RFID Devices Research Articles

Heart Rate Variability Estimation based on RFID Tag-Pair in Dynamic Environments

With the rapid development of smart healthcare, accurate Heart Rate Variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate the heartbeat in a contact-free way, which usually cannot separate multiple users or work in a dynamic environment. In this paper, we propose a lightweight heartbeat-sensing method based on RFID tag pairs, which focuses on HRV extraction in a more general sensing scenario. Based on the tag-pair design, we build a novel heartbeat and respiration model to describe the signal relationship between the two tags from the time and space domains. Based on the model, we propose a Calibrated Temporal-Spatial IQ-Shaping-based signal cancellation algorithm to cancel the respiration and extract the heartbeat. To remove the interference in dynamic measurement, we build an IQ-based signal model via a Principal Component Analysis-based interference estimation. To reduce the statistical error in HRV extraction, we further design a neural network to predict the HRV index. We have implemented a system prototype in a real environment with COTS RFID devices. Extensive experiments show that our system can achieve a median RMSSD error of 7.51ms, which satisfies the medical demand in HRV measurement.

Generic Semantic Trajectory Data Modelling Approach based on Ontologies

Advancements in tracking technologies like GPS, RFID and mobile devices have made trajectory data collection widespread. This surge in tracking device usage and location-based services popularity has greatly increased moving object trajectory data availability. The ontological modelling of this kind of data is of paramount importance in understanding and utilising such data effectively. By incorporating maximum semantic data into this model, a variety of essential elements related to mobile object trajectories can be captured. An ontology model rich in semantics not only accurately represents trajectory characteristics but also links them to other relevant elements such as spatial and temporal contexts, movement types and mobile object behaviours. This semantic richness grants the model great adaptability, allowing it to be reused in various contexts related to object mobility and making it generic. Moreover, by integrating this semantic data, the process of analysis and decision-making experiences significant improvement, as it relies on more comprehensive and well-structured information, thereby facilitating informed conclusions and effective strategy implementation. Our objective is to propose a generic ontological model for trajectory data that is rich in semantics and considers the various aspects of moving objects, their movements, their trajectories and their interactions with their environment, aiming to fill the gap identified in other models proposed in the literature.

An ECC based Secure Authentication Protocol for M2M Communication in Industrial IOT Edge Device

Abstract: Machine to machine communication in industrial IoT is becoming a common phenomenon now-a-days where all the machines like IoT edge devices communicate independently. It’s always been a challenging task to create a protocol for authentication that is both secure and efficient with regard to resources for use in industrial IoT environments' M2M communication. Here we present a safe and effective authentication method that makes use of elliptic curve cryptography (ECC). We demonstrated these vulnerabilities by testing the current protocol with various assaults, including SSL strip attack and MITM attack. To further evaluate the protocol's security against various IoT threats, we employed both official and informal approaches, such as the Scyther tool and BAN logic. Secured from a variety of IoT Threats, such as Man-in-the-middle (MITM) attacks, replay attacks, and impersonation attacks, our suggested protocol, absence of device anonymity, weak mutual authentication etc. but also suitable for resource constraint IoT edge devices like RFID devices, sensors, routers and gateways.

Biodegradable Magnesium Alloy for Microchip Implants

This research explores the development of biodegradable RFID microchips using magnesium and aluminium, aiming to address the environmental impact of non-biodegradable RFID devices. Electronic waste poses a significant challenge, and the study focuses on leveraging the unique properties of magnesium and aluminium for controlled degradation. Magnesium's high reactivity and aluminium's durability offer a strategic combination for crafting microchips that maintain functionality throughout their intended lifespan while gradually breaking down in specific environmental conditions. This approach aligns with global efforts to reduce electronic waste and promotes a sustainable alternative in RFID technology. The study's significance lies in its potential to revolutionize the electronic industry, providing an eco-friendly solution to mitigate the environmental consequences of non-biodegradable RFID microchips.

