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Abstract In present times, Radio‐Frequency Identification (RFID) systems have seen a significant rise in their usage. There has been an increasing interest in developing even lighter RFID protocols suitable for resource‐constrained environments. Ensuring security and privacy remain critical challenges in RFID‐based systems. Recently proposed lightweight authentication schemes, namely LRSAS+ and LRARP+, are ideally suited for constrained devices. However, this article investigates these schemes and reveals certain vulnerabilities: LRSAS+ is susceptible to tag impersonation, desynchronization, and traceability attacks, while LRARP+ can fall prey to traceability and secret disclosure attacks. An enhanced version of these authentication systems is proposed that tackles their inherent weaknesses by leveraging the χper function. To verify the security of the proposed scheme, a formal analysis is conducted using Gong–Needham–Yahalom logic (GNY logic) and an automated security protocol verification tool, ProVerif. The improved scheme's effectiveness is also compared with multiple contemporary lightweight systems. The results indicate that the enhanced scheme not only meets the security requirements for lightweight authentication schemes but also achieves this with minimal computational overhead.

Abnormal crowd detection and estimation are critical in video surveillance for ensuring public safety and preventing stampedes. Owing to occlusions and blind spots, traditional video surveillance methods cannot detect, estimate, or locate people in dense moving crowds with acceptable accuracy, posing a major challenge. Therefore, this study aims to provide an in-depth analysis of the most recent advancements in recognizing abnormal behaviors in large crowds. We present a comprehensive literature review on crowd anomaly detection using disruptive technologies such as radio frequency identification, wireless sensor networks, Wi-Fi, and Bluetooth low energy, employing device-free noninvasive algorithms based on received signal strength indicator variations to detect the speed and direction of a moving crowd to predict the onset of a stampede. Furthermore, this study presents the most recent findings on mobile crowdsensing based on edge computing, urban dynamics, optical flow, and machine learning techniques. Finally, we critically analyze the major challenges, shedding light on opportunities and directions for future work.

A miniaturized ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna was designed.The antenna was composed of two square spiral resonators shorted inside and outside.By adjusting the edge length of the outer ring spiral resonator and the spacing between the inner and outer ring spiral resonators, the resonant frequency of the proposed tag antenna could be effectively tuned.The simulation results show that the proposed tag antenna has good impedance matching and the frequency band range covers the whole UHF band (840～960 MHz).It has a dimension of 7.60 mm×7.60 mm×0.05 mm, which is 96% smaller than the traditional spiral resonator (SR) antenna.The measure result shows that the proposed tag antenna achieves the maximum reading distance at 910 MHz in 4 W effective isotropic radiated power.The proposed UHF RFID tag antenna has some advantages of small size, wide frequency band, low cost and easy batch fabrication, and is suitable for applications with small size requirements.

The wireless use of radio frequency waves for data acquisition and transfer is known as Radio Frequency Identification (RFID). RFID-based systems have been applied in various fields, including building construction and maintenance. This study aims to evaluate the RFID tools for realizing the sustainability of construction projects. The literature was reviewed to obtain secondary data, complemented by a quantitative method involving the administration of a questionnaire to 107 experts in Nigeria using a random sampling method. It was followed by data analysis using the Exploratory Factor Analysis (EFA) approach. Finally, the structural equation modeling-artificial neural network model was applied to prioritize the major constructs. The EFA results demonstrated that RFID deployment areas may be divided into two main categories: hardware and system. The results affirmed the effectiveness of the system tools for RFID implementation in the building industry. Additionally, the hybrid model revealed that system and hardware predictors rank first and second in the RFID implementation areas. The outcomes of this study are important to understanding tools and methodologies related to the fuzziness of RFID for prospective workforces. Furthermore, it is envisaged that the identified RFID tools would enhance the sustainability of building projects. This study lays the foundation for the enhancement of decision-making in building projects. Although these studies have been confined to Nigeria, the findings apply to other developing countries, especially those with similar construction processes and operations.

ABSTRACT: This study aimed to estimate genetic parameters for feeding behavior (FB) traits and to assess their genetic relationship with performance traits in group-housed broilers. In total, 99,472,151 visits were recorded for 95,711 birds between 2017 and 2022 using electronic feeders. The visits were first clustered into 2,667,617 daily observations for ten FB traits: daily feed intake (DFI), daily number of visits (NVIS), time spent at the feeders (TSF), number of visited feeders (NVF), visiting activity interval (VAI), feeding rate (FR), daily number of meals (NMEAL), average intake per meal (INTMEAL), number of visits per meal (VISMEAL) and interval between meals (MEALIVL). All FB traits were then considered as the average per bird across the feeding test period. Three growth traits (body weight at the start – SBW and at the end of the feeding test – FBW, and weight gain over the test period – BWG), and 2 feed efficiency (FE) traits (Feed Conversion Rate - FCR and Residual Feed Intake - RFI) were also recorded. The (co)variance components were estimated using multitrait animal mixed models. For growth and FE, the heritability (h2) estimates were moderate, ranging from 0.20 ± 0.01 (BWG) to 0.32 ± 0.02 (RFI). Overall, the h2 estimates for FB traits were higher than for productive traits, ranging from 0.31 ± 0.01 (DFI) to 0.56 ± 0.02 (TSF). DFI presented high genetic correlations (0.53–0.86) with all performance traits. Conversely, the remaining FB traits presented null to moderate genetic correlations with these traits, ranging from –0.38 to 0.42 for growth traits and between –0.14 and 0.25 for FE traits. Genetic selection for favorable feeding behavior is expected to exhibit a fast genetic response. The results suggest that it is possible to consider different feeding strategies without compromising the genetic progress of FE. Conversely, breeding strategies prioritizing a higher bird activity might result in lighter broiler lines in the long term, given the negative genetic correlations between visit-related traits (NV, NVF, and NMEAL) and growth traits (SBW and FBW).

Abstract With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.

