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RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water con...
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... Wireless communication between the tag and reader is influenced by factors such as the type of reader, the position of the tag, the direction of the tag, the material of the object to which the tag is attached, the angle of the antenna, and the speed of the object. [6] When other radio frequencies interfere with the frequency with which the tag and reader communicate, the reader cannot identify the tag successfully. For the reasons stated above it is very clear that the object attached to the tag or material contained in the surrounding area can result in changes in tag performance and reader. ...
... Lossy dielectric materials such as concrete walls, human body and water significantly affect the transmission coefficient (impedance matching) and the resulting read range. One way to solve this problem is by designing a well-matched antenna specific to the background Figure 1: Schematic illustration of a PV-RFID sensor dielectric, and an alternate approach is to use robust tag detection schemes as outlined in [32], [33]. ...
Communication range in passive Radio-Frequency Identification (RFID) front-end devices is a critical barrier in the real-world implementation of this low-cost technology. Purely passive RFID tags power up by harvesting the limited RF energy transmitted by the interrogator, and communicate by backscattering the incident signal. This mode of communication keeps manufacturing costs below a few cents per tag, but the limited power available at the tag undermines long-range deployment. In this paper, we present an approach to use Photovoltaics (PV) to augment the available energy at the tag to improve read range and sensing capabilities. We provide this extra-energy to the RFID integrated circuit (IC) using minimum additional electronics yet enabling persistent sensor-data acquisition. Current and emerging thin-film PV technologies have significant potential for being very low-cost, hence eliminating the barrier for implementation and making of PV-RFID wireless sensors. We reduce the long-range PV-RFID idea to practice by creating functional prototypes of i) a wireless building environment sensor to monitor temperature, and ii) an embedded tracker to find lost golf balls. The read range of PV-RFID is enhanced 8 times compared to conventional passive devices. In addition, the PV-RFID tags persistently transmit large volumes of sensor data (>0.14 million measurements per day) without using batteries. For communication range and energy persistence, we observe good agreement between calculated estimates and experimental results. We have also identified avenues for future research to develop low-cost PV-RFID devices for wireless sensing in the midst of the other competitive wireless technologies such as Bluetooth, Zigbee, Long Range (LoRa) backscatter etc.
... Lossy dielectric materials such as concrete walls, human body and water significantly affect the transmission coefficient (impedance matching) and the resulting read range. One way to solve this problem is by designing a well-matched antenna specific to the background Figure 1: Schematic illustration of a PV-RFID sensor dielectric, and an alternate approach is to use robust tag detection schemes as outlined in [32], [33]. ...
Communication range in passive Radio-Frequency Identification (RFID) front-end devices is a critical barrier in the real-world implementation of this low-cost technology. Purely passive RFID tags power up by harvesting the limited RF energy transmitted by the interrogator, and communicate by backscattering the incident signal. This mode of communication keeps manufacturing costs below a few cents per tag, but the limited power available at the tag undermines long-range deployment. In this paper, we present an approach to use Photovoltaics (PV) to augment the available energy at the tag to improve read range and sensing capabilities. We provide this extra-energy to the RFID integrated circuit (IC) using minimum additional electronics yet enabling persistent sensor-data acquisition. Current and emerging thin-film PV technologies have significant potential for being very low-cost, hence eliminating the barrier for implementation and making of PV-RFID wireless sensors. We reduce the long-range PV-RFID idea to practice by creating functional prototypes of i) a wireless building environment sensor to monitor temperature, and ii) an embedded tracker to find lost golf balls. The read range of PV-RFID is enhanced 8 times compared to conventional passive devices. In addition, the PV-RFID tags persistently transmit large volumes of sensor data (>0.14 million measurements per day) without using batteries. For communication range and energy persistence, we observe good agreement between calculated estimates and experimental results. We have also identified avenues for future research to develop low-cost PV-RFID devices for wireless sensing in the midst of the other competitive wireless technologies such as Bluetooth, Zigbee, Long Range (LoRa) backscatter etc.
... In starting we plug the wire in switch every node in block state for this node status take 50 second we have discussed already .suppose that switch1 is a starting node and send BPDU to on a node and take 20 second there is no response because is a computer not a switch, after go to listening state take 15 second, and go to next state that is learning state take 15 second .After completed all state then go to forward state otherwise switch1 forward the BPDU to switch2 and switch2 forward the BPDU to switch1 means loop in between switches so we apply spanning tree algorithms [6], [7]. ...
