Radio Frequency Identification Data Research Articles

An improved sensing data cleaning scheme for object localization in edge computing environment

Abstract Radio frequency identification (RFID) is widely applied due to its fast identification speed and non-contact detection. However, the identification process of RFID tags is susceptible to interference from other tags and environmental factors, resulting in inaccurate identification data. To overcome these problem, this paper proposes an improved sensing data cleaning scheme for object localization in edge computing environment. In tag level data cleaning, we use adaptive sliding window and further consider dynamic tags and read rate in continuous reading cycle to adjust the window size timely and appropriately. In the reader level data cleaning, we estimate the tag number based on Chebyshev’s inequality through Markov chain for cyclic control and optimize different time slot lengths to improve the recognition rate. We build an edge computing environment and combine the proposed tag-level cleaning method and reader-level cleaning method to form a comprehensive RFID data cleaning process. Comparative experimental results show that the RFID data cleaning method proposed in this paper can effectively reduce redundant and missing data and improve the accuracy of tag recognition.

Performance of a UHF RFID Detection System to Assess Activity Levels and Lying Behaviour in Fattening Bulls

Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p < 0.001), ԑGroup2 = −120.63 min/d (p < 0.001), and ԑGroup1+2 = −34.43 min/d (p < 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate.

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

IoT Based Smart Trolley for Auto Billing

This paper proposes the development of a Smart Trolley system aimed at enhancing the shopping experience by automating the billing process. The system integrates Raspberry Pi, a low-cost, credit-card-sized computer, and RFID (Radio-Frequency Identification) technology to create an efficient and seamless checkout process for customers in retail environments. The Smart Trolley employs RFID tags attached to products and an RFID reader mounted on the trolley to identify items as they are placed inside. The Raspberry Pi processes the RFID data and maintains a running total of the items in the trolley. Additionally, it communicates with the store's database to retrieve product information and pricing. Through this integration, the Smart Trolley eliminates the need for manual scanning of each item at checkout, reducing waiting times and enhancing overall efficiency. Moreover, the system provides real-time updates on the total bill, allowing customers to track their expenses as they shop. This paper outlines the design, implementation, and testing of the Smart Trolley system, demonstrating its effectiveness in automating the billing process while improving the shopping experience for customers. The proposed system holds promise for revolutionizing retail operations, offering a glimpse into the future of smart, technology-driven shopping solutions. Key Words: Autobilling, Rasberry Pi, RFID(EM-18), LCD, Smart Shopping, IoT.

A framework for locating multiple RFID tags using RF hologram tensors

In this paper, we present a Deep Neural Network (DNN) based framework that employs Radio Frequency (RF) hologram tensors to locate multiple Ultra-High Frequency (UHF) passive Radio-Frequency Identification (RFID) tags. The RF hologram tensor exhibits a strong relationship between observation and spatial location, helping to improve the robustness to dynamic environments and equipment. Since RFID data is often marred by noise, we implement two types of deep neural network architectures to clean up the RF hologram tensor. Leveraging the spatial relationship between tags, the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping. In contrast to fingerprinting-based localization systems that use deep networks as classifiers, our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints. We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors. The proposed framework is implemented using commodity RFID devices, and its superior performance is validated through extensive experiments.

RFIDeep: Unfolding the potential of deep learning for radio‐frequency identification

Abstract Automatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio‐Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. We present a deep learning workflow, coined “RFIDeep”, to derive ecological features, such as breeding status and outcome, from RFID mark‐recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long‐term automatic monitoring of a long‐lived seabird that breeds in densely packed colonies, hence with many daily entries and exits. To determine individual breeding status and phenology and for each breeding season, we first developed a one‐dimensional convolution neural network (1D‐CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a “black box” step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%; phenological accuracy = 86.9%; and breeding success accuracy = 97.3%. RFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark‐recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analysing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology.

An Initial Study on the Use of Machine Learning and Radio Frequency Identification Data for Predicting Health Outcomes in Free-Range Laying Hens.

