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In this paper, we present a novel time-efficient tag identification protocol for active radio frequency identification (RFID) systems. Our protocol design is based on a conventional M-ary Detecting Tree (MDT) that divides contention tags into M subgroups and performs the identification in two phases: scanning and collecting. While the MDT protocol effectively eliminates redundant empty slots (i.e., slots without any tag responses), it requires two success slots (i.e., slots with exactly one tag’s response) to identify one tag, raising concerns about potential identification delays. We introduce an innovative anti-collision protocol called Enhanced M-ary Detecting Tree (EMDT) to address this issue. The proposed EMDT switches from the scanning phase to the collecting phase when the estimated number of responding tags becomes smaller than a predefined threshold, which is found via theoretical analysis and has an optimal value of three. This adjustment allows the reader to typically require just one success slot for identifying each tag, thus significantly reducing the total time needed to identify all tags. The proposed phase-switching mechanism is backed by Zero-Estimation (ZE) for tag cardinality estimation and Manchester encoding for data transmission, both widely employed in RFID standards. Theoretical analysis and computer simulations are performed in different system settings, including ideal and non-ideal transmission channels, to validate the efficacy of the proposed protocol in comparison with conventional alternatives.

Radio-frequency identification is one of the Internet of Things’ most promising technologies and has been recently used in combination with mobile robots for logistics in business and retail applications. This manuscript deals with the localization of passive UHF RFID tags within industrial environments employing receiving antennas mounted on a mobile robot by using multilateration techniques that exploit narrowband phase-delay measurements. Two distinct Particle Filter approaches are presented to solve the 3D multilateration problem online and take advantage of a synthetic aperture created by the motion of the robot in the environment. One of the methods can operate in the presence of acquisition jumps since it does not rely on an unwrapping technique. Experimental results show promising performance concerning the recent literature. Moreover, the presented approach enables robust estimations concerning signal loss due to communication disturbances in noisy environments, typical of the industrial setting.

Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.

In this study, a low-cost dual-chip RFID tag antenna is proposed for indoor localization. The antenna integrates two RFID chips, each generating opposing radiation patterns. Angle-of-arrival based localization is performed with a single reader by analyzing the Received Signal Strength Indicator (RSSI) and the RSSI ratio of both chips. The antenna incorporates a mechanically adjustable reflector at the back that allows simple beam elevation control to tune the antenna for different indoor environments. The proposed dual-chip tag was measured in an indoor environment with different beam configurations. The localization is performed using a simple sinusoidal regression model with three model parameters and a linear regression model with two model parameters to fit the RSSI ratio of both chips and the RSSI of each individual chip, respectively. The proposed technique yields an average angle-of-arrival error better than 6.6° within the tag’s horizontal detection range of 4.2 meters.

Visually Impaired Persons (VIPs) have difficulty in recognizing vehicles used for navigation. Additionally, they may not be able to identify the bus to their desired destination. However, the bus bay in which the designated bus stops has not been analyzed in the existing literature. Thus, a guidance system for VIPs that identifies the correct bus for transportation is presented in this paper. Initially, speech data indicating the VIP’s destination are pre-processed and converted to text. Next, utilizing the Arctan Gradient-activated Recurrent Neural Network (ArcGRNN) model, the number of bays at the location is detected with the help of a Global Positioning System (GPS), input text, and bay location details. Then, the optimal bay is chosen from the detected bays by utilizing the Experienced Perturbed Bacteria Foraging Triangular Optimization Algorithm (EPBFTOA), and an image of the selected bay is captured and pre-processed. Next, the bus is identified utilizing a You Only Look Once (YOLO) series model. Utilizing the Sub-pixel Shuffling Convoluted Encoder–ArcGRNN Decoder (SSCEAD) framework, the text is detected and segmented for the buses identified in the image. From the segmented output, the text is extracted, based on the destination and route of the bus. Finally, regarding the similarity value with respect to the VIP’s destination, a decision is made utilizing the Multi-characteristic Non-linear S-Curve-Fuzzy Rule (MNC-FR). This decision informs the bus conductor about the VIP, such that the bus can be stopped appropriately to pick them up. During testing, the proposed system selected the optimal bay in 247,891 ms, which led to deciding the bus stop for the VIP with a fuzzification time of 34,197 ms. Thus, the proposed model exhibits superior performance over those utilized in prevailing works.

