Video analytics plays a vital role in modern applications such as public safety and smart cities, yet transmitting high-resolution video over wireless networks is severely constrained by bandwidth and latency. Existing semantic communication approaches alleviate communication overhead by discarding irrelevant content, but they often impose prohibitive computational costs on resource-constrained surveillance devices. To overcome this limitation, we propose SenSem, a sensing-assisted semantic communication framework that uniquely leverages channel state information (CSI) to reduce both communication and computation overhead. SenSem first exploits location cues embedded in CSI to estimate the region of interest and crop frames before upload. On the cropped frames, a lightweight semantic evaluator scores blocks, and a joint block selection and transmit power control algorithm maximizes the analytics performance for multi-device uplink; at the edge, a sensing-assisted analytics network injects spatial cues to further boost inference. Extensive evaluations on the WARP platform demonstrate that SenSem consistently outperforms state-of-the-art baselines, achieving superior video analytics accuracy under strict latency constraints. By seamlessly reducing both transmission and device-side computation overhead, SenSem, offers a scalable and efficient solution for next-generation wireless video analytics systems.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on theclear-textbeamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, thefirstattack to manipulate traffic incommodityWi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi- Fi traffic while maintaining a low exposure rate. Furthermore, we also introduce a defense strategy, namely BeamCrypt, that jointly leverages reciprocity and similarity of the channel within the coherence time to authenticate the legitimate user with low overhead. We implement it using WARP and evaluation results verify the effectiveness.

In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on channel state information (CSI). However, due to the CSI being highly specific to users’ characteristics, the CSI-activity relationship is notoriously domain dependent, essentially demanding EI to learn sufficient datasets from various domains in order to gain cross-domain sensing capability. This poses a crucial challenge owing to the EDs’ limited resources, for which storing datasets across all domains will be a significant burden. In this paper, we propose theEdgeCLframework, enabling the EI to continually learn-then-discard each incoming dataset, while remaining resilient to catastrophic forgetting. We design a transformer-based discriminator for handling sequences of noisy and nonequispaced CSI samples. Besides, we propose a distilled core-set based knowledge retention method with robustness-enhanced optimization to train the discriminator, preserving its performance for previous domains while preventing future forgetting. Experimental evaluations show that EdgeCL achieves 89% of performance compared to cumulative training while consuming only 3% of its memory, mitigating forgetting by 79%.

Wi-Fi is deemed as a promising sensing media due to its ubiquity, yet Wi-Fi sensing is known to be confined by its limited bandwidth that leads to insufficient range resolution. Though sampling a wider spectrum multiple times can enable wideband sensing, its practicality is still hampered by the need for accessing Wi-Fi firmware. In this paper, we propose μCeiver-Fi to exploit spectrum resources for fine-granularity Wi-Fi sensing; it relies solely on a commodity multi-link receiver. Since the channel samples from multiple links under the same receiver can still be misaligned, we first innovate in a comprehensive calibration process to align these samples. This is followed by a novel optimization framework to extend effective sensing bandwidth to GHz-level using only a few channel samples. Finally, we specifically design a spectral representation for sensing information in order to bridge between wideband signals and diversified downstream applications. Through comprehensive evaluations in Wi-Fi pose estimation task, we demonstrate the promising performance of μCeiver-Fi in fine-granularity sensing.

Wi-Fi human sensing, boosted by latest progress in both system innovation and deep analytics, has demonstrated ever-increasing resolution of users' activities. Nonetheless, it may become a spy on users' private activities such as password entry or intimate social interactions. Existing countermeasures include signal obfuscation and adversarial perturbations to hamper and confuse Wi-Fi sensing, yet they both require substantial changes in Wi-Fi hardware/firmware, and they at most stay at user level in protection granularity. This paper presents Poison2Cure, the first semantic-level privacy-preserving framework for Wi-Fi human sensing systems, with full compatibility to any underlying hardware. The innovation behind Poison2Cure lies in feeding poisoned training data from (privacy-sensitive) users to the neural model for Wi-Fi sensing, degrading only the sensing for private activities while retaining that for regular ones. Moreover, we tackle the harsh conditions where the neural model is kept confidential and/or preceded by data cleansing. Our extensive evaluations demonstrate that Poison2Cure reduces over 76% of the accuracy for the private activities while keeping the accuracy for regular activities largely intact.

