arXiv subjects

Wenye Wang

Publications and source records attributed to Wenye Wang.

4 recordsLinked to original sources

arXiv2026

Commodity RF Sensing of Belowground Tuber Growth

Belowground yield-forming organs of root and tuber crops are difficult to measure during growth, and management therefore relies on aboveground proxies and destructive sampling. Aboveground wireless links could provide a low-cost, non-invasive alternative, but strong attenuation and soil-dependent variability make repeatable subsurface sensing challenging. In a controlled greenhouse pot study of sweet potato, we deploy aboveground antennas in a line-of-sight-suppressed geometry and collect daily swept-frequency channel spectra together with standardized cellular link indicators, revealing consistent frequency-dependent attenuation and rippling as tubers develop. Here, we show that swept-frequency measurements in the 2.0-3.5 gigahertz band yield four interpretable spectral features that classify day-indexed growth stages with up to 87.5% accuracy across two soil recipes and two moisture regimes, and that fusing cellular link-quality indicators enables 5-centimeter-grid tuber localization with up to 95.0% accuracy, providing a proof-of-concept for subsurface crop monitoring without buried sensors, and motivating validation across cultivars and larger soil volumes.

cs.OH↗

arXiv2026

DuTrack: Long-Term Indoor Human Tracking with Dual-Channel Sensing and Inference

Wi-Fi tracking technology demonstrates promising potential for future smart home and intelligent family care. Currently, accurate Wi-Fi tracking methods rely primarily on fine-grained velocity features. However, such velocity-based approaches suffer from the problem of accumulative errors, making it challenging to stably track users' trajectories over a long period of time. This paper presents DuTrack, a fusion-based tracking system for stable human tracking. The fundamental idea is to leverage the ubiquitous acoustic signals in households to rectify the accumulative Wi-Fi tracking error. Theoretically, Wi-Fi sensing in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios can be modeled as elliptical Fresnel zones and hyperbolic zones, respectively. By designing acoustic sensing signals, we are able to model the acoustic sensing zones as a series of hyperbolic clusters. We reveal how to fuse the fields of electromagnetic waves and mechanical waves, and establish the optimization equation. Next, we design a data-driven architecture to solve the aforementioned optimization equation. Experimental results show that the proposed multimodal tracking scheme exhibits superior performance. We achieve a 89.37% reduction in median tracking error compared to model-based methods and a 65.02% reduction compared to data-driven methods.

eess.SP↗

arXiv2026

Uni-Fi: Integrated Multi-Task Wi-Fi Sensing

Wi-Fi sensing technology enables non-intrusive, continuous monitoring of user locations and activities, which supports diverse smart home applications. Since different sensing tasks exhibit contextual relationships, their integration can enhance individual module performance. However, integrating sensing tasks across different studies faces challenges due to the absence of: 1) a unified architecture that captures the fundamental nature shared across diverse sensing tasks, and 2) an extensible pipeline that accommodates future sensing methodologies. This paper presents UNI-FI, an extensible framework for multi-task Wi-Fi sensing integration. This paper makes the following contributions: 1) we propose a unified theoretical framework that reveals fundamental differences between single-task and multi-task sensing; 2) we develop a scalable sensing pipeline that automatically generates a multi-task sensing solver, enabling seamless integration of multiple sensing models. Experimental results show that UNI-FI achieves robust performance across tasks, with a median localization error of approximately 0.54 m, 98.34% accuracy for activity classification, and 98.57% accuracy for presence detection.

eess.SP↗

arXiv2012

Enabling Adaptive Rate and Relay Selection for 802.11 Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Moreover, for an 802.11 network that employs geographic opportunistic routing with adaptive rate and relay selection, we propose a novel method to reduce the effect of the feedback delay at the MAC layer in the presence of Rayleigh fading. This method exploits channel reciprocity and fading prediction and does not require significant modification to the existing 802.11 frame structure. Extensive network simulations demonstrate that the proposed approach significantly improves the throughput, delay, and packet delivery ratio for high mobile velocities relative to previously proposed approaches that employ outdated CSI at the transmitter.

cs.NI↗