A Wireless Self-Service System for Library Using Commodity RFID Devices

A Wireless Self-Service System for Library Using Commodity RFID Devices

RF-Siamese: Approaching Accurate RFID Gesture Recognition With One Sample

Performing accurate sensing in diverse environments is a challenging issue in wireless sensing technologies. Existing solutions usually require collecting a large number of samples to train a classifier for every environment, or further assume similar sample distribution between different environments such that a model trained in one environment can be transferred to another. In this paper, we propose RF-Siamese, an RFID-based gesture sensing approach that achieves comparable accuracy to existing solutions but requires only a few samples in each eivironment. RF-Siamese leverages Siamese networks to distinguish different gestures with only a small number of samples and is enhanced by several novel designs to achieve high accuracy in diverse environments. First, the network structure and parameters (e.g., loss function and distance metric) are carefully designed to be suitable for RFID gesture recognition. Second, a permutation-based dataset generation strategy is proposed to make full use of the collected samples to enhance the recognition accuracy. Third, a template matching method is proposed to extend the Siamese network to classify multiple gestures. Extensive experiments on commercial RFID devices demonstrate that RF-Siamese achieves a high accuracy of 0.93 with only one sample of each gesture when recognizing 18 different gestures, while state-of-the-art approaches based on transfer learning and meta learning achieve an accuracy of only 0.59 and 0.70, respectively.

RF-SLAM: UHF-RFID Based Simultaneous Tags Mapping and Robot Localization Algorithm for Smart Warehouse Position Service

In this paper, we propose an RFID based simultaneous localization and mapping (RF-SLAM) method that allows us, for the first time, to estimate the robot's position and the tags' 3D position in the warehouse environment simultaneously without any reference tags and external sensors, using only COTS RFID device. RF-SLAM is designed to transform the RFID measurement into the relative tag position constraint and use a corresponding graph based model to solve the SLAM problem. Specifically, a multi-antenna based relative localization method using phase measurement and odometer data in a short time is proposed as the front end. The back end is a novel graph model based on the relative tags position constraint and odometer constraint. Experiments in different types of warehouses show the localization accuracy of robot and tags' 3D position is about 5cm and 10cm, respectively. The experimental results in a more challenging and actual environment are still competitive.

Secure UHF RFID Authentication With Smart Devices

Commodity ultra-high-frequency (UHF) RFID authentication systems only provide weak user authentication, as RFID tags can be easily stolen, lost, or cloned by attackers. This paper presents the design and evaluation of SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. SmartRFID explores extremely popular smart devices and requires a legitimate user to enroll his smart device along with his RFID tag. Besides authenticating the RFID tag as usual, SmartRFID verifies whether the user simultaneously possesses the associated smart device with both feature-based machine learning and deep learning techniques. The user is considered authentic if and only if passing the dual verifications. Comprehensive user experiments on commodity smartwatches and RFID devices confirmed the high security and usability of SmartRFID. In particular, SmartRFID achieves a true acceptance rate of above 97.5% and a false acceptance rate of less than 0.7% based on deep learning. In addition, SmartRFID can achieve an average authentication latency of less than 2.21 s, which is comparable to inputting a PIN on a door keypad or smartphone.

Chipless RFID Sensors for Bioimplants: A Comprehensive Review

RFID technology has boomed in the area of biomedical applications, particularly in wearable sensors and bioimplants. The placement of the tag externally or internally generates numerous challenges, such as the design procedure, fabrication method, and real-time testing, along with unique challenges specific to diagnosis and other on-body applications. This article presents a review of bioimplants for several on-body applications associated with RFID technologies and their impact on human tissue. Several fabrication methods are also discussed, with a focus on understanding flexible, conformal, and strong RFID devices. In light of the future of upcoming applications, this article also reviews transformative RFID-based solutions for wireless body area network and implantable uses. In addition, the review highlights the design and implant of prototype devices. In sum, the main focus of this article is all aspects of chipless RFID utilized in multiple places in the human body, beginning with limb sensing and tracking, which basically involves a prosthetic structural tag (PST) responsible for providing information about a prosthetic limb in space. Similarly, the ultrahigh-frequency (UHF) passive RFID implant gives information about the tag, which is placed in the limb, and the communication between the readers takes place without any physical contact. An alternative touch probe method that resonates at about 13.56 MHz improves the accuracy of the implant. Stent tags are discussed for cardiovascular systems. Other specific topics covered are body implants based on an organically modified ceramic technology (ORMOCER) substrate, myoelectric prostheses, fractal-based RFID implants for crack sensing, and the thermal effects of implants.

