Accurately monitoring micron-level vibrations with sub-hertz frequency estimation error is critical for early fault detection in industrial equipment. Existing solutions either rely on powered sensors or suffer from limited accuracy in passive operation, restricting scalability and long-term deployment. We presentVibro-Stethos, a fully battery-free sensing system that accurately captures micron-level vibrations with sub-hertz frequency estimation error. It employs a dual-junction fieldeffect transistor (JFET) analog frontend to convert vibration into impedance modulation and encodes this onto passive RFID backscatter. An embedded RFID chip enables selective tag activation and provides path-invariant reference amplitude normalization. A Graph Attention Network (GAT)-based model adaptively fuses features from spatially distributed tags, enabling robust fault classification under tag sparsity and placement variation. Extensive evaluation demonstrates that Vibro-Stethos achieves amplitude measurement errors within 2$\mu$m, frequency estimation errors below 0.1 Hz, and vibration fault classification accuracy of 93.7%. Real-world deployments on transformers further confirm its diagnostic capability. Vibro-Stethos offers a practical, robust, and accurate battery-free solution for pervasive industrial vibration monitoring.

Reconfigurable Intelligent Surfaces (RISs) have emerged as cost-effective technologies for improving wireless signal transmission. Conventional RIS designs, however, face challenges such as bulkiness, high production costs, limited scalability, and complex installation due to their reliance on wired connections. In this work, we introduce MetaMosaic, a novel RIS platform that repurposes 920 MHz RFID tags into battery-free unit cells, enabling an affordable, scalable, and flexible one-bit phase-modulated RIS. The system is engineered for compatibility with 2.4 GHz Wi-Fi communications while being controlled at 920 MHz. Our design incorporates two central innovations: the transformation of commercial RFID tags into functional unit cells and the development of a tailored neural radiance field to guide efficient reconfiguration. To further enhance global search capability and support multi-hotspot alignment, we extend the system with a genetic algorithm (GA)-based optimization strategy. Compared with the vanilla MetaMosaic, the GA-based MetaMosaic achieves an additional 3.1 dB signal strength improvement and enables simultaneous enhancement for up to five target points. Extensive testing across ten diverse environments demonstrates that MetaMosaic consistently boosts signal strength, with a mean gain of 19 dB over non-RIS setups. This outperforms current leading RIS systems by a 3-fold improvement.

Autonomous vehicles rely on Full Self-Driving (FSD) models for perception and trajectory planning. However, their wireless communication systems face significant challenges in dynamic environments, particularly evident in beamforming dependent vehicle-to-satellite links. Motivated by the powerful sensing capabilities of autonomous vehicles, we introduce Full Smart-Communication (FSC), a unified framework that reuses intermediate outputs from FSD pipelines—such as planned trajectories, occupancy maps, and 3D posture estimates—to model the spatio-temporal radio environment. To avoid accessing raw sensor data directly, FSC leverages processed semantic and kinematic information to anticipate channel conditions and proactively compute beamforming parameters ahead of time. Tested in realistic driving datasets, FSC improves signal strength by up to 15 dB. FSC bridges the gap between autonomous perception and robust communication, enabling proactive, low-latency, and energy-efficient connectivity.

In industrial systems, monitoring micro-scale vibrations is crucial for assessing the operational health of equipment. Existing solutions typically rely on battery-powered sensors or are constrained by LoS requirements. To address these limitations, we propose Vibro-Stethos, a micro-scale,sub-herz vibration sensing system based on battery-free tags. It innovatively utilizes the resistance characteristic of JFET in the ohmic region to convert the voltage generated by PZT vibrations into antenna’s impedance, enabling the modulation of vibration information into the backscatter signal. An integrated RFID chip enhances identifiability and controllability. Additionally, a GCN-based vibration fault recognition model ensures accurate identification of vibration states regardless of tag placement. Experimental results demonstrate that Vibro-Stethos achieves an amplitude error of 2μm and a frequency error of 0.1Hz, with a sensing range of up to 5 meters. It can also recognize seven types of vibration states with 89.3% accuracy and has proven robust and effective in real plant deployments.