“Knowledge transfer” is achieved through sharing or disseminating knowledge, and providing inputs to problem solving; it is commonly associated with attending a series of classroom lectures and maintaining students’ engagement with the taught subject. This paper examines how a specific radio frequency identification (RFID) based information system (IS) solution could be utilized to help monitor and increase engagement of university students during lecturing activities. This IS solution relies on student attendance as the main method to measure their engagement. Initially, the main stakeholders were identified: students, lecturers, administration team and the Student Loans Company (source of funding). A value proposition canvas was subsequently created, and potential system requirements were identified. A design of the proposed RFID based system was created based on these requirements and then compared with a real-life (already existing) system at Henley Business School. By comparing these two systems, the authors determined related benefits/drawbacks of the proposed system in monitoring student engagement. Potential benefits consisted of allowing all parties to easily capture attendance (with very minimal involvement of the university’s staff) and increased efficiency in analyzing student attendance data. Its main limitation was inaccurately capturing the exact time a student leaves a session. Building a working prototype for detailed evaluation and further fine-tuning/improvements must be part of future work.

Internet of Things (IoT) is being widely developed in various fields, and its penetration rate in daily life is continuously increasing. The nature of the social function of objects and giving them an identity has made it possible to successfully integrate this technology into many traditional systems and improve their performance by automation. Libraries are one of the most obvious examples of smartening by IoT architecture. So far, various architectures have been presented for smartening libraries through IoT technology. However, a low-cost and ideal architecture that can cover all the requirements in a wide range of smart library applications has not been provided. This article attempts to fill some of the existing research gaps in this field by presenting a new architecture for smartening libraries. In the proposed method, Software Defined Networking (SDN) is used to reduce implementation costs and improve the management process of network components. In this architecture, the communication platform of network active objects is formed based on a cluster-based topology. Also, passive Radio Frequency IDentification (RFID) tags are utilized to manage books and library property. Two stages of evaluation have been conducted for the suggested method's performance: actual deployment and computer simulations. Based on the findings, it can be concluded that this study has succeeded in creating an effective and affordable design for smart libraries, which is a major advancement over conventional libraries.

Nowadays, vehicle tracking and location identification are necessary for safety. With vehicle tracking, it is also important to record the arrival and departure timings of vehicles. This recorded data will be helpful in different aspects like identifying the stolen vehicle, to observing the transportation system functionality in an organization or any other place. Implementing a smart vehicle monitoring system that combines tracking and data collection is the main goal of the current paper. Additionally, a mobile warning is delivered to the driver's phone when the car arrives late. We can also access this data through mobile applications from anywhere. RFID technology is applied to this system's implementation. Radio Frequency Identification is what RFID stands for. It is a wireless technology which consists of a Tag and Reader as major components. To store the data on the website IoT technology is used. Such systems are simple to construct at a minimal cost when IoT (Internet of Things) and RFID technologies are combined. [ABSTRACT FROM AUTHOR]

The level of poor economic situations in Nigeria has made people conscious of their vehicles, aged parents, younger ones and valuable belongings. Lots of car owners both personal and commercial are in fear of consistent increase in security challenges, excessive reports of vehicle stealing, kidnapping for ransom on the high ways on daily basis, as a result, are in constant search for efficient system to monitor their vehicle at any point in time. There are many systems that have been developed to reduce these problems by introduction of an effectual vehicle tracking system which is developed for tracking the movement and location of any device embedded vehicle at will, with the aid of microcontroller which employs IoT (Internet of Things) technologies to communicate with either Smartphone or personal computer. This paper reviews some existing works and explores in details to compare some of these systems, the technologies utilized, successes achieved, and gaps deserving further improvement in such systems. [ABSTRACT FROM AUTHOR]

As crop vandalization with conflicts between farmers and herdsmen become recurrent in Nigeria, existing farm intrusion prevention methods such as fence mounting and placement of farm guards can no longer guarantee farm security. This is because intruders either jump over the fence or attack guards on duty without visual evidence. Therefore, a complementary approach using computer technologies for effective detection is required. This paper presents an IoT-based farm intrusion detection model using RFID and image recognition technology. RFID sensor as well as cameras are placed at entrances of a fenced farmland for simultaneous identification. The sensor reads workers’ tags for identification, while cameras capture images of users for further identification as captured images are sent to Convolutional Neutral Network (CNN) for recognition. A user whose image cannot be recognized is flagged as an intruder and an intrusion alert with visual evidence is sent to the farm owner. The system showed a high level of effectiveness with an accuracy of 90%, Precision of 70%, and 80% Recall rate and effectively controlled the rate of illegal encroachment into farmland.