Split ring resonator inspired defected ground plane dual operating band antenna for wireless LAN and RFID applications

Defected ground structure (DGS) using an array of the three split ring resonators inspired antenna for the Wireless Local Area Network(WLAN) and RFID sensor is proposed in this manuscript. The complete physical measurement for the proposed antenna is 50 X 40 mm2 (at lower operating frequency). The defected ground plane structure along with the inclusion of the square split ring resonators (SRRs) in the ground plane helps in gain and bandwidth improvement. 1 × 3 split ring resonator array is used to make the existing ground plane to be defected. The cost effective FR – 4 materials is used as a substrate for the fabrication with the standard thickness of 1.6 mm. The resonance frequencies are at 2.4 and 5.8 GHz having the average gain 2.65dBi. The impedance bandwidths for both the frequencies are 5.0 % (2.31 to 2.49 GHz) and 3.9% (4.9 to 6.1 GHz). The dipole and omni directional radiation patterns, overall gain and efficiency of around 75% make the antenna most practically possible for the RFID and WLAN devices.

Efficient Authentication in RFID Devices Using Et Als Algorithm

Abstract: Security plays a vital role during the transmission of private data from one sender to the other. Although there are many security algorithms implemented but here we are providing the security algorithms on the RFID devices. The authentication techniques implemented in RFID is based on the new algorithm based on smart cards. The data send through the tags can be made secure using the proposed algorithm so that the unauthorized users can’t access the data without any further unique numbers

Tamera: Contactless Commodity Tracking, Material and Shopping Behavior Recognition Using COTS RFIDs

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera , a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m × 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.

Comparison of Security Performance of NTRU and ECC Algorithms For RFID Authentication

The rapid development of the Internet of Things creates information security vulnerabilities due to the unavoidable process of exchanging data. One device that is vulnerable to data security is RFID. One way to increase its security is to embed a cryptosystem in it. The NTRU algorithm can be a solution because of its low computational power. However, ECC is widely used because its computational power requirements are lower than other traditional public key algorithms. This research proposes the implementation and performance analysis of the ECC and NTRU algorithms on RFID devices. Testing is carried out by running 100-400 RFID devices simultaneously. The key generation and ECC decryption processes were faster than NTRU. The NTRU encryption process is faster than ECC. The ECC algorithm is more efficient and suitable for RFID devices. However, ECC is vulnerable to invalid curve attacks that can attack at the recommended security level. In NTRU, there is also a vulnerability to attacks using the LLL algorithm, but these attacks cannot yet attack the recommended security level. For this reason, the NTRU algorithm is more suitable for use on RFID devices, provided that the RFID device must increase its resources for computational needs heavier than ECC.

Secured Authentication of RFID Devices Using Lightweight Block Ciphers on FPGA Platforms

Secured Authentication of RFID Devices Using Lightweight Block Ciphers on FPGA Platforms

RAPP: A Radio Tomography Localization Method Characterized by Performance Parameterization in Rapid-Moving RFID System

Exploring and developing the perception system is an important means to promote the development of intelligent transportation. In this paper, we propose RAPP, a radio tomography-based method enabling performance parameterization to achieve parameter adjustment of localization accuracy without dense deployment of passive UHF RFID devices. The reader antenna takes a rapid-moving system as the carrier and generates virtual antenna elements to inventory received signal strength (RSS) of the pre-deployed tags. To eliminate the impact of the lack of tag backward signals during the mobile process and supplement the link distribution for each segment of the monitored platform, the Cubic Spline Interpolation algorithm (CSIA) is proposed to homogenize the spacing of the virtual antenna elements by preprocessing the measurements. Then the optimized link information is adopted to perform radio tomographic imaging to recognize the locations of targets. Theoretical analysis on ill-condition is derived to analyze how the number of virtual interpolation elements affects the localization accuracy. Further derivation about the relationship between the Bayesian Cramér-Rao Bound (CRB) and the link density provides a solution to influence the localization performance through parameter adjustment. Extensive simulation and experiment show that RAPP outperforms on multiple targets localization and behaves robustly in a multipath environment.

