Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor–critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.

Conventional hybrid precoding and combining based transceivers require a large number of high-resolution radio frequency (RF) phase shifters (PSs), which impose prohibitive hardware costs and power consumption. To address the above issue, both partially connected RF PSs and low-resolution PSs have been proposed. However, the performance limits of these low-cost designs have not been investigated theoretically. Furthermore, there is room for improvement in their spectral efficiency. To fill this knowledge gap, we derive the mean square error performance discrepancy between an optimal precoder/combiner and the hybrid analog-digital precoder/combiner under the constraint of 1-bit PSs relying on lattice theory. Then, by observing that this performance gap can be reduced by deactivating parts of the PSs whilst improving both the spectral and energy efficiency, we develop an adaptive RF PS connection network. To resolve the associated hybrid precoding and combining problems, we appropriately adapt Babai's algorithm from the lattice decoding literature. Our simulation results demonstrate the superiority of the proposed scheme both in terms of its spectral and energy efficiency.

Secure communication with noisy feedback is studied in this correspondence. Unlike the majority of previous research efforts where the eavesdropper's channel state information (CSI)-either instantaneous or statistical CSI, is known by the legitimate users, a joint feedback and artificial noise scheme without any eavesdropper's CSI is proposed. The proposed scheme lets the source and the legitimate destination alternatively transmit private messages and degrade the eavesdropper's channel quality in all transmission phases. An insightful discovery is that if the transmit power is lager than a certain threshold, a positive secrecy capacity can be achieved, which overcomes the problem that the secrecy cannot be achieved when the legitimate user's CSI is worse than the eavesdropper's. The secrecy capacity maximization problem is investigated by adaptive power allocation for the private signals and the artificial noise where a near optimal solution of the power of artificial noise is obtained under the high SNR situation. Finally, the numerical results verify the theoretical analysis and illustrate the secrecy performance of the proposed scheme.

This paper investigates the secure transmission in multiple access wiretap channels, where multiple legitimate users transmit private information to an intended receiver in the presence of multiple eavesdroppers. In order to improve security, we propose a novel cooperative jamming scheme, in which users do not share channel state information (CSI) but the legitimate channels will not be degraded by the artificial noise. The basic idea is to make each user exploit its own CSI in two slots to design artificial noise, so that the intended receiver can eliminate all the artificial noise but the eavesdroppers cannot. In this process, the interference between users plays a key role to achieve security, because it guarantees that the artificial noise from different users helps each other. We consider the non-collusion and collusion of eavesdroppers and analyze the secrecy performance for both scenarios. We adopt the secrecy sum-rate as the main metric, and show that positive secrecy sum-rate can be achieved by using the proposed scheme. Especially, we observe that when eavesdroppers collude and their additive white Gaussian noise (AWGN) close to zero, the number of users must not be less than twice the number of eavesdroppers to ensure positive secrecy sum-rate. Finally, simulation results are provided to corroborate our theoretical findings.

The advancement of cloud-assisted Internet of Things (IoT) has amplified the usability for secure and searchable image retrieval, addressing the growing demand for privacy protection in digital multimedia. Current encrypted image retrieval schemes focus either on search precision or search efficiency, making them less viable for deployment on IoT devices with limited resources. Therefore, in this article, we present a Privacy-Preserving Clustering-Based Image Retrieval (PPCBIR) scheme for IoT environment. First, we employ a Convolutional Neural Networks (CNN) for feature extraction and design an extended k-Nearest Neighbor (kNN) algorithm to protect the privacy of image features. Then, we build a novel clustering-based hierarchical index tree structure to improve retrieval efficiency without compromising data privacy. Subsequently, a matrix re-encryption technique is implemented to achieve the availability of multi-terminal key distribution in IoT. Furthermore, we propose an index merging method that is scalable to index trees constructed by different data owners. Finally, formal security analysis demonstrates that PPCBIR is resistant to various threat models. Extensive experiments using authentic datasets indicate that our proposed scheme is comparable to linear retrieval in search accuracy and outperforms existing state-of-the-art schemes in search efficiency, and demonstrate its practicability in IoT.