Owing to the rapid development of the ambient Internet of Things (IoT) industry, Backscatter Communication Systems (BCS) require higher receiver sensitivity for large-scale IoT scenarios than traditional Radio Frequency Identification (RFID) systems. This paper proposes a method of using Multiple-Input Multiple-Output (MIMO) technology to compensate for reflected signals in the 920 MHz backscatter communication systems. The researcher first created a MATLAB model to verify the theoretical feasibility and then built a One-Transmitter Two-Receiver (1T2R) BCS transceiver system compatible with the EPC global protocol for practical verification on LabVIEW. The ITIR and 1T2R tests were conducted using the National Instrument (NI) USRP-2952R device, the directional antennas, and the standard RF electronic tags. Through the process of superimposing and utilizing the phase shift between the two reflected carriers, it has been confirmed that the overall signal strength can be enhanced from 2.2 mV to 4.13 mV and 4.37 mV (nearly reaching 4.70 mV by using two Rx antennas in ideal conditions). After calculation, this advancement increases signal strength from −40.14 dBm (when using a single Rx antenna) to −34.18 dBm (when using two Rx antennas). The improvement in signal strength can provide a gain of 5.96 dB to the backscatter communication systems. Experimental results demonstrate that utilizing MIMO technology to the backscatter communication systems can effectively enhance the sensitivity of the receiver at 920 MHz.

Vehicular Ad-hoc Networks (VANETs) indeed have significant potential to enhance transportation efficiency, accident prevention, and overall comfort. They enable communication between vehicles and road infrastructure, facilitating the exchange of entertainment and traffic information. This capability can lead to safer and more efficient transportation systems while improving the overall experience for drivers and passengers. The challenging and critical security requirements in VANETs, such as access management, authentication, and privacy protection, are essential for achieving the objectives of vehicle-to-infrastructure (V2I) and intervehicle (I2V) communications. These security measures ensure the integrity, confidentiality, and availability of communication channels, thereby enhancing transportation efficiency, accident prevention, and overall comfort within VANETs. In this study, we investigate the concerns of secure access control, authentication, and privacy protection in VANETs. In response to these challenges, we propose automobile authentication methods. This protocol is built on a toll-based certificate management system utilizing RFID tags and readers deployed on automobiles and toll booths. Additionally, we introduce a vehicular mobility management method and a VANET node attachment strategy that considers quality of service (QoS) requirements. This comprehensive approach addresses key security and operational aspects of VANETs.

A key characteristic of free-range chicken farming is to enable chickens to spend time outdoors. However, each chicken may use the available areas for roaming in variable ways. To check if, and how, broilers use their outdoor range at an individual level, we need to reliably characterise range use behaviour. Traditional methods relying on visual scans require significant time investment and only provide discontinuous information. Passive RFID (Radio Frequency Identification) systems enable tracking individually tagged chickens’ when they go through pop-holes; hence, they only provide partial information on the movements of individual chickens. Here, we describe a new method to measure chickens’ range use and test its reliability on three ranges each containing a different breed. We used an active RFID system to localise chickens in their barn, or in one of nine zones of their range, every 30 seconds and assessed range-use behaviour in 600 chickens belonging to three breeds of slow- or medium-growing broilers used for outdoor production (all

The positioning method for mine track locomotives is mainly based on ultra wide band (UWB) positioning. But the complex environment of underground transportation roadways and frequent not line of sight（NLOS） propagation seriously impact the precision of UWB positioning. At present, research on positioning errors caused by NLOS has problems such as complex algorithms and poor real-time positioning. Based on the analysis of the positioning characteristics of railway locomotives and the UWB positioning technology based on the time of arrival (TOA), a positioning method of mine track locomotive based on NLOS error suppression is proposed. Two positioning cards are installed at different positions on the track locomotive. Radio frequency identification technology is used to accurately divide the relative position relationship between the positioning card and the positioning base station. The distance measurement value of the UWB positioning signal is greater than the actual value under NLOS propagation conditions. Based on this situation, the empirical range of the difference between the distance measurement value and the actual value between the two positioning cards is used to calculate the discrimination threshold for NLOS propagation conditions under different position nelationship between positioning cards and positioning base station. By using this discrimination threshold, the ranging value of the positioning signal propagated by NLOS path is eliminated. The ranging value of the positioning signal propagated by the line of sight path is used for positioning calculation, so that to suppress NLOS errors and improve the average positioning precision of mine track locomotives. The test results show that using the positioning method of mine track locomotives based on NLOS error suppression, the average positioning error of locomotives is within 1 m when the positioning signal is in line of sight propagation conditions. When the positioning signal is in NLOS propagation conditions, most of the NLOS errors are effectively suppressed. The average positioning precision is about 1 meter. The positioning precision of track locomotives has been greatly improved compared with normal UWB positioning method based on TOA.

In order to cope with the problems and new challenges arising from the application of Internet of Things (IoT) technology in urban management and to put forward corresponding administrative regulations, this paper explains the concept of IoT technology. And put forward the Internet of Things, radio frequency identification, wireless sensor networks, and other key technologies as the main level of the system architecture. Also, the perception layer, network layer, and application layer are the main levels of the system architecture. At the same time, it explains the specific characteristics of IOT technology and its specific applications in urban planning, transportation, medical care, family, and agricultural management. Zhoushan City, Zhejiang Province, China is chosen as the research object to analyze the application of IoT technology in urban management. The overall index score of Zhoushan City’s IoT city management construction is mainly concentrated between 60% and 70%, in which the scoring rate of the secondary indexes under the first-level index of the city’s intelligent governance regulations is relatively low, all below the 62% level, with an average value of less than 4. In the problem of information silos and shortage of talent, the mainstream opinions of Zhoushan City’s residents are “IoT applications are numerous and complex” and “lack of IoT applications”, respectively. In the problem of information silo and talent shortage, the mainstream views of Zhoushan residents are “the variety and complexity of IoT applications” and “the lack of protection mechanism for talents”, accounting for 68.64% and 58.12%, respectively, and 68.87% of the residents have not used IoT applications.

