimal counterpart, and the proposed transmission scheme is advantageous in terms of secrecy rate performance compared to the state-of-the-art.

A secure downlink transmission system which is exposed to multiple eavesdroppers and is appropriate for Internet of Things (IoT) applications is considered. A worst case scenario is assumed, in the sense that, in order to enhance their interception ability all eavesdroppers are located close to each other, near the controller and collude to form joint receive beamforming. For such a system, a novel cooperative nonorthogonal multiple access (NOMA) secure transmission scheme for which an IoT device with a stronger channel condition acts as an energy harvesting relay in order to assist a second IoT device operating under weaker channel conditions, is proposed and its performance is analyzed and evaluated. A secrecy sum rate (SSR) maximization problem is formulated and solved under three constraints: 1) transmit power; 2) successive interference cancellation; and 3) quality of service. By considering both passive and active eavesdroppers scenarios, two optimization schemes are proposed to improve the overall system SSR. On the one hand, for the passive eavesdropper scenario, an artificial noise-aided secure beamforming scheme is proposed. Since this optimization problem is nonconvex, instead of using traditional but highly complex, brute-force 2-D search, it is conveniently transformed into a convex one by using an epigraph reformulation. On the other hand, for the active multiantennas eavesdroppers’ scenario, the orthogonal-projection-based beamforming scheme is considered, and by employing the successive convex approximation method, a suboptimal solution is proposed. Furthermore, since for single antenna transmission the orthogonal-projection-based scheme may not be applicable a simple power control scheme is proposed. Various performance evaluation results obtained by means of computer simulations have verified that the proposed schemes outperform other benchmark schemes in terms of SSR performance.

Physical layer security is a crucial technology to achieve secure information transmission. However, emerging technologies, such as machine learning, impose significant challenges on existing security systems. In this paper, we propose a new relay system to achieve physical layer secure transmission and construct a deep learning model on the relay to design secure beamforming vector. This design is able to adapt to the current channel of the legitimate user and resist the eavesdropping from eavesdroppers. The model takes the channel’s statistical characteristic into account and it can learn the channel state information (CSI) to design secure beamforming in order to against eavesdropping effectively. The performance of bit error rate (BER) is close to the Shannon limit when the receiver demodulates the received signals, and we also ensure that the eavesdroppers cannot demodulate the transmitted signals. In addition, we design a power allocation algorithm, which considers the eavesdropper’s CSI on the relay. Simulation results show that the proposed model is able to reach the pre-defined BER requirements. The model can maximize the system security rate by optimizing power allocation at the relay.