Within the burgeoning 6G wireless network landscape, there is an intensified push toward achieving all-encompassing accessibility through integrated solutions spanning a multitude of domains. Notwithstanding recent advancements, the conventional relay-centric communication paradigms grapple with scalability and optimal performance issues. In this paper, we introduce MeAnt ---a versatile IoT platform uniquely architected to foster seamless cross-medium communication by leveraging the compact design of piezoelectric-based mechanical antennas (Piezo-MAs). By capitalizing on the propagation attributes of medium-frequency radios emitted from Piezo-MAs, MeAnt promises communication across diverse environments such as air, water, soil, concrete, and even biological tissue, all while maintaining a compact antenna footprint. Moreover, in light of challenges such as potential interference from AM broadcasts and the intrinsic unidirectional nature of Piezo-MAs, we have developed a finely crafted full-stack communication protocol. Comprehensive tests underscore the system's proficiency, demonstrating a penetration depth of up to 10 m in cross-medium environments and realizing a throughput of 8.7 kbps.

In the era of ubiquitous short-video sharing, an overlooked yet significant privacy risk has arisen: the accidental disclosure of confidential information through reflective surfaces, such as mirrors, glass, or even polished metal. Such reflections can inadvertently disclose sensitive personal details to a broad audience without the awareness of content creators. Our examination of 100 top-rated TikTok short videos reveals that, on average, 37.2% of frames in each video feature identifiable reflective surfaces, posing potential privacy risks. In this work, we introduce a framework designed to scrutinize reflective privacy risks in glass-laden short videos. At the heart of the framework is the development of a reflection-specific neural radiance field, termed RP-NeRF, which enables reflection-aware ray tracing for precise extraction and reconstruction of the reflective scenes from the surfaces they appear on. A detailed field study on the framework indicates that the precision in detecting human presence and recognizing objects from the reconstructed reflective images reaches as high as 90.8% and 89.6%, respectively, even when dealing with a reflective surface that boasts 90% transparency and a mere 4% reflectance rate. These findings highlight the urgent need for greater awareness and advanced solutions to safeguard privacy in our digital age, especially in light of the significant impact of short-video sharing.

Cross-Technology Communication (CTC) is an emerging technology that enables physical-layer direct communication from a WiFi sender to other Internet of Things (IoT) receivers via waveform emulation. Previous research has primarily relied on the reverse engineering to identify the suitable WiFi payloads capable of emulating waveforms similar to desired IoT packets (e.g., ZigBee). However, this approach has several limitations, including irreversibility, scalability challenges, symbol misalignment, and an over-reliance on empirical methods. In this work, we present XiTuXi, a one-size-fits-all solution to automatically achieve the CTC by taking advantage of the neural machine translation (NMT). Inspired by the task comparability between CTC and homophony-based cross-linguistic communication, we employ a well-known NMT model called Transformer to learn the rationale behind translating bit sequences for CTC without human intervention. Specifically, we introduce forward engineering as a solution to tackle the challenge of acquiring training datasets. By utilizing XiTuXi, we effortlessly achieved CTC across 30 protocol combinations (including 802.11b, g, n, ax, ah → Zig-Bee, Bluetooth, LoRa, and Sigfox), ultimately freeing experts from the tedious tasks they faced previously.

