The proliferation of hidden WiFi cameras has raised serious privacy concerns, making their accurate detection and localization essential for the secure development of future intelligent wireless networks. However, existing solutions often require substantial user involvement, large movement spaces, predefined system parameters, or pre-collected training data, limiting their practicality and scalability. In this paper, we present DiffLoc+, a novel and low-cost system that localizes hidden WiFi cameras by harnessing the fundamental physical principle of electromagnetic diffraction. When an obstacle crosses the line-of-sight path between a transmitter and a receiver, it causes a distinctive signal attenuation pattern. We theoretically analyze the feasibility of exploiting this phenomenon for localization and identify two key conditions for building an unbiased diffraction-based model: symmetry and observability. To satisfy these conditions, DiffLoc+ introduces a controllable diffraction generation mechanism that precisely rotates a small metal plate around a WiFi receiver (e.g. a Raspberry Pi), producing a stable and predictable diffraction “shadowing” effect. We then construct an unbiased localization model that maps this effect to the azimuth of the camera. To ensure the robustness of the theoretical model in real-world applications, DiffLoc+ further introduces two robustness-enhancing mechanisms: (1) an attenuation-region difference-driven subcarrier selection method, which filters subcarriers that reliably reflect the diffraction attenuation pattern by quantifying the signal contrast between diffraction- and reflection-dominated regions; and (2) an uncertainty evaluation framework that integrates result consistency and diffraction signal quality to eliminate unreliable estimates. Implemented entirely with commodity off-the-shelf (COTS) hardware, DiffLoc+ achieves an average angular error of 11.92° across six diverse indoor environments and eleven commercial camera models, demonstrating its effectiveness and robustness.

The large-scale deployment of IoT devices accelerates intelligent applications but also brings significant security risks. Device detection helps mitigate these risks by identifying unauthorized or rogue devices and improving visibility into network activity. However, existing device detection methods based on network and transport layer protocols face two key challenges: encrypted traffic conceals protocol information, and most approaches fail to detect out-of-distribution (OOD) devices, limiting their effectiveness in real-world scenarios. To address these issues, this paper proposes an OOD detection method based on the 802.11 protocol. Specifically, we first extract intrinsic packet attributes from the 802.11 protocol headers, including transmission timing patterns and packet structure characteristics, without relying on any network or transport layer information. Then, these features are input into a bidirectional long short-term memory (LSTM) model to learn sequential dependencies, and the extracted feature embeddings are evaluated through k-nearest neighbor (KNN) distance calculation to detect both in-distribution (ID) and OOD samples. Experiments conducted on 12 commercial IoT devices spanning 8 categories demonstrate that the proposed method achieves effective device identification and OOD detection performance.

Generalizable WiFi gesture recognition has gained increasing attention for its contactless operation, ubiquitous infrastructure and enhanced robustness. Among existing methods, source-free domain adaptation (SFDA) stands out by preserving privacy and reducing computational demands without relying on source data. Current methods typically process low-level WiFi signals and their high-level semantic representations from a unified perspective, making temporal semantic learning highly susceptible to low-level signal noise and lacking consistent semantic guidance for cross domain alignment, thereby limiting the effectiveness. In this paper, we propose ViFi, a novel SFDA framework specifically designed for cross-domain WiFi gesture recognition. Unlike prior work, ViFi introduces a viewpoint-hierarchical strategy that explicitly processes cross-domain sensing from two perspectives: the perceptual (signal-level) and the semantic (gesture-level). This separation mitigates the impact of signal noise on high-level semantics while preventing semantic space drift during domain alignment. ViFi operates in two key stages. First, it anchors the perceptual encoder and employs masked signal semantic reconstruction to learn robust high-level temporal semantics. Then, it freezes the semantic encoder and aligns the perceptual encoder across domains, again leveraging masked reconstruction to ensure alignment under a unified and meaningful semantic space. We evaluate ViFi on a public dataset, and experimental results show that our viewpoint-hierarchical method achieves over 15% improvement compared to the baseline and significantly outperforms state-of-the-art approaches.