As a technology with ubiquitous presence in unlicensed spectrum, Wi-Fi has demonstrated prominent capabilities in both communication and sensing. However, since the bandwidth requirements for communication and sensing differ significantly, channel bandwidths excessive for communication (e.g., 160 MHz) still fail to achieve multi-person sensing. Though stitching multiple consecutive channels to expand the effective bandwidth sounds plausible, it may never reachultra-wideband(UWB) in practice. To this end, we propose UWB-Fi as a novel Wi-Fi sensing framework with ultra-wide bandwidth, leveraging only discrete and irregular channel samples. We first design a fast channel hopping scheme to enable arbitrary channel sampling across 4.7GHz bandwidth on commodityWi-Fi hardware without interrupting default communications. As no algorithm exists to exploit such channel samples, we establish a theoretical analysis driven bycompressive sensing, so as to enable anexplainabledeep learning model. This model transforms sparse channel samples into high-dimensional (position) spectra, effectively avoiding thebias-variance dilemmain parameter estimation while encoding sufficient information for general sensing. Our extensive evaluations demonstrate that UWB-Fi successfully achieves centimeter-level fine-granularity multi-person sensing.

Accurate gesture-based interactions are crucial for enhancing the immersive experience in VR (virtual reality) systems; they in turn necessitate gesture positioning and recognition inphysical world. However, existing VR gesture recognition methods are predominantly vision-based, incurring high computational demands and raising privacy concerns. Meanwhile, Wi-Fi-based gesture recognition methods, deemed as promising complement to vision-based ones, typically lack gesture positioning capabilities. To this end, we propose VR-Fi, a gesture positioning and recognition system leveraging VR(-headset)-embedded Wi-Fi. To position gestures across different areas, VR-Fi innovates in afrequency-hopping bandwidth expansion(FHBE) technique to improve spatial resolution for locating a target. Additionally, VR-Fi innovates in neural models to process the FHBE-enhanced Wi-Fi CSI (channel state information) and enable the multi-task requirements of the joint positioning and recognition of hand gestures. Extensive experimental results demonstrate that VR-Fi achieves a positioning accuracy of 94.47%, a recognition accuracy of 92.13%, and a joint accuracy of 89.47%.

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, the first attack to manipulate traffic in commodity Wi-Fi systems; it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it; the results reveal that BeamCraft effectively manipulates Wi-Fi traffic while maintaining a low exposure rate.

The limited bandwidth of Wi-Fi severely confines the granularity (especially in differentiating multiple subjects) of Wi-Fi sensing, posing a significant challenge for its wide adoption. Though utilizing multiple channels to expand the effective bandwidth sounds plausible, continuous spectrum stitching towards ultra-wideband (UWB) is far from practical given various constraints (e.g., the runtime channel availability and inconsistent channel responses across a wide bandwidth). To this end, we propose UWB-Fi as a novel Wi-Fi sensing system with ultra-wide bandwidth, leveraging only discrete and irregular channel sampling. We first design a fast channel hopping scheme to perform arbitrary sampling across 4.7GHz (i.e., 2.4 to 7.1GHz) bandwidth on commodity Wi-Fi hardware without interrupting default communications. As no signal processing tool is available to handle such channel samples, we innovate in a model-based deep learning approach that translates discrete channel samples to high-dimensional spectral parameters; this method successfully avoids the bias-variance tradeoff in parameter estimation, while filtering out hardware-related offsets inherent to Wi-Fi. Through extensive evaluations, we demonstrate that UWB-Fi successfully achieves fine-granularity sensing, enabling centimeter-level resolution for indoor multi-person sensing.

Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multi-parametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net (AEN) method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function (CDF) based on the multiplication theorem and the Markov chain Monte Carlo (MCMC) method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.