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关键词： IoT   RFID   k-means   e-commerce   Marketing  

摘要： The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors.

摘要： This paper proposes a nature-inspired spider web-shaped ultra-high frequency (UHF) radio frequency identification (RFID) reader antenna and battery-free sensor-based system for healthcare applications. This antenna design consists of eight concentric decagons of various sizes and five straight microstrip *** lines are connected to the ground using 50 Omega resistors from both ends, except for one microstrip line that is reserved for connecting a feeding port. The reader antenna design features fairly strong and uniform electric and magnetic field characteristics. It also exhibits wideband characteristics, covering whole UHF RFID band (860-960 MHz) and providing a tag reading volume of 200 x 200 x20 mm(3). Additionally, it has low gain characteristics, which are necessary for the majority of nearfield applications to prevent the misreading of other tags. Moreover, the current distribution in this design is symmetric throughout the structure, effectively resolving orientation sensitivity issues commonly encountered in low-cost linearly polarized tag antennas. The measurement results show that the reader antenna can read medicine pills tagged using low-cost passive/battery-free RFID tags, tagged expensive jewelry, intervenes solution, and blood bags positioned in various orientations. As a result, the proposed reader antenna-based system is a strong contender for near-field RFID, healthcare, and IoT applications.

关键词： RFID network planning optimization   Chaotic particle swarm optimization (CPSO)   BIRCH clustering algorithm   Coverage   Interference  

摘要： The difficulty of deploying an RFID network optimally is brought on by the quick advancement of radio frequency identification (RFID) technology. It has been established that the RFID network planning (RNP) problem, which includes various constraints and objectives, is NP-hard. The research has given a lot of attention to the use of (EC) Evolutionary Computation and (SI) Swarm Intelligence for solving RNP, but the suggested methods experienced trouble regulating the number of readers deployed in the network. The complexity and cost of the network, however, are considerably influenced by the number of installed readers. In this study, we introduced a hybrid (BCPSO) algorithm that seeks to maximize tag coverage while minimizing interference, using as little power and as little load balancing as possible with a little number of readers. The suggested algorithm is a hybrid algorithm made up of a Chaotic Particle Swarm Optimization (CPSO) used to find the best positions for readers that incorporate the chaos approach into the PSO algorithm to increase randomness in the search for the PSO algorithm and addressed the issue of local minima, and a Balanced Iterative Reducing and Clustering using the Hierarchies (BIRCH) algorithm used to automatically count the number of readers and initialize the readers' coordinates. The proposed hybrid BCPSO algorithm improves the coverage of tags by 100% with the fewest readers while avoiding interference, and it also improves load balancing in three cases: case 30 by 99.94%, case 50 by 42.4%, and case 100 by 46.23%, according to comparison results between the proposed algorithm and the other algorithms in Jaballah and Meddeb (J Ambient Intell Human Comput 12:2905-2914, 2021. 10.1007/s12652-020-02446-5). and were compared the proposed hybrid BCPSO algorithm to the proposed algorithm in Cao et al. (Soft Comput 25: 5747-5761, 2021. 10.1007/s00500-020-05569-1), The comparison results demonstrate that the proposed hybrid BCPSO algorithm incre

关键词： Radio frequency identification (RFID)  

摘要： In this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A large data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing. © 2023, CC BY-NC-ND.

关键词： Indoor positioning   machine learning   pattern matching   phase difference  

摘要： An indoor positioning system, with ultra-high frequency radio frequency identification based on phase difference (PD), is presented in this paper. Several groups of phases are obtained by setting reader's different transmitting frequency to reduce the phase ambiguity. Two positioning algorithms are proposed with rough PDs, which are pattern matching based on PD correction and machine learning based on without PD correction. The experiments of two methods, in which machine learning gets a better positioning precision of 0.43 m, are also presented and discussed.

关键词： Particle swarm optimization (PSO)  

摘要： This study proposes a hybrid sparrow search algorithm, known as HSSA, since sparrow search algorithms have poor population diversity and local optimal solutions in the late stage of RFID network optimization. First of all, the proposed algorithm for population initialization enhances population diversity by using an elite opposition-based learning strategy. Additionally, the algorithm incorporates the variation and crossover strategies of the Mayfly Algorithm (MA), which allows the solution to leapfrog local optima and achieve high-quality results. Last but not least, the seagull optimization algorithm (SOA) is combined with the producer equation of the sparrow search algorithm (SSA) to change the producer's single search strategy. In the experiments, HSSA was compared with SSA, improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and SOA. In the first part of the experiment, the performance of the algorithms and the improved strategies is tested and analyzed using the CEC2017 test suite. In the second part of the experiment, four conflicting objectives are optimized in two experimental environments. Experimental results in different environments show the superiority of HSSA over other algorithms. © 2023, The Authors. All rights reserved.