Seaports have a very important role in connecting freight by sea. Goods transported through seaports in the world are increasing day by day to meet human needs. This increases the pressure to apply more technologies for better port management. The world's seaports in the 4th generation, the generation in which seaports enhance connectivity to form a large network, have shown high efficiency when applying technology to port management. This optimizes port operations and connects port information into a network that improves productivity and reduces loading and unloading times. Today, the Internet of Things is the foundation for technologies to manage and optimize operations in various fields. It is considered by scientists to be a highly influential technology in the “4.0” era. The Internet of Things (IoT) technology directly affects the activities and processes of loading and unloading goods at seaports. Modern IoT-based port management technologies such as Radio Frequency Identification (RFID) and Dedicated Short Range Communications (DSRC) are contributing to the increased speed and safe movement of goods through seaports. The application of IoT in port management has become an inevitable trend and will be presented in this article. In the next generation, seaports tend to develop into smart ports based on rapidly developing technology platforms such as IoT, blockchain, and cloud computing. Smart port development also poses many issues to be resolved, including environmental issues. In this paper, the authors present some solutions to develop smart ports in an environmentally friendly manner.

This paper presents a comprehensive overview and analysis of the state-of-the-art (SoA) in semi-passive or Battery-Assisted (BAP) Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) sensor tags compliant with EPC Global G2/ISO-18000C. These tags operate on the same communication principle as fully passive sensor tags but incorporate a battery or an energy harvesting module. This additional power source extends communication ranges and enables power demanding applications using low-power microcontrollers (MCUs) and higher-end sensors. This article also analyzes various key features, including tag integrated circuit (IC) architecture, types of energy harvesting modules, and communication range. The main conclusions are threefold. Firstly, selecting the appropriate tag IC requires a careful analysis of its features such as sensitivity, sensor interfaces, or data logging capabilities. For instance, among the solutions examined in the SoA, half of them opted for a tag IC capable of MCU communication via SPI or I2C buses. Secondly, it is essential to assess both the forward and backward communication links to leverage the sensitivity of the tag IC in BAP mode. Interestingly, only one-third of the SoA solutions achieved the theoretical communication range anticipated by the sensitivity of the tag IC. Finally, an energy budget analysis is required to ensure that the energy generation suffices to meet the energy requirements of the tag. While most solutions rely on batteries as the energy source and analyze battery lifespan, only a few studies employing energy harvesters conduct an energy budget analysis due to the additional complexity involved.

The Internet of Things (IoT) is a rapidly evolving field that provides seamless connections to a physical object, making it part of a smart environment. To fully realize the potential power of the connections between these objects in IoT, trust between them is critical. Conventional security measures are not sufficient to provide comprehensive security for this smart world. Trust is used to reduce the risk of insecurity when nodes are connected to the Internet. In an IoT environment, various trust models have been proposed for Wireless Sensor Networks (WSNs) and Radio Frequency Identification (RFID). Nevertheless, these are not fully adapted to the dynamic and uncertain environment of the intelligent IoT. Therefore, this article reviews the characteristics of recent works in IoT trust models. A comprehensive study has been conducted on the classification of trust models. A set of factors are discussed, such as trust characteristics, trust architecture, trust distribution, trust aggregation technique, trust model type, and attack type. In addition, this paper provides readers with an understanding of the current existing trust model and directs future works to propose new models that satisfy all characteristics that should be considered when developing a trust model. Finally, some research challenges and directions are identified.

Objectives: RFID is an acronym for “radio-frequency identification” and refers to a technology whereby digital data encoded in RFID tags or smart labels are captured by a reader via radio waves. Radio Frequency Identification(RFID) is one of the important technical tools for the detection of information resources in the library. This system provides fast and convenient access to resources, prevents theft, accelerates library services, and increases the efficiency of library activities. The aim of this research is to study the feasibility of using RFID in the libraries of Kerman.Methods: The research is survey-descriptive. Its statistical population consists of all managers and employees of 8 public libraries in Kerman. The tool of data collection is a researcher-made questionnaire, which was used to determine its validity, using the face validity method, and the mathematical expectation was 0.73, which is an acceptable value to confirm the validity of the questionnaire. In order to calculate its reliability, Cronbach's alpha was used which is 0.814, indicating the acceptable reliability of the questionnaire. Data analysis was done with inferential statistics tests, a prioritization index based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and SPSS version 20 software. TOPSIS is a multi-criteria decision analysis method that compares a set of alternatives based on a predetermined criterion.Results: The findings indicate that the ability to implement RFID in terms of infrastructure and technological tools in the public libraries of Kerman city is moderate. In terms of existing software, these libraries have the ability to implement RFID. Also, the human resources working in the public libraries of Kerman are moderately prepared for the implementation of RFID. In addition, at present, the technological infrastructure for the implementation of RFID does not seem to be sufficient. It is worthwhile that the selection of the software should be done carefully and studied sufficiently. It is necessary to use RFID in the public libraries of Kerman.Conclusions: The results show that the limitation in the implementation of RFID in the public libraries of Kerman city is mostly related to the expert manpower, budget, and technical issues. According to managers and librarians, it is necessary to carry out this process and take advantage of its facilities. The implementation of educational programs, mid-term and long-term planning, management interaction with library experts for better implementation, and using the capabilities of RFID in providing library services, such as lending, organizing, retrieving, and managing the collection, are among the suggestions of this research.

