We present a two-channel frequency selective 2.4 GHz backscatter modulator for Wi-Fi (IEEE 802.11) and Bluetooth backscatter communication. Prior backscatter modulators create unwanted spectral pollution by backscattering every signal incident over the entire antenna bandwidth. In contrast, the proposed frequency selective modulator employs narrow-band bulk acoustic wave (BAW) filters having a 20 MHz bandwidth to enable backscatter modulation in either Wi-Fi channel 1 (2401-2423 MHz) or Wi-Fi channel 11 (2451-2473 MHz). The measured differential reflection coefficient |Delta Gamma| exceeds 0.568 across Wi-Fi channel 1 and 0.601 across Wi-Fi channel 11. The opposite-channel rejection exceeds 47.02 dB from Wi-Fi channel 1 to Wi-Fi channel 11 and 48.99 dB from Wi-Fi channel 11 to Wi-Fi channel 1. Both simulated and measured performance are presented and compared. When generating BPSK modulation at 11 Mbps (as used by IEEE 802.11b), the modulator has an average power consumption of 20.9 mu W and a measured energy figure of merit as low as 1.9 pJ/bit; meanwhile, when generating FSK modulation at 1 Mbps (as used by Bluetooth Low Energy), the modulator has an average power consumption of 1.9 mu W and a measured energy figure of merit as low as 1.9 pJ/bit. By addressing the key unsolved problem of backscatter-induced spectral pollution, this work paves the way for backscatter devices to become "first-class citizens" of the wireless spectrum.
Fast and efficient RFID tag counting is an essential requirement for a lot of modern applications, particularly in high-density environments. This paper proposes an innovative algorithm designed to accelerate counting while maintaining compatibility with the EPCglobal Class 1 Generation 2 (C1G2) protocol. Relying on a single RN16 answer from each tag to validate its presence, the method speeds up counting by eliminating a full reader-tag communication, reducing processing time. The proposed method was validated using a software-defined radio (SDR) platform. Compared to the standardized protocol, our experiments demonstrated that up to four tags can be counted in the time required to identify a single tag, achieving approximately 77 % time savings across various data rates. This paper shows that the algorithm provides a feasible and scalable solution for RFID tag counting, incorporating collision information into its design to greatly improve counting speed, even in crowded environments.
We present how RAIN RFID power-on-tag-reverse (POTR) features can be used to reliably differentiate between materials having different relative dielectric permittivity (epsilon(r)) and effective loss (tan(delta)). Our approach shows how this is achieved using 15 diverse RAIN RFID tags, having an embedded T-match antenna design, deployed on 7 different material types. We present a data visualization and K-means clustering algorithm that can reliably differentiate between material types with 94% accuracy. We show how our approach is particularly useful at differentiating between materials having very similar epsilon(r) but different tan(delta). We also demonstrate the technique appears to be robust to reflections, wet inlay adhesives and material thickness via a limited study conducted in a non-idealized warehouse environment. Future research directions are also discussed.
Passive RFID tag localization has been mostly focused on monostatic and/or synchronized setups. This work moves a few steps further and performs real-time localization with distributed, i.e., multistatic radios, which are also unsynchronized at the carrier level. It succeeds by exploiting the elliptical direction of arrival (EllDoA) algorithm with a “playback” carrier frequency offset (CFO) mitigation method, showing localization feasibility at a small error cost, even with very cheap software defined radios. The inherent carrier phase offset (CPO) of such distributed setups is addressed, and it is shown that the calibration step needs to be performed only once, and not for further reruns of the experiment; thus, the proposed method is suitable for many real-world and real-time applications, which has not been shown before, to the best of our knowledge.
Battery-less RFID sensor tags in the Internet of Things (IoT) call for low-cost and power-efficiency multiparameter sensor design. Traditional multiparameter sensors rely on time-multiplexed parameter selection to avoid output coupling, which requires extra control logic and increases cost and design complexity. This paper presents a temperature and capacitance (T/C) sensor achieving self decoupling through the proposed self-switching double sampling (SDS) interface. With double sampling, a temperature-sensitive current alternately charges a reference capacitor and a sensing capacitor, simultaneously translating T/C information into a pulse-width-modulated (PWM) waveform. Measuring the low pulse width and the pulse width ratio can decouple temperature and capacitance information, respectively, eliminating the demand for parameter selection. Meanwhile, SDS reuses the PWM waveform as the double-sampling control signal without external control logic. The PWM signal can be sent back by analog PWM backscatter without the need for digitization. The SDS sensor applies a designed dual-slope relaxation oscillator (RxO) with area efficiency, ultralow power, and inherent self-switching topology as the T/C-to-PWM converter. A prototype sensor has been designed and simulated in 55-nm CMOS technology, occupying 0.037 mm(2) and consuming 59.6 nW at room temperature with a 0.8-V supply voltage. The T/C sensor achieves a temperature inaccuracy of -0.58/+0.38 degrees C in -20 similar to 120 degrees C and a capacitance inaccuracy of -24.9/+20 fF in 0 similar to 30 pF.