Maintaining the health and welfare of laying hens is key to achieving peak productivity and has become significant for assuring consumer confidence in the industry. Free-range egg production systems represent diverse environments, with a range of challenges that undermine flock performance not experienced in more conventional production systems. These challenges can include increased exposure to parasites and bacterial or viral infection, along with injuries and plumage damage resulting from increased freedom of movement and interaction with flock-mates. The ability to forecast the incidence of these health challenges across the production lifecycle for individual laying hens could result in an opportunity to make significant economic savings. By delivering the opportunity to reduce mortality rates and increase egg laying rates, the implementation of flock monitoring systems can be a viable solution. This study investigates the use of Radio Frequency Identification technologies (RFID) and machine learning to identify production system usage patterns and to forecast the health status for individual hens. Analysis of the underpinning data is presented that focuses on identifying correlations and structure that are significant for explaining the performance of predictive models that are trained on these challenging, highly unbalanced, datasets. A machine learning workflow was developed that incorporates data resampling to overcome the dataset imbalance and the identification/refinement of important data features. The results demonstrate promising performance, with an average 28% of Spotty Liver Disease, 33% round worm, and 33% of tape worm infections correctly predicted at the end of production. The analysis showed that monitoring hens during the early stages of egg production shows similar performance to models trained with data obtained at later periods of egg production. Future work could improve on these initial predictions by incorporating additional data streams to create a more complete view of flock health.

Spatial Perception of Tagged Cargo Using Fused RFID and CV Data in Intelligent Storage

Radio-frequency identification (RFID) and computer vision (CV) technology are widely employed in intelligent storage systems for sensing, locating, identifying, and monitoring storage cargo. However, both of them have applicability scenarios and limitations. In this article, we propose a system for spatial perception of storage cargo based on the fusion of RFID and CV data. Specifically, we employ a mobile robot carrying an RFID reader and an RGB camera to move between shelves. The reader is connected to two vertically deployed antennas that receive phase values from the tags on the cargo to perform phase unwrapping. Then, we construct a linear system of equations to solve synthetic aperture radar (SAR)-based 3-D localization to obtain the location of the target tag efficiently and accurately. For the images of the shelves captured by the RGB camera, we use image localization techniques based on color and texture features to obtain the pixel coordinates of the cargo in the image. Since the sampling range of the RFID reader antenna is larger than the shooting range of the RGB camera, we use the coherent point drift (CPD) algorithm with threshold judgment to match the localization results of the two subsystems, enabling us to display the inherent information of the target cargo on the plane rendering. Our proposed system is evaluated through various experiments, and the results show that our localization algorithm achieves high accuracy in 3-D space, and the matching algorithm has high robustness to the number of cargo and missing cargo or tags.

A Novel Algorithm for Redundant Data Filtering in WSN and RFID Integrated Networks

Wireless sensor networks (WSN) and radio frequency identification (RFID) are base technologies employed in decentralised dynamic environments. In the hybrid network formed by integrating RFID and WSN, RFID data can be used applying WSN protocols for multi-hop communications. However, RFID data contain excessive duplication, which increases time delay and energy consumption, resulting in wastage of different resources. There exist four popular RFID–WSN integration architectures: hierarchical RFID-sensor topology, network RFID-sensor topology, reader-sensor nodes topology, and mixed topology. In this paper, we propose a new plan for a WSN–RFID integrated network. The entire network is divided into clusters, and the clustering hierarchical routing algorithm is employed to send data from the head nodes to the base station. Further, we propose two algorithms to overcome redundant data on the hybrid network. Our simulation results demonstrate that the proposed method reduces redundant data and processing time compared with existing methods.

Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs

In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays.