Even after two years since the declaration of the new virus Coronavirus Disease 19 (COVID-19), the reported cases are still considerably high in many countries, including Malaysia. The health authorities cannot monitor the health condition and track the location of every home-monitored patient at once due to many confirmed cases in a day. In order to overcome the shortage of manpower, an Internet of Things (IoT)-based self-quarantine system with Radio Frequency Identification (RFID) and Global Positioning System (GPS) tracking is proposed in this paper to monitor the health conditions of the Covid-19 patients and track their real-time location via mobile application. Biomedical sensors are used to measure health conditions such as temperature, pulse oximetry, and heart-rate monitor. In addition, the RFID readers are used to detect patients that intend to leave the quarantine area, and the GPS modules are used to track their actual geometrical location so that the authorities can take further action. The real-time data is automatically pushed to the cloud server for the authorities to remotely view the patient's health condition and location on the Google map using smart devices. Finally, a hardware prototype and a mobile application have been successfully developed in this project. The system is able to display the temperature, heartbeats, and blood oxygen saturation properly on a liquid crystal display (LCD) screen. All these measured values, together with the information from RFID detection and GPS location tracking, can be viewed on a smartphone.

Abstract Body area network has attracted extensive attention for its applications in athletics, medical, diagnosis, and rehabilitation training in the next generation personalized health care solutions. Here, a contact‐separation direct current triboelectric nanogenerators (CSDC‐TENGs) based self‐powered wireless body area network (SWBAN) is reported that enables multi‐joint movements monitoring for human motion. The CSDC‐TENG is designed as a flexible active sensor with an internal contact switch, and the flexible substrate makes the TENG‐sensor stick onto skin easily. Due to the internal switch, the CSDC‐TENG could generate a DC current, a large instantaneous output voltage exceeds 700 V, and an instantaneous power can reach 1.076 W, which is more than 23 000 times higher than that of the traditional contact‐separation mode TENG in same size and materials without the switch. By coupling with flexible coil, the fixed high‐frequency radio signals can be modulated and emitted clearly ranging from 6 to 16 MHz, which can be wirelessly received and demodulated through a reader. Moreover, the SWBAN is demonstrated in a real time monitoring system for joints motion. This work has realized the wearable TENG for self‐powered wireless real‐time monitoring of body movements driven by low‐frequency human daily activities, which may promote a tremendous development of intelligent healthcare, wireless sensing system and body area network.

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

This paper proposes the Lock-Position-Based RFID Adaptive Parallel Collision Tree (LAPCT) algorithm to address the issues of excessive time slots required in the identification process of collision tree algorithms for multiple tags and the high communication complexity between the reader and multiple tags. The LAPCT algorithm adopts a single-query multiple-response mechanism and dynamically divides the response sub-cycle numbers in the identification cycle based on an adaptive strategy. It uses Manchester encoding to lock collision positions and generate a common query prefix, effectively reducing the number of reader queries. This reduction in queries decreases the total number of required time slots and transmitted bits during the reader–tag communication process, thereby improving the efficiency of multiple tag recognition. Theoretical and simulation experiments demonstrate that compared to similar algorithms, the LAPCT algorithm achieves a maximum reduction of 37% in total time slots required, a maximum improvement of 30% in recognition efficiency, and a maximum reduction of 90% in communication complexity. Furthermore, with an increase in the number of tags, the performance advantages of the LAPCT algorithm become more pronounced, making it suitable for large-scale tag scenarios.

Aiming at the problem that long time-consuming and poor positioning accuracy using geometric method in the traditional UHF RFID indoor localization algorithm, an RFID indoor positioning algorithm based on adaptive bat algorithm (ABA) was proposed.Firstly, the phase of multiple frequency points was obtained by frequency hopping technology, and the location evaluation function of bat algorithm was established based on the angle information of multiple signal classification (MUSIC) algorithm and the distance information of clustering.Secondly, the bat location was initialized by tent reverse learning to increase the diversity of the population, and the adaptive weight factor was introduced to update the bat location.Finally, the target position was searched iteratively based on the position evaluation function to achieve fast centimeter level positioning.Experimental results show that the median localization error of the proposed algorithm is 7.74 cm, and the real-time performance is improved by 12 times compared with the traditional positioning algorithm based on the Chinese remainder theorem (CRT).

Radio frequency identification (RFID) is a broad rapidly evolving skill in the past few years. It is characterized by non-contact identification, fast read and write speed, small label size, large data storage capacity, and other technical advantages. RFID technology for goods movement has completely changed the traditional supply chain management, greatly improved the operational efficiency of enterprises, and has become an important method for the development of supply chain logistics. This work mainly studies and analyzes the RFID supply chain, introduces the development and application of RFID supply chain sector technology, and discusses the operation of the supply chain in detail. Then, according to the existing RFID supply chain, a RFID supply chain artificial intelligence (AI) based approach to technology is proposed, and the data analysis of RFID supply chain is introduced in detail. In this work, through the research experiment of AI technology RFID supply chain data analysis, the experimental data show that there are several time-consuming links in the supply chain system. The time consumed in the AI RFID system is 9.9, 3.4, 3.5, and 29.9 min, respectively, while each link in the original system takes 13.4, 4.9, 4.9, and 34.9 min. It can be seen from the above data that the amount of time in each system link of the AI RFID supply chain system is less than that of the original supply chain system, which shortens the entire product passing cycle and greatly improves work efficiency.

High-speed detection and identification of mobile terminals located in the vicinity of highly directive base stations is essential for future mm-wave communication systems. We propose a novel optoelectronic frequency modulated continuous wave (FMCW) radar system for detection, localization and identification of multiple mobile terminals. The simultaneous localization and identification of multiple mobile terminals is achieved by using frequency scanning mm-wave leaky-wave antennas (LWAs) and backscattering communications. LWAs provide RF-based beam steering for estimating the direction of arrival of the echoes using a FMCW radar signal. Additionally, the implementation of backscattering technology in the mobile terminals allows the identification of users. Hence, simultaneous user localization and identification without the use of any complicated radar signal post-processing algorithms is possible. Finally, the introduced modulated backscattering reflection shifts the signals of the targets at higher frequencies, which exhibit lower noise floor and thus have higher signal to noise ratio (SNR).

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies.

