Wi-Fi computational imaging has emerged as a promising paradigm for non-intrusive sensing; however, its practical deployment is severely hindered by dense physical multipath clutter, hardware phase quantization errors, and the limited bandwidth of commercial Wi-Fi. Traditional beamforming-based algorithms often completely lose target focus in complex environments, resulting in severe ghosting artifacts. To overcome these fundamental limitations, we propose RISimg, a novel robust Wi-Fi imaging framework empowered by the spatiotemporal coding of Reconfigurable Intelligent Surface. We design a differential coding strategy to guarantee a well-conditioned and noise-robust sensing matrix, and propose a multi-frequency sparse reconstruction algorithm based on the Least Absolute Shrinkage and Selection Operator. By constructing a large-scale overdetermined system, this physics-driven approach effectively suppresses multipath interference and hardware errors, successfully recovering the basic morphological outlines of complex targets. To further enhance the imaging performance, we propose leveraging a Conditional Diffusion Model to refine the imaging results. By utilizing the reconstruction as a structural prior, a carefully designed Conditional U-Net progressively refines the image through a generative reverse sampling process, restoring high-fidelity continuous boundaries. Extensive evaluations using a hardware prototype built with commercial Wi-Fi devices and a low-cost metasurface demonstrate that the final cascaded CDM achieves an unprecedented Structural Similarity Index Measure of 0.9192 and a Spatial Correlation Coefficient of 0.7336, paving a robust new avenue for Wi-Fi imaging.

Unmanned Aerial Vehicle (UAV) equipped with a gateway shows great potential for data collection in many scenarios, especially for the areas lacking of public network infrastructures. However, our in-field experiments on UAV-assisted LoRa networks show that a large throughput gap exists between the ground-to-air and ground-to-ground transmissions. We find that the misalignment of the radiation direction of transceiver antennas with height difference leads to additional signal strength loss, which is ignored by existing ground-to-ground transmissions. In this paper, we propose a directivity-aware ground-to-air link model called annulus model to quantify the impact of directivity on the ground-to-air link quality. Based on our model, a new ground-to-air channel access scheme for UAV-assisted LoRa networks,PreLoRa, is proposed. By predicting the link quality variations,PreLoRaschedules the transmission periods and adopts optimal transmission configurations for ground nodes to improve the link throughput. We implementPreLoRaon commercial LoRa platforms and extensively evaluate its performance in the wild. Experimental results show thatPreLoRacan significantly improve data collection throughput by up to 65.5% compared to baseline methods.

Due to the poor indoor coverage and positioning accuracy, existing indoor GPS positioning systems leverages additional RF infrastructure as relay with known position. However, in practice, learning the relay position requires establishing an additional connection between user and relays, which is user unfriendly and even infeasible. In this paper, we propose GPSWindow, a plug-and-play indoor GPS positioning system without the prior knowledge of the relay position. By attaching optically transparent metasurfaces to windows, GPSWindow focuses the incident signal towards determined direction and provide an indoor continuous GPS signal coverage. We exploit the difference between consecutive satellite measurements and the Doppler shift measurements to recover the satellite-to-user true distance from the measured satellite-metasurface-user distance, and then locate the user using the traditional trilateration positioning method, eliminating the requirement of relay position. We also design an error correction method that leverages IMU on smartphone and the Doppler shift information to enhance GPSWindow in mobile scenarios. Extensive real-world experiments demonstrate that GPSWindow can provide continuous position service and achieve median positioning accuracy of 3.6m in indoor environments.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this article, we propose PolarScheduler , a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. To deal with packet collisions in a multiple-node environment, we further propose an Attitude-aware Slot-allocation MAC protocol, which avoids both packet collisions and polarization loss. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this paper, we propose PolarScheduler, a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.