A new compact grounded coplanar waveguide slotted multiband planar antenna for radio frequency identification data applications

<p><span>This research presents the development and conception of a new compact grounded coplanar waveguide fed slotted rectangular planar antenna with a multi-frequency band for <a name="_Hlk94076774"></a>radio frequency identification data (RFID) reader applications which is based on the antenna mono-band frequency to use for a various applications RFID to support a different operating range. The optimized of the final prototype designing operates a multiple frequency bands ranging from 0.7-1.1 GHz, 2.2-2.5 GHz and 5.4-6 GHz for 0.9/2.4 GHz and 5.8 GHz RFID operation bands which is adapted from ultra-high frequency band (0.9 GHz) to microwave frequency band (2.4-5.8 GHz) RFID systems. This antenna is implemented and printed on a FR4 substrate with a size of 30×50×1.6 mm<sup>3</sup>. The novel prototype includes of a radiator rectangular patch with a symmetrical slot and a U-slot with I-stub on ground plan. The principles parameters of the antenna have been studied optimized and miniaturized by using a two simulators CST Microwave Studio and advanced design system (ADS) to validate the simulation results before the planar antenna realization. The final structure is achieved and validated of the results measurement. Experimental results show that the proposed antenna with a small size has good and stable radiation and thus promising for a various RFID applications.</span></p>

Measuring bulk material flow—incorporating RFID and point cloud data processing

A methodology incorporating radio frequency identification (RFID) and point cloud data processing to measure the mass flow of bulk material on the conveyor belt in real-time was developed and demonstrated. Specifically, a stereo vision camera was used to collect the point cloud data of the bulk material flow surface in a line-scanning way. Then, applying RFID technology to divide the belt into multiple test segments, the measurement bias caused by the slippage of the speed sensor’s friction wheel was eliminated significantly. Meanwhile, a volume flow measurement model which sequentially obtains the bulk material volume on each test segment in a space volume subtraction way was proposed. Then, given the issue that the bulk density of material flow is not fixed in real application scenarios, a determination model to dynamically estimate the porosity of material flow by point cloud processing was developed. Using this methodology, the measurement errors were within 2.2%.

Optimization of the Intelligent Asset Management System Based on WSN and RFID Technology

With the development of Internet of Things (IoT) technology, especially the promotion of perception layer radio frequency identification (RFID) technology and wireless sensor network (WSN), a new way of thinking is provided for asset management. Applying both to the management technology of assets is an effective way to achieve intelligent asset management. This paper proposes an intelligent network applied to an asset management system based on WSN and RFID technologies. The whole intelligent asset management is divided into four layers according to the functional structure. From the top layer downward, the layers are the management and dispatch center, communication network, intelligent gateway, and data collection layer in order. Meanwhile, the functions and hardware composition of each layer are described. The data acquisition butcher is the sensing terminal of the whole intelligent asset management network. Smart nodes are its key devices to collect RFID data and sensing data to realize the intelligent sensing capability of the assets. The paper uses the MSP430 control chip, MFRC522 RF read/write chip, CC2425 wireless communication chip, temperature sensor, data memory, and voltage regulator chip to design the smart nodes in the system and details the working principle of the smart nodes and the networking process of the system. The newly designed intelligent asset management network can be deployed independently in asset management or can be integrated into the technical design of existing asset management systems. The management scheduling also collects and manages the RFID and sensing information of the area through the data collection layer, which improves the intelligent construction of the assets.

A NOVEL ALGORITHM FOR REDUCING ENERGY CONSUMPTION IN WSN AND RFID INTEGRATED NETWORKS

Wireless sensor networks (WSN) and radio frequency identification (RFID) are base technologies employed in decentralized dynamic environments. In the hybrid network that is induced by integrating RFID and WSN, RFID data can be used by applying WSN protocols for multi-hop communications. RFID data, however, includes a significant amount of duplicate information, which lengthens response time and consumes more energy, wasting a variety of resources. The hierarchical RFID-sensor topology, network RFID-sensor topology, reader-sensor nodes topology, and mixed topology are the four most commonly used RFID-WSN integration architectures. In this article, a novel plan for a WSN–RFID integrated network was proposed. The entire network is divided into clusters, and the Huffman algorithm is used to reduce energy consumption in sending information to the base station. Further, we propose two algorithms to overcome the redundancy of the data on the hybrid network. Our simulation findings demonstrate that the studied method decreases redundant data, energy consumption, and processing duration in comparison with existing methods.

Evaluating the accuracy and biological meaning of visits to RFID-enabled bird feeders using video.