A biometric lock design method was proposed based on the internal features of human body, namely PBLock.It used the backscattered RF signals to collect the impedance characteristics of human fingertips through the contact between human fingers and tags.The advantage of such authentication method is that the human impedance was not easy to be stolen, and it was fully integrated with the hardware characteristics of the device (RFID tags), which greatly increased the difficulty for the attacker to copy and clone.To ensure the availability and efficiency of the system, an optimization authentication mechanism was proposed by cutting tag antenna, which effectively improved the impedance sensitivity.Moreover, the feasibility of passive drive was discussed by using the electromagnetic energy advantage of RFID system.Through a large number of experimental evaluations, the authentication accuracy of PBLock can reach 96%, and the average time cost of a single authentication is 1.4 seconds.Some attack models were presented based on practical environment.The results show that PBLock can effectively prevent counterfeiting attack, impersonation attack and replay attack, which provides an opportunity for the secure application of new biometric electronic locks.

The research article's primary aim was to provide a comprehensive and systematic view of a new dimension of logistics in the form of logistics innovations in agricultural enterprises in Slovakia. The intention thereof was to define and then analyse selected logistics innovations in agricultural enterprises. The research object was formed by 95 agricultural enterprises in Slovakia. A questionnaire was used as the research instrument. MS Excel and IBM SPSS Statistics 20 were used to process the data in the questionnaire. The univariate descriptive statistics and multivariate descriptive statistics were used to interpret the data. The research results were tested and evaluated by means of Spearman's rank correlation coefficient. Medium-sized agricultural enterprises in Slovakia represented the largest segment (68%) of the study. The agricultural enterprises mostly understand the use of smart technologies as an innovation in logistics (mean = 4.43). The results of the research showed that the analysed agricultural enterprises intend to innovate mainly the production logistics (mean = 3.20). Out of the logistics processes, the agricultural enterprises want to innovate mostly the inventory management (mean = 4.55). Lean logistics is the most frequently implemented logistics innovation in agricultural enterprises (mean = 3.64). The hypothesis testing has proven that there is a statistically significant relationship between the utilisation of lean logistics and production logistics innovation. In addition, it was proven that there is a statistically significant relationship between the utilisation of radio frequency identification (RFID) and the logistics process innovation - inventory management.

The article discusses the experience of introducing and using multifunctional identification cards in the education system of the Republic of Belarus. The main advantages of using radio frequency identification technologies are considered. Electronic identification cards have been introduced in the national education system: student cards, employee IDs, student cards. Due to the observance of the principles of continuity of technical solutions in the process of project evolution, solutions are successfully functioning that allow the use of smart cards in functional applications (access control systems in university buildings and dormitories; reader service systems in university libraries, systems for granting benefits to document owners in the provision of various services on the territory of the university). Along with the introduction of multifunctional identification cards, the BSU has created an infrastructure for the use of new electronic documents on the Mifare Plus platform.

A biometric lock design method was proposed based on the internal features of human body, namely PBLock. It used the backscattered RF signals to collect the impedance characteristics of human fingertips through the contact between human fingers and tags. The advantage of such authentication method is that the human impedance was not easy to be stolen, and it was fully integrated with the hardware characteristics of the device (RFID tags), which greatly increased the difficulty for the attacker to copy and clone. To ensure the availability and efficiency of the system, an optimization authentication mechanism was proposed by cutting tag antenna, which effectively improved the impedance sensitivity. Moreover, the feasibility of passive drive was discussed by using the electromagnetic energy advantage of RFID system. Through a large number of experimental evaluations, the authentication accuracy of PBLock can reach 96%, and the average time cost of a single authentication is 1.4 seconds. Some attack models were presented based on practical environment. The results show that PBLock can effectively prevent counterfeiting attack, impersonation attack and replay attack, which provides an opportunity for the secure application of new biometric electronic locks.

The article discusses the experience of introducing and using multifunctional identification cards in the education system of the Republic of Belarus. The main advantages of using radio frequency identification technologies are considered. Electronic identification cards have been introduced in the national education system: student cards, employee IDs, student cards. Due to the observance of the principles of continuity of technical solutions in the process of project evolution, solutions are successfully functioning that allow the use of smart cards in functional applications (access control systems in university buildings and dormitories; reader service systems in university libraries, systems for granting benefits to document owners in the provision of various services on the territory of the university). Along with the introduction of multifunctional identification cards, the BSU has created an infrastructure for the use of new electronic documents on the Mifare Plus platform.

In this paper, we study the throughput utility functions in buffer-equipped monostatic backscatter communication networks with multi-antenna Readers. In the considered model, the backscatter nodes (BNs) store the data in their buffers before transmission to the Reader. We investigate three utility functions, namely, the sum, the proportional and the common throughput. We design online admission policies, corresponding to each utility function, to determine how much data can be admitted in the buffers. Moreover, we propose an online data link control policy for jointly controlling the transmit and receive beamforming vectors as well as the reflection coefficients of the BNs. The proposed policies for data admission and data link control jointly optimize the throughput utility, while stabilizing the buffers. We adopt the min-drift-plus-penalty (MDPP) method in designing the control policies. Following the MDPP method, we cast the optimal data link control and the data admission policies as solutions of two independent optimization problems which should be solved in each time slot. The optimization problem corresponding to the data link control is non-convex and does not have a trivial solution. Using Lagrangian dual and quadratic transforms, we find a closed-form iterative solution. Finally, we use the results on the achievable rates of finite blocklength codes to study the system performance in the cases with short packets. As demonstrated, the proposed policies achieve optimal utility and stabilize the data buffers in the BNs.