Hidden wireless cameras pose significant privacy threats, necessitating effective detection and localization methods. However, existing localization solutions often require impractical activity spaces, expensive specialized devices, or pre-collected training data, limiting their practical deployment. To address these limitations, we introduce CamLopa, a training-free wireless camera localization framework that operates with minimal activity space constraints using low-cost, commercial-off-the-shelf (COTS) devices. CamLopa can achieve detection and localization in just 45 seconds of user activities with a Raspberry Pi board. During this short period, it analyzes the causal relationship between wireless traffic and user movement to detect the presence of a hidden camera. Upon detection, CamLopa utilizes a novel azimuth localization model based on wireless signal propagation path analysis for localization. This model leverages the time ratio of user paths crossing the First Fresnel Zone (FFZ) to determine the camera's azimuth angle. Subsequently, CamLopa refines the localization by identifying the camera's quadrant. We evaluate CamLopa across various devices and environments, demonstrating its effectiveness with a 95.37% detection accuracy for snooping cameras and an average localization error of 17.23°, under the significantly reduced activity space requirements and without the need for training. Our code and demo are available at https://github.com/CamLoPA/CamLoPA-Code.

WiFi channel state information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors, such as data privacy protection, hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this article, we propose Wi-SFDAGR, a WiFi-based source-free domain adaptation gesture recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods.

Pulmonary function testing is a crucial examination for respiratory diseases. Current medical spirometers are bulky and inconvenient, while available portable spirometers are extremely expensive and often lack accuracy. Furthermore, both devices require direct contact, inevitably increasing the cross-infection risk. To tackle these challenges, we propose Wi-Pulmo, an end-to-end deep learning-based Wireless System that utilizes WiFi channel state information (CSI) to provide contact-free, convenient, cost-effective, and precise pulmonary function testing outside the clinical setting. Based on the analysis of thoracic and abdominal movement patterns, Wi-Pulmo first validates the feasibility of using WiFi to estimate pulmonary function. Then, Wi-Pulmo designs an efficient fine-grained sensing quality-based algorithm for complete exhalation segmentation. Additionally, a relevant interference-tolerant learning algorithm based on variational inference is proposed to accurately map the CSI of WiFi signals to pulmonary function. Extensive experiments achieved average monitoring error rates of 2.59% for normal subjects in daily scenarios and 5.87% for real patients in tertiary hospitals over a two-month period. These satisfactory results demonstrate the strong effectiveness and robustness of Wi-Pulmo. Furthermore, our findings in clinical reveal a close correlation between chronic diseases and pulmonary function.

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known class during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based open-set gesture recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a twofold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the channel state information (CSI) ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness.

Describing the dynamics of information diffusion within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information diffusion. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information diffusion process. Extensive experiments are conducted on six datasets to predict the information diffusion range and the diffusion trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information diffusion predictions.

Modeling the information propagation process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information propagation process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking the dynamic and evolving nature of user attitudes and the impact of the public opinion environment. In this paper, we propose a novel model, UAPE, which considers the influence of the aforementioned factors on the information propagation process. Specifically, UAPE regards the user's attitude towards the topic as dynamically changing, with the change jointly affected by multiple users simultaneously. Furthermore, the joint influence of multiple users can be considered as the impact of the public opinion environment. Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01 %, surpassing the performance of existing research.

Hidden WiFi cameras pose significant privacy threats, necessitating effective localization methods. In this work, we introduce CamLoPA, a system designed for the detection and localization of WiFi cameras. CamLoPA achieves this in just 45 seconds of user walking. It begins by analyzing the causal relationship between WiFi traffic and user movement to identify the presence of a snooping camera. Upon detection, CamLoPA utilizes a novel azimuth location model based on WiFi signal propagation path analysis to localize the hidden camera. Comprehensive evaluations demonstrate that CamLoPA can accurately and swiftly detect and localize snooping WiFi cameras with minimal constraints.