Radio Frequency Identification Anti-collision Algorithm Research Articles

An adaptive RFID anti-collision algorithm for network intrusion detection

Radio frequency identification (RFID) provides real-time network monitoring capabilities for threat identification. However, accurate detection is impeded by tag interference. This paper presents an adaptive collision tree algorithm that selects optimal binary or octal splits based on collision counts to handle interference. Experiments demonstrate an integrated RFID intrusion detection framework that achieves 8.98% higher throughput and 99.82% detection accuracy compared to other protocols. The method enables efficient real-time threat identification as networks proliferate. However, there are limitations to the approach, such as assumptions of fixed tag populations rather than dynamic tags and a lack of field testing. To strengthen the approach, further research on fluctuating tags and validation in real-world network deployments is necessary. This work presents an adaptive method for leveraging RFID to achieve scalable and accurate network intrusion detection.

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.

Efficient Tag Grouping RFID Anti-Collision Algorithm for Internet of Things Applications Based on Improved K-Means Clustering

Dynamic Frame Slotted ALOHA (DFSA) is a de facto algorithm in the EPC Global Class-1 Generation-2 protocol for Radio Frequency Identification (RFID) tag collision problem. DFSA fails when the UHF RFID tag deployment becomes dense like in Internet of Things (IoT). Existing works do not provide readers prior tag estimates. Most algorithms assume a collision slot means two tag collision. But in dense IoT applications, much more than two tags can constitute a collision slot. Moreover, research proves collision slot might occur due to other reasons such as error-prone channel. This paper proposes a RFID anti-collision algorithm, kg-DFSA that equips the reader with prior information on accurate tag estimate. In kg-DFSA, tag identification is divided into two stages – initialization and identification. In the initialization stage, the reader uses improved K-means clustering running concurrently with a tag counter algorithm to cluster tags into K groups using tags’ RN16 while the counter returns an accurate tag number estimate. In the identification stage, the tags are read only in frame chunks that match their group IDs while a new frame size look up table is developed to boost efficiency. Variants of the proposed kg-DFSA, traditional DFSA and another grouping based DFSA algorithm (FCM-DFSA) were implemented in MATLAB. Extensive Monte Carlo simulation shows the proposed kg-DFSA edges DFSA in terms of success rate 50%, system efficiency 65% and identification time 28%. The proposed model is useful in enhancing the existing MAC protocol to support dense IoT deployment of RFID.

Capture-Aware Identification of Mobile RFID Tags With Unreliable Channels

Radio frequency identification (RFID) has been widely applied in large-scale applications such as logistics, merchandise and transportation. However, it is still a technical challenge to effectively estimate the number of tags in complex mobile environments. Most of existing tag identification protocols assume that readers and tags remain stationary throughout the whole identification process and ideal channel assumptions are typically considered between them. Hence, conventional algorithms may fail in mobile scenarios with unreliable channels. In this paper, we propose a novel RFID anti-collision algorithm for tag identification considering path loss. Based on a probabilistic identification model, we derive the collision, empty and success probabilities in a mobile RFID environment, which will be used to define the cardinality estimation method and the optimal frame length. Both simulation and experimental results of the proposed solution show noticeable performance improvement over the commercial solutions.

An adaptive frame slotted ALOHA anti‐collision algorithm based on tag grouping

Multi-tag anti-collision is an important problem in radio frequency identification (RFID) application. Solving the problem is of great significance to the RFID technology application and the future internet of things; therefore, an adaptive frame slotted ALOHA anti-collision algorithm based on tag grouping (IGA) is proposed. First, a novel method for estimating the number of tags accurately is proposed. Through theoretical research and the experimental verification, a relationship is obtained between the ratio of the collision time slot in the frame and the average number of tags in each collision slot, which helps us to calculate the number of tags. Second, the method of estimating the number of tags is applied to the IGA algorithm. The reader randomly groups the tags after the number of tags are estimated, and recognises the tags by grouping. In the identification process, the idle time slot is skipped automatically, and the collided tags can be identified with an additional frame until all tags are identified. The simulation results show that the total time slot of the IGA algorithm is relatively small, and the identification efficiency is about 71%, which is 30% better than the the improved RFID anti-collision algorithm and 90% higher than the traditional ALOHA algorithm.

A Mobility Aware Binary Tree Algorithm to Resolve RFID Jam and Bottleneck Problems in a Next Generation Specimen Management System.