In this paper, we introduce an ultra-high frequency radio frequency identification (UHF-RFID) mobile robot platform that is capable of performing fully autonomous inventory taking and stocktaking by providing three-dimensional (3D) product maps and thus making possible the concept of a smart warehouse. The proposed novel hardware architecture consists of an eight-channel UHF-RFID-listener for parallel signal phase recovery, including two different carrier leakage suppression circuits and a correlation decoder for each channel for the tag signal, which can handle a backscatter link frequency (BLF) deviation of up to 22 % to decode the tag data. The system also uses eight parallel channels for multiple-input multiple-output (MIMO) localization. For the system evaluation we labeled clothes stored in a warehouse with tags and generated their product map. The proposed localization algorithm is based on a synthetic aperture radar (SAR) MIMO approach that needs exact knowledge of the antenna positions and, therefore, of the driven trajectory. This position is provided by the robot, which takes advantage of a simultaneous localization and mapping (SLAM) algorithm, determining the position with 1 cm accuracy while generating two-dimensional (2D) maps of the surroundings. We placed ten tags at known positions to assess the system's performance and were able to locate these tags within a root mean square error (RMSE) of 1.45 cm in 3D.

Abstract The electromagnetic radiation of electronic equipment carries information and can cause information leakage, which poses a serious threat to the security system; especially the information leakage caused by encryption or other important equipment will have more serious consequences. In the past decade or so, the attack technology and means for the physical layer have developed rapidly. And system designers have no effective method for this situation to eliminate or defend against threats with an absolute level of security. In recent years, device identification has been developed and improved as a physical-level technology to improve the security of integrated circuit (IC)-based multifactor authentication systems. Device identification tasks (including device identification and verification) are accomplished by monitoring and exploiting the characteristics of the IC’s unintentional electromagnetic radiation, without requiring any modification and process to hardware devices, thereby providing versatility and adapting existing hardware devices. Device identification based on deep residual networks and radio frequency is a technology applicable to the physical layer, which can improve the security of integrated circuit (IC)-based multifactor authentication systems. Device identification tasks (identification and verification) are accomplished by passively monitoring and utilizing the inherent properties of IC unintended RF transmissions without requiring any modifications to the analysis equipment. After the device performs a series of operations, the device is classified and identified using a deep residual neural network. The gradient descent method is used to adjust the network parameters, the batch training method is used to speed up the parameter tuning speed, the parameter regularization is used to improve the generalization, and finally, the Softmax classifier is used for classification. In the end, 28 chips of 4 models can be accurately identified into 4 categories, then the individual chips in each category can be identified, and finally 28 chips can be accurately identified, and the verification accuracy reached 100%. Therefore, the identification of radio frequency equipment based on deep residual network is very suitable as a countermeasure for implementing the device cloning technology and is expected to be related to various security issues.

Since the radio frequency identification (RFID) technology was put forward,it has been widely used in the transportation,logistics,industry and business due to its convenience and high efficiency.As a carrier for storing recognizable data,the RFID tags play a crucial role in the Internet of things (IoT).More and more functions and modules are embedded in the RFID tags and developed into smart tags in different application fields.Recently,wireless batteryless smart tags have arisen and grown rapidly with the development of the IoT and various emerging backscatter technologies.Wireless batteryless smart tags use the passive backscatter technology to harvest the energy and transmit information through radio frequency signals.Starting from the RFID technology,the history of the RFID and smart tags was introduced briefly,the differences between traditional smart tags and emerging wireless smart tags were compared,the advantages of wireless batteryless smart tags were concluded,several specific applications of it in different fields were listed,and the open challenges were analyzed.

This paper presents a compact circularly polarized microstrip antenna for radio frequency identification (RFID) readers. The dimensions of the proposed antenna are reduced by etching a cross slot in the radiation patch and loading four grounded coupling patches on the four corners of the radiation patch. An inverted Z-shaped coupling feedline is used to realize circularly polarized operation. The measured -10 dB |S11| bandwidth, 3-dB axial ratio bandwidth and maximum gain are 872-1095 MHz, 888-933 MHz, and 5.52 dBi, respectively, at 922 MHz frequency. The overall dimension of the proposed antenna is 0.36 λgX 0.36 λgX 0.05 λg (λg is the guide wavelength at the center frequency of 915 MHz). The proposed antenna has small size with good overall performance and is suitable for compact RFID devices.

Counterfeit medications are known as the medications that were manufactured for the purpose of deceptively representing as authentic, effective and original in the market. Such medications cause severe health issues for patients. Counterfeited drugs have an inimical effect on the human health. The legal manufacturing companies also face threats to their revenue loss due to these counterfeited medicines. In this paper, we introduce a novel authentication protocol for anti-counterfeited drugs systems based on Internet of Things (IoT) to help checking the validity of drugs “unit dosage”. Our protocol uses the near-field communication (NFC) as it is convenient for mobile environment. The protocol also offers reliable update phase for NFC. Furthermore, our scheme is complemented with performance evaluation along with the use of random oracle model for formal security analysis. We also evaluate our protocol broadly using Py-Charm tool. Results show that proposed protocol resists most of common related flaws almost in equal computing cost with more added security features.

The common paradigm of the RFID network is to obtain data from multiple tags through a reader. The tag collision problem seriously affects the efficiency of tag identification. The anti-collision algorithm provides a reliable guarantee to identify multiple tags as fast as possible. Many papers have proposed query tree algorithms based on bit tracking, but these algorithms still have problems such as idle paging and redundant prefixes. In this paper, we start with an analysis of state-of-the-art anti-collision algorithms. Based on summarizing current works, we propose a Smart Batch-Processing protocol (SBP) for RFID multiple tag identification based on M-ary collision bit arbitration mechanism. In the SBP, the reader firstly maps m-bit collision bits into 2m slots and then predicts the distribution of tags in slots. Finally, the reader allows the tag to return the remaining part of its ID at the mapped slot using a batch-processing command. By using SBP, the reader can simultaneously identify 2m groups of tags in a slot. Besides, empty queries can be eliminated in the tag identification process. The description of SBP is given first, and then, its performance is theoretical analyzed and simulated. Both theoretical analysis and extensive simulation results show that the SBP algorithm reduces 22.94% and 22.28% of time and energy consumption compared to other M-ary collision bit arbitration based methods.

