RFID has revolutionized item-level intelligence in IoT ecosystems, yet static localization with passive tags remains challenging due to multipath interference inherent in RF signals. We present LuxTag, the first system to enable visible light-based sensing and localization using standard, commercial RFID tags by transforming them into ambient light sensors. Our key insight leverages the discovery that photon-induced leakage currents in passive RFID ICs modulate their persistence time (i.e., the duration a tag remains operational after RF excitation ceases) proportional to ambient illuminance. LuxTag introduces two innovations: (i) a first-principles model characterizing how ambient light alters tag persistence time, enabling battery-free light sensing without hardware modifications; (ii) a differential measurement technique and zero-shot calibration method to isolate light effects and autonomously derive tag parameters, ensuring robust and accurate static localization system using COTS RFID infrastructure. Extensive experiments demonstrate that LuxTag achieves a mean light intensity error of 3.6 lux and 60.7% improvement over state-of-the-art static RFID localization. By synergizing the ubiquity of RFID with the multipath resilience of optical sensing, LuxTag opens new avenues for static RFID localization in smart warehouses, retails, and beyond.

Radio Frequency Identification (RFID) technology plays a pivotal role in modern applications ranging from retail and logistics to healthcare and security. However, a fundamental challenge persists in large-scale RFID systems: maximizing the coverage capacity of readers – the ability to reliably identify and communicate with the maximum number of tags within their operational range. While previous research has explored various aspects of RFID performance, the systematic optimization of coverage capacity remains underinvestigated. This paper addresses this gap by developing a comprehensive framework that integrates theoretical analysis with practical implementation strategies. We first establish a novel coverage capacity model that incorporates critical factors such as tag spatial distribution and link loss dynamics, providing a theoretical foundation for determining the upper bounds of reader performance. Building on this, we propose a cutoff-power-based optimization approach that dynamically adapts to real-world conditions without relying on predefined system parameters. Furthermore, to extend coverage across larger areas, we investigate advanced multi-reader configurations and present strategic deployment methodologies. Our framework is designed to characterize the baseline coverage capacity of existing RFID readers, rather than to enlarge the interrogation zone through additional hardware or protocol modifications. Moreover, it can be naturally extended to advanced configurations such as MIMO or phased-array systems once their antenna parameters are specified. The effectiveness of our approach is validated through extensive experiments using commercial RFID hardware, demonstrating measurable improvements in coverage capability. By bridging theoretical principles with practical constraints, this work offers actionable insights for designing and deploying high-performance RFID systems.

Touch switches are the foundation of a smart automation system that provides us with a seamless and intuitive interaction between users and smart devices. Typical touch switches and recent gesture sensing solutions suffer from some limitations including low scalability, low robustness, hardware modifications, and long time delay. In this work, we propose an RFID tag based touch switch called Tach that provides users with a battery-free, low-latency, low-cost, and plug-n-play touch-based input primitive, with no need for any hardware modifications. In Tach, we turn a passive tag as a touch switch and use the principle of impedance mismatch to sense the touch action by users. More specifically, we observe that when the finger touches the tag, the impedance of tag antenna changes. This change gives rise to impedance mismatch between the tag antenna and the tag chip, which further causes a frequency shift and a specific signal pattern. We design a continue wavelet transform based segmentation scheme and a dynamic time warping approach to achieve accurate and reliable touch identification in real scenarios. Additionally, multi-tag switches are discussed to support more user inputs. We implement Tach with COTS RFID devices. Extensive experiments show that the sensing accuracy reaches 96.5% with a low latency of 300ms, regardless of different users and environmental changes. The promising results might open up a new avenue in the design of touch switches in the near future. Code and datasets will be made available.

With 52.8 billion RFID tags used worldwide in 2024, a common basic functionality needed by RFID-enabled applications is cardinality estimation — to quickly estimate the number of distinct tags in an RFID system. Although many advanced solutions have been proposed over the past decade, they suffer from one major limitation in practical use: they need to either modify the existing RFID standard or obtain MAC-layer information, both of which however cannot be supported by commercial off-the-shelf (COTS) devices. In this paper, we revisit the counting problem and propose a novel counting scheme called average time duration based counter (ATD) that quickly estimates the number of distinct tags in a standards-compliant manner. Compared with existing work, the competitive advantage of ATD is that it can be directly deployed on a COTS RFID system, with no need for any hardware modifications. In ATD, we found a new and measurable indicator — the time duration between two adjacent singleton slots, which depends on the number of tags. Following this observation, we derive the theoretical relationship between the time indicator and the number of tags and then give the proof of the estimation as well as its parameter settings. Additionally, we propose a flag-flipping solution to address the overlapping problem in the multi-reader case. We implement ATD in a COTS RFID system with 1000 tags. Experimental results show that ATD is$4.2\times $faster than the baseline of tag inventory; the performance gain will be further increased in a larger RFID system.

For driving assistance and autonomous driving systems, it is important to differentiate between dynamic objects such as moving vehicles and static objects such as guard rails. Among all the sensor modalities, RADAR and FMCW LiDAR can provide information regarding the motion state of the raw measurement data. On the other hand, perception pipelines using measurement data from ToF LiDAR typically can only differentiate between dynamic and static states on the object level. In this work, a new algorithm called DONEX was developed to classify the motion state of 3D LiDAR point cloud echoes using an occupancy grid approach. Through algorithmic improvements, e.g. 2D grid approach, it was possible to reduce the runtime. Scenarios, in which the measuring sensor is located in a moving vehicle, were also considered.