SenSync tackles key challenges in RFID-based differential sensing systems, including temporal misalignment, phase ambiguity, and environmental sensitivity. Traditional techniques are limited by sequential data processing, which introduces time shifts, and arbitrary phase jumps injected by commercial RFID readers, which obscure accurate differential measurements. These issues, compounded by multipath effects and dynamic environments, hinder the deployment of robust RFID sensing systems at scale. To address these challenges, we propose innovative algorithms and signal processing techniques to align and interpret time-shifted data from multiple ICs. Our approach mitigates the effects of temporal misalignment and phase ambiguity, ensuring reliable differential sensing in real-world applications. By improving data alignment and robustness, we accelerate the sensory resolution by 5 x. Furthermore, we developed a user interface capable of automatically detecting sensors within the system's field of operation and displaying their readings in real-time, demonstrating the practical applicability and versatility of our proposed solution.
Dense RFID environments pose critical challenges such as Reader-to-Reader Interference (RRI), Reader-to-Tag Collisions (RTC), and inefficient resource utilization, which degrade system performance and scalability. Traditional Media Access Control (MAC) protocols, including CSMA and TDMA, struggle to address these issues effectively, particularly in dynamic and large-scale deployments. This paper introduces MCSMARA (Markov Decision Process (MDP)-based Carrier Sense Multiple Access with Reader Arbitration), a novel MAC protocol designed to optimize reader coordination in dense RFID networks. By leveraging an MDP framework, MCSMARA models reader state transitions and employs a utility-based arbitration mechanism to dynamically allocate frequencies and time slots. The protocol incorporates adaptive backoff and decentralized neighborhood discovery for efficient resource management without centralized control. Simulation results demonstrate that MCSMARA reduces collisions by up to 30 %, improves throughput by 25 %, and ensures superior scalability, supporting a large amount of readers with minimal computational overhead. These findings establish MCSMARA as a transformative solution for RFID networks in logistics, retail, and industrial IoT, with potential for extension to mobile and heterogeneous environments.
We present the design, simulation, fabrication and measurement results of a biodegradable sensor for post operative monitoring. A backscattering measurement technique is demonstrated to detect frequency shifts resulting from the change in the thickness of a sensing film. Our sensor is composed of a modified split-ring resonator loaded with interdigitated capacitors. The sensor operates at around 3.2 GHz in free space and around 2 GHz in liquid solution. We demonstrated that our backscattering measurement data, quantified as resonance frequency in a laboratory environment, matches well with the simulation results.
We propose collaborative backscatter techniques for tag-to-tag communication between battery-less RF tags. The low incident backscatter power and the limited processing ability in the passive receive circuits limit the performance of such links. By recruiting 'helper' tags to boost backscatter signals, such links can be substantially strengthened, depending upon the network topology and channel conditions. Two techniques are developed and evaluated on a prototype tag network, demonstrating close-to-optimal performance with low computational overhead.
Deep neural networks (DNN) have become a widely used tool for improving efficiency in various problems; however, the time-consuming training process and the need for large amounts of labeled data often limit their practicality. This work presents a deep feed-forward neural network, designed for real-time tag localization, which addresses these challenges by lever-aging easily-generated simulation data for training. The network uses phase-based feature inputs, and performance is evaluated using simulation data, as well as real-world measurements from a custom multistatic RFID interrogation testbed. In the first part, state-of-the-art techniques are contrasted to neural networks in a simulated environment, and a pronounced advantage for the latter is demonstrated, for random tag positions. Then, a substantially improved setup is presented, which, by utilizing only one extra antenna, yields a remarkable 76% reduction in mean absolute error (MAE), in the order of 2.48 cm, while median absolute error is below 1 cm. This outcome is attributed to the increased distance between antenna pairs and not to phase ambiguity. In the second part, experimental data are used to evaluate the efficiency of DNNs in two real-world immobile scenarios, using a custom multistatic experimental setup with software-defined radios (SDR). Even though DNNs are trained exclusively on simulated data, they are found to perform in a comparable fashion to the conventional methods, with MAE ranging from 19 to 27 cm for the first and second scenarios, respectively; similarly, the median absolute error is found within 1 cm between the two scenarios, at approximately 8.5 cm. These results suggest that DNNs offer tremendous potential for further localization improvements, especially if more real-world measurements, possibly through future automated (e.g., robotic) processes, are blended into the training process.
