WiFi indoor localization plays an important role in many real-world applications and has gained widespread attentions from both academia and industry over the past decade. While existing works have already achieved remarkable performance under various practical scenarios, they solely utilize multiple WiFi Access Points (APs) to achieve a higher accuracy and do not deeply explore the intrinsic relationships among them. To further unleash the potential of multiple APs and reach the limits of WiFi indoor localization, in this paper, we propose Qidi, a novel multi-APs collaboration based localization system. To the best of our knowledge, we are the first to explicitly reveal three underlying relationships among multiple spatially distributed APs, i.e., consistency, continuity, and non-uniformity. By combining these three basic principles with the unique characteristics of specific tasks, a series of long-lasting practical challenges, such as automatic phase offset calibration, bilateral angle ambiguity, elevation angle estimation with Uniform Linear Array (ULA) and Non-Line-of-Sight (NLoS), can be efficiently resolved. Extensive experiments in various complex environments are provided to demonstrate that Qidi can achieve 2.4°, 3.2° median errors of joint azimuth and elevation angle estimation, and 0.4mlocalization median error even in 20m×20mexhibition hall. Moreover, a one-month longitudinal evaluation conducted on a real-world deployed WiFi ISAC system further validates the effectiveness and robustness of the proposed localization system.

In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.

The past few decades have seen significant advancements in WiFi indoor localization leveraging fine-grained Channel State Information (CSI). However, existing methods often require multiple access points (APs) or the target device to share sensing data. This cooperative localization complicates the deployment of practical localization systems. In this paper, we present SniFi, a non-cooperative localization method that seamlessly integrates with existing WiFi infrastructure. Unlike prior methods, SniFi does not require multiple CSI-capable APs or additional user actions. We use a single network interface card (NIC) to sniff WiFi traffic and obtain the angle information needed for localization. For APs that do not support CSI acquisition, we first utilize Beamforming Feedback Information (BFI) to estimate the Angle of Departure (AoD). Then, by analyzing the sniffed packets, we can obtain the CSI and the corresponding Angle of Arrival (AoA) from the target device to the sniffer. During experiments, we also address the challenge of angle ambiguity of the latest Intel AX210 NIC by integrating map constraints and temporal continuity. These techniques together enable us to achieve accurate localization without making changes to existing AP networks. Extensive evaluations across various environments and APs demonstrate that SniFi achieves decimeter-level accuracy in median error.

Intelligent Reflecting Surface (IRS) has emerged as a crucial technology for indoor localization in future wireless networks. However, existing IRS-aided systems that utilize commercial off-the-shelf (COTS) WiFi devices face challenges due to two key factors. First, the passive reflective nature of IRS generates relatively weak reflected signals, which can be easily drowned out by multipath interference and noise. Secondly, existing works require a large number of IRS codebooks for fine-grained scanning of the entire area, resulting in significant time requirements. This paper introduces SRAL-IRS, which enables swift, robust, and accurate IRS-aided indoor localization using commercial WiFi devices. Through intelligent codebook design and theoretical derivation, we reveal the theoretical relationship between the IRS codebook modifications, target position, and variations in the received signal. Subsequently, SRAL-IRS mitigates the impact of environmental and hardware noise by carefully selecting and combining WiFi subcarriers. Finally, by utilizing the received data from multiple codebooks and subcarriers and leveraging the orthogonality between the noise subspace and the IRS-reflected signal subspace, we can achieve precise localization even with a limited number of IRS codebooks. Real-world implementation of SRAL-IRS, utilizing our designed IRS prototype and COTS WiFi devices, validates its feasibility and effectiveness.

Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for complex scenarios where outdoor human activities can be mistaken as intrusions. In this paper, we propose RoFi, a robust WiFi intrusion detection system which can handle more complex scenarios. It achieves this by exploring the distribution of autocorrelation function (ACF) of Channel State Information (CSI) when intrusion occurs, where likelihood ratio testing is employed to discriminate intrusion and non-intrusion scenarios, eliminating the variance of different environments. Without complex calibration, RoFi achieves an accuracy of over 97.5% in practical deployment, outperforming existing methods.

WiFi-based respiration sensing technology has gained increasing attention due to its contactless sensing capabilities and utilization of existing WiFi devices. However, existing studies are limited to certain scenarios without addressing the motion interference from other individuals. In this paper, we tackle the challenge of robust respiration sensing in the presence of other individuals. Specifically, through an in-depth examination of the correlation between respiratory signals and spatial beam patterns, we develop a respiratory-energy based approach to evaluate the diverse impact of dynamic interference on respiratory signals. When significant interference is detected, we employ a convex-optimization-based beam control strategy, which exploits the inherent characteristics of human respiration, to adaptively adjust the spatial beam pattern. This approach enables a robust and precise gain adjustment between the target and interfering individual, effectively mitigating the impact of interference. Experimental results demonstrate that our approach can reduce the mean absolute error (MAE) of respiration detection by up to 32% compared to state-of-the-art methods, significantly enhancing the accuracy and robustness of WiFi-based respiration sensing.

The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.