Thanks to the miniaturization of components and therefore the significant reduction of energy needs, the design of passive smart-tag incorporating intelligent sensors is now possible. The goal of this study is to develop a tire pressure sensor for an electric bike with battery-free. Therefore, there is no need to replace periodically the battery, so as cuts off maintenance costs and extend the life time of the smart-tags. An UHF RFID reader installed on the bicycle frame and connected to the battery e-bike transmits the RF power at 868 MHz toward each tag integrated in the wheel rims.Firstly, this article presents the whole hardware architecture of a passive smart-tag which had to respond of technics constraints and the complexity to integrate 2 antennas in a small package box. Secondly, the methods to ensure an ultra-low consumption are developed. Finally, practical tests are compared with simulations, and, validations are carried out showing the compliance and the industrialization of this innovative system.

Throughout the years, attendance monitoring has widely evolved from manual methods to the integration of technology. Manual attendance monitoring in an institution consumes time, hard to maintain, prone to human error, and leads to misplaced or lost records. Individuals also tend to take advantage of the flaws of the manual method that leads them to commit hoax and forgery. Despite the innovation of different automated systems, biometric recognition systems, RFID technology, and barcodes also have disadvantages. To address the shortcomings of the existing methods and to minimize the contingencies, this paper describes the use of a web application that utilizes an ultra-high frequency (UHF) RFID with a motion sensor to monitor the attendance of students from the University of San Carlos. A Raspberry Pi 3 Model B is used in controlling the input and output, in which it is outputted on an LED. Wireless communication is used between the hardware system and the web system to record and validate students’ attendance records. A web application is developed for faculty, students, and chairperson to monitor class attendance and to minimize contingencies. In addition, the faculty can edit and generate a seating plan.

Science, technology, engineering and mathematics (STEM) education is a globalized trend of equipping students to facilitate technological and scientific developments. Among STEM education, technology education (TE) plays a significant role in teaching applied knowledge and skills to create and add value to systems and products. In higher education, the learning effectiveness of the TE assisted by the immersive technologies is an active research area to enhance the teaching quality and learning performance. In this study, a taught subject of radio frequency identification (RFID) assisted by using mixed reality technologies in a higher education institution was examined, while the soft systems methodology (SSM) was incorporated to evaluate the changes in learning performance. Under the framework of SSM, stakeholders’ perceptions toward immersive learning and RFID education are structured. Thus, a rich picture for teaching activities is established for subject control, monitoring, and evaluation. Subsequently, the design of TE does not only satisfy the students’ needs but also requirements from teachers, industries, and market trends. Finally, it is found that SSM is an effective approach in designing courses regarding hands-on technologies, and the use of immersive technologies improves the learning performance for acquiring fundamental knowledge and application know-how.

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces.

Structural health monitoring (SHM) plays a critical role in ensuring the safety of large-scale structures during their operational lifespan, such as pipelines, railways and buildings. In the last few years, radio frequency identification (RFID) combined with sensors has attracted increasing interest in SHM for the advantages of being low cost, passive and maintenance-free. Numerous scientific papers have demonstrated the great potential of RFID sensing technology in SHM, e.g., RFID vibration and crack sensing systems. Although considerable progress has been made in RFID-based SHM, there are still numerous scientific challenges to be addressed, for example, multi-parameters detection and the low sampling rate of RFID sensing systems. This paper aims to promote the application of SHM based on RFID from laboratory testing or modelling to large-scale realistic structures. First, based on the analysis of the fundamentals of the RFID sensing system, various topologies that transform RFID into passive wireless sensors are analyzed with their working mechanism and novel applications in SHM. Then, the technical challenges and solutions are summarized based on the in-depth analysis. Lastly, future directions about printable flexible sensor tags and structural health prognostics are suggested. The detailed discussion will be instructive to promote the application of RFID in SHM.

This article presents results of research dealing with the usage of automatic identification and data capture based on the radio frequency identification technology in the environment of Industry 4.0. This topic is actual both for academic theory and for practical business, as in the current business environment there is the undeniable and rising need of fast, accurate and cost-effective acquirement of data for the purposes of process management and controlling. Technologies of automatic identification and data capture can be successfully used to fulfil such need, assuming the right technology is chosen. Results of conducted research show, that the radio frequency identification technology can be successfully used in particular components of Industry 4.0, such as big data, system integration and internet of things. The results also show, that only the ultra-high and high frequency variants of radio frequency identification technologies are suitable for the applications connected to the Industry 4.0 concept. The low frequency variant of this technology is too limited by its technical restriction, especially the low velocity and possible distance of reading, to be of any use for such applications.

Сurrent temperature monitoring, humidity, physical condition and real-time data transmission are extremely important in the pharmaceutical supply chains. The intelligent logistics system for monitoring the state of objects, implemented on the basis of the Internet of things, which can be used for the pharmaceutical industry, has been considered in this article. The two-level network architecture of the IOT platform has been reviewed. The first level is radio frequency object identification, and the second level is organization of wireless sensor network. By adapting the proposed system, the pharmaceutical company can easily monitor and control the quality of the goods during transportation.

Internet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The radio frequency fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust principle component analysis (RPCA) is used for the dimensionality reduction and the support vector machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real IoT equipment environment.