This paper presents a self-matched passive harmonic generator circuit for backscatter IoT devices, utilizing a single varactor-loaded transmission line. The proposed design is implemented on an FR4 PCB with only one varactor, significantly simplifying the circuit, reducing its size, and lowering costs. The parallel configuration of the circuit enables precise control over the generated output power, while the self-matched nature of the design eliminates the need for additional passive matching networks, further enhancing its simplicity and efficiency. Results demonstrate a significant improvement in output power at the second harmonic, achieving a maximum of -8.5 dBm at 4 GHz with an input power of 0 dBm at 2 GHz, outperforming existing designs. The compact, energy-efficient, and low-cost nature of the proposed circuit makes it an ideal solution for batteryless IoT applications.
The EU Digital Product Passport (DPP) regulation mandates robust product traceability to support sustainability reporting and regulatory compliance. However, the inherent complexity of global supply chains, marked by frequent changes in product ownership, transformations, and aggregations, poses significant challenges. While GS1 EPCIS 2.0 offers a standardized framework for capturing and sharing product visibility data, querying traceability information becomes inefficient and cumbersome, particularly in scenarios involving multilevel transformations and aggregations. This paper proposes an integrated architecture that combines EPCIS 2.0 with a knowledge graph to address these challenges. By linking traceability events using GS1 keys, the system constructs a graph-based model that enables the retrieval of complete product histories with a single query, regardless of the complexity of the supply chain. The platform ingests visibility events in real time from distributed sources, enhancing scalability, interoperability, and traceability accuracy.
In this paper we study and fabricate surface acoustic wave devices that combine magnetic field and temperature measurements and that integrate a radio-frequency identification functionality (RFID). The use of a Transmissive-Reflective Delay Line (TR-DL) design enables to control the amplitude of the RFID signal peaks while maximizing the sensitivity of the sensor. The latter is based on a double layer of magnetoelastic CoFeB film used as sensitive layer to the magnetic field and deposited on top of a ZnO-covered LiNbO 3 Y-X substrate. This structure is used to generate a Love wave which enhances the sensitivity to the magnetic field. The achieved device demonstrates a high sensitivity to the magnetic field, a temperature compensation and reflected signal levels of -20 dB in a wired connection. The obtained sensitivity of 3630 ppm/mT (or 1.482 Hz/nT), is among the highest reported in the literature on magnetic SAW sensors.
In this paper, we show how wideband measurement of threshold sensitivity and backscatter curves from generic T-matched RAIN (passive UHF) RFID tags can be used for determining magnetic properties of the tagged items. The method is based on measuring all three tag resonances (two for sensitivity and one for backscatter), calculating from those the natural resonant frequency of the tag antenna loop portion and its frequency shift relative to free space, and then extracting effective magnetic permeability. The method is robust to the presence of non-magnetic materials. Possible applications include item sortation for reuse/recycling and smart package inspection.
The study introduces a novel application of Radio Frequency Identification (RFID) technology in smart antenna systems, specifically targeting the wireless control of a Yagi-Uda antenna's radiation pattern using Ultra High Frequency (UHF) RFID technology. By leveraging RFID, the system remotely adjusts the length of the passive resonator, enabling it to act as either a director or a reflector. This innovative approach addresses the limitations of wire-based control seen in traditional pattern-configurable antennas, ensuring stable antenna patterns while minimizing costs and energy consumption. At 2.4 GHz, the proposed Yagi antenna achieves directional operation with a gain of approximately 5.6 dBi and an impressive front-to-back ratio of 10.6 dB. Additionally, the system demonstrates high energy efficiency, consuming only 12 mu W of power.