... Model-Based self-configuration methods for RFID readers such as the methods described in [15], [16] and [17] are limited to application cases, where the conditions during model-learning and operation of the RFID reader are similar. Hence, this approach does not work in application scenarios, where the required transmission power is rapidly changing and significantly influenced by modified tag types, tag positions or sources of interference. ...
Wireless communication systems such as bluetooth, WLAN and RFID gain more and more importance in industrial production systems. However, high requirements with respect to availability and determinism have to be met in industrial environments with several sources of interference such as frequency converters and welding machines. Finding the optimal parameter configuration, which allows for low power consumption and reliable operation, is in many cases time-consuming and error-prone. In this paper, a self-optimizing RFID system is presented, which adapts both the transmission frequency and the transmission power of the RFID reader to the system environment. The proposed method allows for a robust communication with low energy consumption. Evaluation has been conducted for an application scenario in the SmartFactory OWL. In the investigated settings, transmission power of the RFID reader could be reduced from 27dBm to 15.2dBm on average.
... Radio Frequency Identification (RFID) is based on radio communication for tagging and identifying an object [11]. It consists of two blocks namely, RFID transceivers (readers) and RFID transponders (tags). ...
In this paper, we propose a new structure patch antenna excited by two identical microstrip lines having a common feeding adapted to 50 ohm and intended to applications of Radio Frequency Identification (RFID). The slots inserted at the patch antenna have a direct impact on improving the radiation characteristics in terms of reflection coefficient, voltage standing wave ratio, input impedance and directivity over a range of frequencies ranging from 2 GHz up to 2.9 GHz. The analysis and optimization have been performed using the simulator HFSS (High Frequency Structure Simulator) based on the finite element method. Next, in order to validate our simulation, we use another electromagnetic simulator CST MWS (Computer Simulation Technology-MicroWaves Studio) which is based on the finite integration method. The simulation results of the two simulators agree well practically.
... The factors that influence detectability of the mobile RFID tag with fixed RFID reader includes [25], [26]: ...
RFID is a low power wireless emerging technology which has given rise to highly promising applications in real life. It can be employed for robot navigation. In multi-robot environment, when many robots are moving in the same work space, there is a possibility of their physical collision with themselves as well as with physical objects. In the present work, we have proposed and developed a processor incorporating smart algorithm for avoiding such collisions with the help of RFID technology and implemented it by using VHDL. The design procedure and the simulated results are very useful in designing and implementing a practical RFID system. The RTL schematic view of the processor is achieved by successfully synthesizing the proposed design.KEYWORDS
... This analysis is usually performed by very time consuming trialand-error procedures, also called " experimental approach " . Currently, there is no standard established for specifying tag and reader positions, and very little research and virtually no systematic analysis on this issue has been reported so far [7] [8] [9]. Nevertheless, this is a crucial issue for the RFID systems before they can be widely used in practice. ...
RFID Tag detection/recognition is one of the most critical issues for successful deployment of RFID systems in diverse applications. The main factors influencing tag detection by RFID reader antenna include tag position, relative position of reader, read field length, etc. In this paper, we analyze the characteristics of tag detection for a carton box object on a wooden pallet by an experimental approach based on tag signal strength, and we propose a method for predicting detection related directly to the strength of tag signal using an intelligent machine learning technique called support vector machine (SVM). The use of the proposed method is able to save time and cost by quick prediction of tag detection. Extensive experiments showed that the proposed approach can predict tag recognition for a carton box object with an accuracy at 95% for various reader heights and read field lengths. The proposed approach is effective for determining the best tag detection influencing factor conditioned on the target object with the help of detectability prediction.
Nowadays, audiovisual content is distributed rapidly but also extensively to remote regions via the web in a number of formats, comprising images, audio, video, and textual. Everything is easily accessible and simple for all users thanks to digitized transmission via the World Wide Web. As a consequence, data protection is indeed a required and essential activity. Networking or data security has three primary goals: confidentiality, integrity, and availability. Confidentiality refers to content that is secure yet not accessed by unauthorized individuals. The term “integrity” refers to an information’s veracity, while “availability” refers to the ease in which authorized users can access essential data. Information security is insufficient on its own to assure the constant operation of data such as text, audio, video, and electronic images.
Although there are several ways to image security available, including encryption, watermarking, digital watermarking, reversible watermarking, cryptography, and steganography. The goal of this book is to transfer secure textual data storage on public networks and IoT devices by concealing secret data in multimedia. It also covers discussions on textual image recognition using machine learning/deep learning-based methods. This book also offers advanced steganography ways for embedding textual data on the cover image, as well as a new way for secure transmission of biological imaging, imaging with machine learning and deep learning, and 2D, 3D imaging in the field of telemedicine.
