With the continuous increase of location-aware wireless applications, various indoor localization techniques based on radio frequency (RF) electromagnetic waves,^1,2 ultrasound,³ and infrared⁴ (IR) have been proposed. For low-frequency RF-based approaches, such as wireless local networks^4,5 (WLAN) and ultra-wideband,⁶ the localization accuracy is highly related to the density and distribution of the routers, and the wireless RF signals are liable to suffer from RF interference.⁷ The average localization error for RF localization is several meters, and this accuracy level cannot be applied to the traditional trialeration process to localize user devices correctly due to multipath propagation.⁸ Ultrasound-based approaches can provide a high localization accuracy of 10 cm but require expensive and specialized infrastructures.⁹ IR-based indoor localization can achieve relatively high localization accuracy of centimeter-level, but its transmission distance (1 m) is shorter than that of VLC (10 m).¹⁰ It also leads to a higher cost since it is necessary to install large numbers of IR nodes in the room. Recently, visible light communication (VLC) localization methods have drawn much attention of researchers and industries. Unlike conventional techniques, VLC-based localization is immune to electromagnetic interference and can be used in RF-restricted zones¹¹ (e.g., airport and hospital). It can achieve centimeter-level accuracy¹² 13. 14. 15. 16. 17. 18. 19. 20. 21. ^{– 22} with lower cost infrastructures by remodifying the existing illumination system to VLC transmitters. Moreover, it offers a higher security for users since visible light signals cannot transmit across the walls and may not be accessed by eavesdroppers from other rooms.¹²

In this paper, we study the secrecy performance of non-orthogonal multiple access (NOMA) enabled visible light communication (VLC) broadcast channels in the presence of an active eavesdropper (Eve). The considered VLC system consists of multiple separately distributed light-emitting diodes arrays and multiple randomly located users (UEs) in an indoor room. User clustering is conducted to reduce the implementation complexity of successive interference cancellations. Two cooperative precoding strategies based on zero-forcing (ZF) and maximum ratio transmission (MRT) are designed using the effective channel of each cluster. Based on each precoding strategy, a sum secrecy rate maximization problem is developed to obtain the near-optimal power allocation (PA) to strengthen UEs’ confidential transmission and degrade Eve’s reception under minimum secrecy rate requirement, peak amplitude, non-negativity, and power constraints. To tackle the challenging non-convex problem for each precoding strategy, equivalent transformations and arithmetic-geometric mean approximation are conducted to convert the original problem into a series of geometric programming (GP) problems. Based on the reformulated problems, iterative algorithms are proposed to obtain near-optimal solutions by solving the GP problems through successive convex approximations. The convergence and complexity analysis of the proposed algorithms are studied. Simulation results show that the sum security performance of the proposed PA approach outperforms conventional PA approaches in both ZF-based and MRT-based precoder schemes. The effectiveness of applying NOMA compared with the orthogonal multiple access-based scheme is also validated for the proposed system.

In this paper, we consider improving the secure performance of multiple-input single-output visible light communication channel in the presence of multiple eavesdroppers with multiple photodiodes. Our goal is to design an optimal artificial-noise (AN) aided transmission strategy to maximize the achievable secrecy rate subject to both sum power constraint and peak amplitude constraint. We consider a joint optimization of the transmit covariance and AN covariance for the non-convex secrecy rate maximization (SRM) problem. In order to solve it, the SRM problem is transformed into a series of single-variable semidefinite programming (SDP) problems without losing any optimality, and a one-dimensional search based algorithm is proposed to handle the converted problem, with polynomial complexity. By exploiting Karush-Kuhn-Tucker conditions of the problem, beamforming is found to be optimal for the confidential information transmission. Simulation results show the superior performance of the proposed AN-aided method compared with two other AN-aided methods and no AN-aided method.

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.

This article investigates the physical-layer security for user-centric cell-free massive multiple-input-multiple-output (UC-CF-mMIMO)-enabled Internet of Things (IoT) network with multiple active eavesdroppers (Eves). We assume that each Eve intends to decode the information of the user (UE) closest to it and is intelligent enough to eliminate interference of other UEs perfectly. Two eavesdropping modes of noncolluding and colluding Eves are considered. Additionally, access points (APs) inject artificial noise (AN) into confidential data signal to hamper the eavesdropping of Eves. To assess the secrecy performance of UC-CF-mMIMO-enabled IoT network, we derive the worst case secrecy rate expression. Then, a max-min secrecy rate (MMSR) problem is formulated via joint optimization of AP clustering, AN selection, and power allocation, which aims to maximize the minimal secrecy rate among attacked UEs while adhering to the Quality-of-Service requirement of UEs, the limitation on the number of UEs and AN sent by each AP, and maximum transmit power limitation of APs. Due to the mixed-integer and nonconvex nature, the formulated problem is tackled via employing the ${\ell }_{1}$ -norm relaxation and successive convex approximation approach. Finally, we obtain a near-optimal solution to the MMSR problem by iteratively solving a sequence of second-order cone programmings. Simulation results demonstrate the superiority of proposed approach by the comparison with some existing schemes.

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.