Nondestructive Diagnostic Measurement Methods for HF RFID Devices With AI Assistance

Nondestructive Diagnostic Measurement Methods for HF RFID Devices With AI Assistance

RF-Access: Barrier-Free Access Control Systems with UHF RFID

Traditional RFID-based access control systems use flap barriers to help manage pedestrian access and block unauthorized staff at any entrance, which requires visitors to swipe their cards individually and wait for the opening of the blocking body, resulting in low-frequency pedestrian access and even congestion in places with large passenger flow. This paper proposes a barrier-free access control system (RF-Access) with UHF RFID technology. The main advantage of RF-Access is that it provides non-intrusive access control by removing flap barriers and operations of swiping the card. The visitors just go across the system without any stay at the entrance. Meanwhile RF-Access performs the authentication, which greatly improves time efficiency and quality of service. RF-Access addresses two key issues of the non-intrusive access control: motion direction detection and illegal intrusion detection. In RF-Access, we first propose a dual-antenna system setup together with a time-slot-based model to monitor users’ moving directions, which is robust to different environmental factors, such as multi-path effects. Afterwards, we use a tag array to detect illegal intrusion in case attackers do not carry any RFID tags. We implement a prototype of RF-Access with commercial RFID devices. Extensive experiments show that our system can detect the moving direction with 99.83% accuracy and detect illegal intrusion with an accuracy of 96.67%.

A Metric for Assessing, Comparing, and Predicting the Performance of Autonomous RFID-Based Inventory Robots for Retail

RFID technology is being widely adopted by retailers due to its accuracy, versatility and reduction of operational costs. Most commonly, RFID in retail is used for taking frequent and accurate inventories of items in the stores. Usually, RFID inventories use handheld RFID devices, which makes the task tedious, costly, and prone to human errors. More reliable, fully automatic alternatives exist, such as smart shelves, overhead RFID antennas, and RFID equipped robots. Among them, robots seem to be the preferred choice by retailers with large stores. However, retailers need an objective way to compare the different options for inventory solutions and to calculate the return on investment (ROI) of each of them before they make and investment decision. In this article we present a metric for assessing, comparing, and predicting the performance of autonomous RFIDbased robots in retail stores. The metric models both the store and the robot, and predicts the performance of a given robot when inventorying a specific store. The metric also allows to compare the performance of different RFID robots in different stores. The metric has been developed using experimental data, and has been validated in a real store.

ExHIBit: Breath-based augmentative and alternative communication solution using commercial RFID devices

Patients with neuromuscular system disease may be systemic paralyzed and even lose the ability to speak. Patients can use breath to express intentions. But existing solutions either only allow patients to express several predefined sentences or cannot serve blind patients. We aim to develop a breath-based text input system named ExHIBit that allows paralyzed users, even paralyzed blind users, to freely express any sentences composed of letters and numbers. Based on the above motivation, we adopt binary sequences to express letters/numbers, and then specify that the inspiratory and expiratory actions represent one bit information respectively. Breath-holding action serves as an indication of symbol value change (0⇄1). We leverage commercial RFID devices to capture user’s respiratory waveform. A novel recursive algorithm called Dual-Channel Follow-Keep-Pump (DFKP) is proposed to extract channel signals of respiratory waveform. Then we adopt the first-order differential features of channel signals to detect and identify respiratory actions. ExHIBit adaptively adjusts parameters to cope with the non-stationary changes of respiration. Experiments demonstrate that ExHIBit can obtain 95.14% recognition accuracy and an input speed of 1.86 word/minute.

Automated Supply Chain Management in IIOT 4.0 (Car Manufacturing)

Fully Automated supply chain management in Industrial IOT increases the productivity of an industry manufacturing cars exponentially than the industries that operates and depends on the human labour and power. All the stages of supply chain management are automated starting from the materials required for manufacturing a car to selling of car to the consumer (i.e end to end). The automation in each stage is done with components such as RFID device (passive), beacon tags and various type of sensors depending on the requirement of the consumers. These devices are connected wirelessly and the data obtained from it is pushed into the cloud, where we can access the data and refresh it view the data frequently as per our requirement. By this we can know the availability of stocks, live tracking of materials arriving into the warehouse and to the distributors etc. The data which is pushed into the cloud is then viewed in application built for this purpose for easy access of users. The application also helps the user to know the current condition of their car and helps them maintain it in a very healthy condition.