Increasing supply chain performance (SCP) has always been a common phenomenon in the history of organizations. Recent technological advancements [including, radio frequency identification (RFID) in combination with big data predictive analytics (BDPA)] have been able to significantly leverage SCP. In this respect, there is an urgent need for acceptance of RFID technology as well as BDPA to be empirically investigated in order to better understand the enhancement of SCP. Therefore, this paper aims at establishing and empirically investigating the relationship between BDPA acceptance, RFID acceptance, and SCP, respectively. The population of this paper comprises the pharmaceutical logistics industry in China. The sample was obtained from employees through the use of a simple random sampling method. Data were collected through an adapted questionnaire. SmartPLS 3.0 software was used for structural equation modeling analysis. Partial least square-structural equation modeling (PLS–SEM), the value of path coefficient, was found to be 0.700. This showed a direct positive effect of BDPA acceptance on SCP. Moreover, RFID acceptance was also tested through the “variance accounted for” method of mediation. Results showed a partial mediation of 57% between BDPA and SCP. Theoretically, this paper fills the knowledge gap by finding relationships between BDPA and SCP via a mediation role of RFID technology. Practically, it would be beneficial for supply chain managers in the pharmaceutical industry to adopt and implement BDPA and RFID technology so as to enhance SCP.

Previous research studies mostly focused on enhancing the security of radio frequency identification (RFID) protocols for various RFID applications that rely on a centralized database. However, blockchain technology is quickly emerging as a novel distributed and decentralized alternative that provides higher data protection, reliability, immutability, transparency, and lower management costs compared with a conventional centralized database. These properties make it extremely suitable for integration in a supply chain management system. In order to successfully fuse RFID and blockchain technologies together, a secure method of communication is required between the RFID tagged goods and the blockchain nodes. Therefore, this paper proposes a robust ultra-lightweight mutual authentication RFID protocol that works together with a decentralized database to create a secure blockchain-enabled supply chain management system. Detailed security analysis is performed to prove that the proposed protocol is secure from key disclosure, replay, man-in-the-middle, de-synchronization, and tracking attacks. In addition to that, a formal analysis is conducted using Gong, Needham, and Yahalom logic and automated validation of internet security protocols and applications tool to verify the security of the proposed protocol. The protocol is proven to be efficient with respect to storage, computational, and communication costs. In addition to that, a further step is taken to ensure the robustness of the protocol by analyzing the probability of data collision written to the blockchain.

Cognition arguably drives most behaviours in animals, but whether and why individuals in the wild vary consistently in their cognitive performance is scarcely known, especially under mixed-species scenarios. One reason for this is that quantifying the relative importance of individual, contextual, ecological and social factors remains a major challenge. We examined how many of these factors, and sources of bias, affected participation and performance, in an initial discrimination learning experiment and two reversal learning experiments during self-administered trials in a population of great tits and blue tits. Individuals were randomly allocated to different rewarding feeders within an array. Participation was high and only weakly affected by age and species. In the initial learning experiment, great tits learned faster than blue tits. Great tits also showed greater consistency in performance across two reversal learning experiments. Individuals assigned to the feeders on the edge of the array learned faster. More errors were made on feeders neighbouring the rewarded feeder and on feeders that had been rewarded in the previous experiment. Our estimates of learning consistency were unaffected by multiple factors, suggesting that, even though there was some influence of these factors on performance, we obtained a robust measure of discrimination learning in the wild.

Objective: Technologies such as Radio Frequency Identification (RFID) offer many benefits to health care providers and may raise stakeholder concerns. This study reviews a new technology from another industry, summarizes previous research on medical applications of RFID, and analyses survey responses on RFID applications. The goal is to develop principles for evaluating and implementing new technologies. Design: Marketing and stakeholder theories were used to develop lessons from the case study and from prior research. A survey was mailed to adults in four Midwestern states in the US. Main Outcome Measures: Respondent support ratings for two medical and two non-medical applications of RFID were analysed using principal component analysis and binary logistic regressions. Profiles of those supporting the applications were developed. Results: The case study highlighted the importance of considering the needs and concerns of all stakeholders. Previous studies suggested that many hospital administrators who examined RFID may not have included some stakeholders. This research found that support for RFID varied across respondents and across applications. Anxiety about technology was negatively linked with RFID support. Religiosity also had negative coefficients for some applications. Conclusions: Administrators considering new technologies need to consider patient privacy needs and stakeholder concerns. Surveying stakeholders and utilizing advisory boards could help administrators tailor their actions to the communities they serve. A few opponents to a technology can limit its adoption. Regular, two-way communications may help health care organizations improve technology decisions and enhance the odds of implementation success.

This paper designs a UHF band and small passive anti-metal RFID electronic tag antenna. On the basis of one end port enclosed small micro-strip antenna, the substrate uses dielectric constant in 22 of the microwave dielectric ceramic to further decrease the size of the antenna. Insert-fed is adopted，in which one end grounds through short-circuit patch，and the other end of the radiation patch loads short-circuit patch to redize broadband. Using HFSS simulation software for antenna design, the size of the antenna is about 32 mm×24 mm×4 mm, which is placed on a 200 mm×200 mm metal plate. In the working band of 860～960 MHz, S11 is less than -15 dB, impedance matching is excellent, and the reading distance of the simulation of MATLAB is about 7 m. The overall design of the tag antenna meets the design requirements of miniaturization, anti-metal, broadband, and low cost.

Abstract Purpose The main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers. Methods This study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon. Results Using vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. The study also discovered that choice of road type and car type, have varying influence on peak hour travels. Conclusions The high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion.

Purpose: This research seeks to identify and rank factors affecting RFID's adoption and is based on the theory of diffusion of innovation in public libraries. Methodology: 13 variables in three categories, namely technical, organizational, and environmental were examined. A questionnaire with 30 questions was designed and distributed among 320 librarians in 68 public libraries in the cities of Shiraz and Kerman. Respondents were selected by simple random sampling. 193 completed questionnaires were returned. SPSS and LISREL were used for descriptive and inferential statistical analysis (t-test, structural equation model and TOPSIS). Findings: All identified factors have some effect on the use of RFID technology. Also all identified affecting factors of RFID adoption have correlation as an overall structure. The model is suitable in terms of fitness indexes. Ultimately, factors ranking revealed that the most important one is the cost. Originality/Value: The findings indicate the necessity of carefully considering adoption of the technology. What we found what needs to be considered to prevent the failure of the project and waste of funds.