Mobile payments have grown significantly recently, driven by their contactless feature that minimizes COVID-19 transmission risks. While NFC offers more security and convenience than barcodes and benefits those with amblyopia, many smartphones lack NFC due to module shortages or security decisions. In this work, we present${\sf MagCode}$, an innovative method connecting NFC readers with cameras, allowing users to enjoy NFC payment security using prevalent camera technology. At the heart of${\sf MagCode}$is the harmless magnetic interference on the CMOS image sensor of a smartphone placed nearby the NFC reader, resulting in a group of barcode-like stripes appearing on the captured images. We take advantage of these stripes to encode the data and achieve simplex communication from an NFC reader to an NFC-denied or NFC-disabled smartphone. In particular, we developed a comprehensive suite of protocols spanning from the physical layer to the transport layer, and we rigorously tested our proof-of-concept prototype on 11 different smart devices. Our extensive evaluations showcase a maximum throughput of 2.58 kbps–surpassing magnetometer-based alternatives by a factor of 58–and demonstrate an average data exchange time of 1.3 seconds for mobile payment transactions between an NFC reader and a smartphone.

Mobile payment has achieved explosive growth in recent years due to its contactless feature, which lowers the infection risk of COVID-19. In the market, near-field communication (NFC) and barcodes have become the de facto standard technologies for mobile payment. The NFC-based payment outperforms barcode-based payment in terms of security, usability, and convenience. It is especially more user-friendly for the amblyopia group. Unfortunately, NFC functionality is unavailable in nearly half of smartphones in the market nowadays due to the shortage of NFC modules or being disabled for security reasons.

Mobile payments have experienced a significant surge in recent years, primarily due to their contactless feature that mitigates the risk of COVID-19 transmission. In this landscape, NFC-based payment outperforms barcode-based payment in terms of security, usability, and convenience. It is especially more user-friendly for the amblyopic community. Unfortunately, NFC functionality is unavailable in nearly half of the smartphones in the market nowadays due to the shortage of NFC modules or being disabled for security reasons.

Recent works demonstrated that speech recognition systems or voice assistants can be manipulated by malicious voice commands, which are injected through various inaudible media, such as ultrasound, laser, and electromagnetic interference (EMI). In this work, we explore a new kind of inaudible voice attack through the magnetic interference induced by a wireless charger. Essentially, we show that the microphone components of smart devices suffer from severe magnetic interference when they are enjoying wireless charging, due to the absence of effective protection against the EMI at low frequencies (100 kHz or below). By taking advantage of this vulnerability, we design two inaudible voice attacks, HeartwormAttack and ParasiteAttack, both of which aim to inject malicious voice commands into smart devices being wirelessly charged. They make use of a compromised wireless charger or accessory equipment (called parasite) to inject the voice, respectively. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and 6 types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of two proposed attacks with commercial charging settings.

Recent advances have demonstrated that voice assistants or speech recognition systems can be manipulated by malicious and inaudible voice commands. However, the previously proposed attacks require an acoustical generator (e.g., a speaker or a capacitor) to trigger mechanical vibrations at a microphone diaphragm. In this work, we investigate a new type of inaudible command attack using wireless chargers. Specifically, the magnetic interference generated by a wireless charger can induce an inaudible sound at a nearby microphone, without triggering any mechanical vibrations, even if the microphone is equipped with a Faraday cage and an internal electromagnetic interference filter already. By taking advantage of this new insight, we will present a novel inaudible command attack demo that can inject inaudible voice commands into smart devices that are being charged or near to a charger. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and six types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of the proposed attack with commercial charging settings.

Given the increasing number of building collapse tragedies nowadays (e.g., Florida condo collapse), people gradually recognize that long-term and persistent structural health monitoring (SHM) becomes indispensable for civilian buildings. However, current SHM techniques suffer from high cost and deployment difficulty caused by the wired connection. In this work, we collaborate with experts from civil engineering to create a type of promising self-sensing concrete by introducing a novel functional filler, called EcoCapsule-a battery-free and miniature piezoelectric backscatter node. We overcome the fundamental challenges in in-concrete energy harvesting and wireless communication to achieve SHM via EcoCapsules. We prototype EcoCapsules and mix them with other raw materials (such as cement, sand, water, etc) to cast the self-sensing concrete, into which EcoCapsules are implanted permanently. We tested EcoCapsules regarding real-world buildings comprehensively.