关键词： Paper-based chipless RFID humidity sensor   HFSS simulation   Graphene oxide thin film   Sensitivity  

摘要： In this paper, a highly sensitive paper-based humidity sensor is designed and fabricated to overcome the typically low sensitivity and small quality factor of paper-based chipless radio-frequency identification (RFID) humidity sensors. A toroidal inductor-interdigitated electrodes (IDE) capacitor is used as the resonator structure of the new sensor, and the structural parameters of the sensor are optimized using the software of high frequency structure simulator (HFSS) to improve the quality factor. Three paper-based sensor samples were fabricated via screen printing using regular printing paper, Kodak photo paper, and double-sided copper-coated paper as the substrate. In addition, the resonance characteristics, sensitivity, recovery characteristics, stability and other sensor performances were tested. The results show that the sensitivity of the copper-coated-paper-based sensor is significantly better than the two other paper-based sensors. The simulation and test results of the moisture-sensitive characteristics are consistent for graphene oxide (GO) films, which were prepared using the spray method on the surface of the copper-coated-paper-based sensor with mass concentrations of 1 mg/mL, 2 mg/mL, and 5 mg/mL. This indicates that GO films can significantly improve sensor sensitivity when combined with paper-based moisture sensing. Furthermore, the sensitivity increases with increasing GO concentration, the 5 mg/mL GO sensor show a sensitivity of 6.25 MHz/%RH and a range of 60 % RH 90 % RH. The 1 mg/mL GO humidity sensor shows good recovery characteristics (92.4 % recovery). Based on the presence of hydrophilic functional groups in the GO material, a moisture sensing mechanism that GO could improve the sensitivity of the sensor is analyzed.

关键词： Radio frequency identification (RFID)  

摘要： Recent statistics have demonstrated that missing items have become the main cause of loss for retailers in inventory management. To quickly identify missing tags, traditional protocols adopt Aloha-based strategies which take a long time, especially when the number of tags is large. Among them, few works considered the effect of unexpected unknown tags on the missing tag identification process. With the presence of unknown tags, some missing tags may be falsely identified as present. Thus, the system's reliability is hardly guaranteed. In this work, we propose an efficient early-breaking estimation and tree-splitting-based missing tag identification (ETMTI) protocol for large-scale RFID systems. In ETMTI, a new early-breaking estimation and deactivation method is developed to effectively estimate the number of unknown tags and deactivate them within a short time. Next, a new tree-splitting-based missing tag identification method is proposed to quickly identify missing tags with a B-ary splitting tree. Besides, a bit-tracking response strategy is designed to further reduce the time cost. The optimal parameters, time cost, and false negative rate of ETMTI are analyzed theoretically. Simulation results are presented to demonstrate that the proposed ETMTI protocol takes a smaller time and has a lower false negative rate than the best-performing benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Image processing   normalized convolution neural network (CNN)   radio frequency identification (RFID) tags   reading distance prediction  

摘要： In the field of radio frequency identification (RFID) application, the locations of RFID multitags have a great influence on the reading distance of RFID system. In order to improve the reading distance of RFID multitags, a novel image processing and deep normalized convolutional neural network (DNCNN) for location measurement and reading distance prediction of RFID multitags are proposed. First, in view of the complex electromagnetic interference environment, image technology is involved in the experimental platform design. The designed experimental testing platform is utilized to measure the reading distance and collects the images of RFID multitags. Second, due to the existence of motion blur in the acquired images, the hybrid deep priors (HYDP) method is proposed to restore the blurred images. Then, the template matching method is utilized to measure the 3-D locations of RFID multitags quickly and accurately. Finally, DNCNN is proposed to deeply mine the relationship between the RFID tags' location distributions and corresponding reading distance. The experiments are conducted to verify the effectiveness and superiority of constructed DNCNN. The experimental results show that the relationship between the tags' location distributions and corresponding reading distance can be well built by DNCNN. The constructed DNCNN can effectively predict the reading distance of RFID multitags. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percent error (MAPE) of DNCNN prediction results are 0.0377 m, 0.0433 m, and 2.45%, respectively. The proposed DNCNN in this article can find the optimal location distribution of RFID tags corresponding the max predicted reading distance, which can effectively improve the reading performance of RFID system.