Radio‐frequency identification (RFID) technology has gained popularity in ornithological studies as a way to collect large quantities of data to answer specific biological questions, but few published studies report methodologies used for validating the accuracy of RFID data. Further, connections between the RFID data and the behaviors of interest in a study are not always clearly established. These methodological deficiencies may seriously impact a study's results and subsequent interpretation. We built RFID‐equipped bird feeders and mounted them at three sites in Tompkins County, New York. We deployed passive integrated transponder tags on black‐capped chickadees, tufted titmice, and white‐breasted nuthatches and used a GoPro video camera to record the three tagged species at the feeders. We then reviewed the video to determine the accuracy of the RFID reader and understand the birds’ behavior at the feeders. We found that our RFID system recorded only 34.2% of all visits by tagged birds (n = 237) and that RFID detection increased with the length of a visit. We also found that our three tagged species and two other species that visited the feeders, American goldfinch and hairy woodpecker, retrieved food in 79.5% of their visits. Chickadees, titmice, nuthatches, and woodpeckers spent, on average, 2.3 s at feeders to collect one seed per visit. In contrast, goldfinches spent an average of 9.0 s at feeders and consumed up to 30 seeds per visit. Our results demonstrate the importance of confirming detection accuracy and that video can be used to identify behavioral characteristics associated with an RFID reader's detections. This simple—yet time‐intensive—method for assessing the accuracy and biological meaning of RFID data is useful for ornithological studies but can be used in research focusing on various taxa and study systems.

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

RFID-Pose: Vision-Aided Three-Dimensional Human Pose Estimation With Radio-Frequency Identification

In recent years, human pose tracking has become an important topic in computer vision (CV). To improve the privacy of human pose tracking, there is considerable interest in techniques without using a video camera. To this end, radio-frequency identification (RFID) tags, as a low-cost wearable sensor, provide an effective solution for 3-D human pose tracking. In this article, we propose RFID-Pose, a vision-aided realtime 3-D human pose estimation system, which is based on deep learning assisted by CV. The RFID phase data are calibrated to effectively mitigate the severe phase distortion, and high accuracy low rank tensor completion is employed to impute the missing RFID data. The system then estimates the spatial rotation angle of each human limb, and utilizes the rotation angles to reconstruct human pose in realtime with the forward kinematic technique. A prototype is developed with commodity RFID devices. High pose estimation accuracy and realtime operation of RFID-Pose are demonstrated in our experiments using Kinect 2.0 as a benchmark.

Bayesian Wavelet Regression for Spatial Estimation

Radio Frequency Identification (RFID) has taken center stage at retail and consumer products forum. RFID is not a new technology; it has been in use for many years. In this paper, we first review RFID technology and the components that form the backbone of the RFID system. Next, we demonstrate the usefulness of RFID in supply chain and present some data mining challenges in RFID. Finally a real-life case study is used to illustrate how organizations are using RFID data.

Anthropogenic noise alters parental behavior and nestling developmental patterns, but not fledging condition

Abstract Anthropogenic noise is a ubiquitous feature of the American landscape, and is a known stressor for many bird species, leading to negative effects in behavior, physiology, reproduction, and ultimately fitness. While a number of studies have examined how anthropogenic noise affects avian fitness, there are few that simultaneously examine how anthropogenic noise impacts the relationship between parental care behavior and nestling fitness. We conducted Brownian noise playbacks for 6 h a day during the nesting cycle on Eastern Bluebird (Sialia sialis) nest boxes to investigate if experimentally elevated noise affected parental care behavior, nestling body conditions, and nestling stress indices. We documented nest attendance by adult females using radio frequency identification (RFID), and we assessed nestling stress by measuring baseline corticosterone levels and telomere lengths. Based on the RFID data collected during individual brood cycles, adult bluebirds exposed to noise had significantly higher feeding rates earlier in the brood cycle than adults in the control group, but reduced feeding rates later in the cycle. Nestlings exposed to noise had higher body conditions than the control nestlings at 11 days of age, but conditions equalized between treatments by day 14. We found no differences in nestling baseline corticosterone levels or nestling telomere lengths between the two treatment groups. Our results revealed that noise altered adult behavior, which corresponded with altered nestling body condition. However, the absence of indicators of longer-term effects of noise on offspring suggests adult behavior may have been a short-term response.