Wireless communications are critical in the constantly changing environment of IoT and RFID technologies, where thousands of devices can be deployed across a wide range of scenarios. Whether connecting to cloud servers or local fog/edge devices, maintaining seamless communications is difficult, especially in demanding contexts like industrial warehouses or remote rural areas. Opportunistic networks, when combined with edge devices, provide a possible solution to this challenge. These networks enable IoT devices, particularly mobile devices, to redirect information as it passes via other devices until it reaches an edge node. Using different communication protocols, this paper investigates their effects on response times and total messages received for a opportunistic assisted RFID system. Specifically, this article compares two communications technologies (Bluetooth 5 and Wize) when used for building a novel Opportunistic Edge Computing (OEC) identification system based on low-cost Single-Board Computers (SBCs). For such a comparison, measurements have been performed for quantifying packet loss and latency. The tests consisted in two experiments under identical conditions and scenarios, with a node located roadside, transmitting identification information, and a node located inside a moving vehicle that was driven at varying vehicle speeds. The obtained results show for Bluetooth 5 average latencies ranging between 700 and 950 ms with packet losses between 7% and 27%, whereas for Wize the average delay as between
Logistics management has emerged as a key component to human activities conducted in space or remote habitations in general. The RFID Enabled Autonomous Logistics Management (REALM) system has played a key role in providing cargo tracking capabilities in the complex RF scattering environment in the International Space Station (ISS). Currently, the inferencing engines used by REALM to predict the location of RFID tagged items operate on an hour of data, whereas many movements of interest aboard the ISS occurs on the scale of seconds. In this work, we propose a new inferencing engine that produces an embedding space to represent the location of RFID marked cargo on the scale of 30 seconds to 2 minutes of data, allowing for the categorization of movement of cargo and predictions of a coarse location
There are numerous applications for low-cost materials sensing; however most RFID sensing solutions are limited to measurement of permittivity. To address this need, we present a novel materials sensing circuit for active RFID tags that enables the ability to measure relative changes in electrical permittivity and magnetic permeability as well as conductivity and AC loss. This design is based on a JFET Clapp oscillator topology that uses minimal parts, and where the resonator coil is also used as the sensor antenna. We show how the loss and reactive components of the material impedance can be independently derived from the amplitude and frequency of the oscillator and measured using an integrated frequency counter and diode detector. The completed sensing circuit, containing few parts and a cost of approximately US$1, was demonstrated using an integrated active RFID tag that is compliant with the IEEE 802.15.4 protocol. Our oscillator design has high stability and quick start-up, which enables very low duty cycle operation and achieves an average power consumption of less than 1 mW.
We present RL2, a robotic system for efficient and accurate localization of UHF RFID tags. In contrast to past robotic RFID localization systems, which have mostly focused on location accuracy, RL2 learns how to jointly optimize the accuracy and speed of localization. To do so, it introduces a reinforcement-learning-based (RL) trajectory optimization network that learns the next best trajectory for a robot-mounted reader antenna. Our algorithm encodes the aperture length and location confidence (using a synthetic-aperture-radar formulation) from multiple RFID tags into the state observations and uses them to learn the optimal trajectory. We built an end-to-end prototype of RL2 with an antenna moving on a ceiling-mounted 2D robotic track. We evaluated RL2 and demonstrated that with the median 3D localization accuracy of 0.55m, it locates multiple RFID tags 2.13x faster compared to a baseline strategy. Our results show the potential for RL-based RFID localization to enhance the efficiency of RFID inventory processes in areas spanning manufacturing, retail, and logistics.
Sensing the motion of physical objects in an environment enables numerous applications, from tracking occupancy in buildings and monitoring vital signs to diagnosing faults in machines. Typically, these application scenarios involve attaching a sensor, such as an accelerometer, to the object of interest, like a wearable device that tracks our steps. However, many of these scenarios require tracking motion in a noncontact manner where the sensor is not in touch with the object. A sensor in such a scenario observes variations in radio, light, acoustic, and infrared fields disturbed by the object's motion. Current noncontact sensing mechanisms often require substantial energy and involve complex processing on sophisticated hardware. We present TunnelSense, a novel mechanism that rethinks noncontact sensing using tunnel diode oscillators. They are highly sensitive to changes in their electromagnetic environments. The motion of an object near a tunnel diode oscillator induces corresponding changes in its resonant frequency and thus in the generated radio waves. Additionally, the low-power characteristics of the tunnel diode allow tags designed using them to operate on less than 100 mu W of power consumption and with a biasing voltage starting at 70mV. This enables prolonged tag operation on a small battery or energy harvested from the environment. Among numerous applications enabled by the TunnelSense system, this work demonstrates its ability to detect breathing at distances up to 30 cm between the subject and the TunnelSense tag.