Hospitals are continuously working to reduce delayed analysis and specimen errors during transfers from testing stations to clinical laboratories. Radio-frequency identification (RFID) tags, which provide automated specimen labeling and tracking, have been proposed as a solution to specimen management that reduces human resource costs and analytic delays. Conventional RFID solutions, however, confront the problem of traffic jams and bottlenecks on the conveyor belts that connect testing stations with clinical laboratories. This mainly results from methods which assume that the arrival rate of specimens to laboratory RFID readers is fixed/stable, which is unsuitable and impractical in the real world. Previous RFID algorithms have attempted to minimize the time required for tag identification without taking the dynamic arrival rates of specimens into account. Therefore, we propose a novel RFID anti-collision algorithm called the Mobility Aware Binary Tree Algorithm (MABT), which can be used to improve the identification of dynamic tags within the reader’s coverage area and limited dwell time.

Adapted RFID anti-collision algorithm and its application in sharing economy

In recent years, the sharing economy has developed vigorously, and the internet of things (IoT) technology is an important technology to realise the sharing economy. As an automatic identification technology, radio frequency identification (RFID) technology is the key technology to build the internet of things and an essential part of the sharing economy. In order to solve the collision problem of tags identification, an improved grouping adaptive query tree algorithm (IGAQT) is proposed in this paper. Compared to the most of existing anti-collision algorithms, simulation results show that our proposed algorithm can achieve better performance in terms of time complexity and communication overload. And the algorithm is applied to the actual case of sharing economy.

Effective anti-collision algorithms for RFID robots system

PurposeThe purpose of this paper is to study how to improve the performance of RFID robot system by anti-collision algorithms. For radio frequency identification (RFID) robots operating in mobile scenes, effective anti-collision algorithm not only reduces missed reading but also enhances the speed of RFID robots movement.Design/methodology/approachAn effective anti-collision algorithm is proposed to accelerate tag identification in RFID robots systems in this paper. The tag collisions in the current time slot are detected by a new method, and then further resolve each small tag collision to improve system throughput, rather than the total tags number estimation. After the reader detected the collision, three different collision resolution methods were described and studied, and the situation of missing tag caused by reader moving is also discussed.FindingsThe proposed algorithm achieves theoretical system throughput of about 0.48, 0.50 and 0.61 and simulates to show that the proposed algorithm performance is significantly improved compared with the existing ALOHA-based algorithm.Originality/valueThe proposed RFID anti-collision algorithm is beneficial to improve the moving speed and identification reliability of the RFID robots in complex environments.

Channel-Quality Aware RFID Tag Identification Algorithm to Accommodate the Varying Channel Quality of IoT Environment

Radio Frequency Identification (RFID) technique is broadly adopted as the automated identification system for the Internet of Things (IoT). Many RFID anti-collision algorithms were proposed to accelerate the tag identification process. However, they misjudged some unreadable slots which were due to collision instead of the bad channel condition, causing low bandwidth usage. This study proposes the Channel-quality Aware Query Tree algorithm (CAQT) to improve the identification performance in an error-prone channel environment. CAQT has three novel features: (1) it estimates the channel quality continuously and statistically in the rapidly changing channel quality environment; (2) it asks the tag for retransmission or to split the collide tags based on the channel quality; (3) the number of the groups which it splits tags is based on the estimated number of tags collide in current slot. The simulation results show that CAQT uses less than 31% slots compared with the conventional algorithms. The simulation results also demonstrate that CAQT provides enhanced performance when the channel quality is varying especially in outdoor environment, for example, ticket checking for railway or subway system.

Design of a Quaternary Query Tree ALOHA Protocol Based on Optimal Tag Estimation Method

Radio Frequency Identification (RFID) technology is one of the most promising technologies in the IoT (The Internet of Things) era. Many RFID systems have been used in supermarkets or warehouses. There are two challenges for RFID anti-collision algorithms. The first challenge is accurately estimating the number of tags; the other is improving the efficiency of RFID systems. This paper proposes an optimal tag estimation method in which tags respond to the reader in assigned time slots instead of responding randomly. In order to improve the performance of the RFID system, a 4-ary query tree Additive Link On-line HAwaii (ALOHA) protocol is presented that combines the merits of query tree algorithm and frame slotted ALOHA, and avoids their weaknesses. Simulation results show that the proposed algorithm has a higher tag identification efficiency compared to other dynamic frame slotted ALOHA algorithms, and it can overcome the tag starvation phenomenon, because it traces each tag until all of them are identified successfully.

Optimal Frame Length Analysis and an Efficient Anti-Collision Algorithm With Early Adjustment of Frame Length for RFID Systems

This paper proposes an efficient anti-collision algorithm to improve the read performance of the EPCglobal ultra-high frequency (UHF) Class-1 Generation-2 radio-frequency identification (RFID) standard. Optimal frame length is one of the most important parameters to be adjusted in RFID anti-collision algorithms. Hence, we first derive the optimal frame length by analyzing the maximum of normalized throughput and by taking into account the unequal time intervals of successful, collision, and empty slots. We find that the optimal frame length should be set to 1.89 times of the number of tags when the ratio between collision-slot duration and empty-slot duration is 5. The proposed anti-collision algorithm is based on the mechanism of early adjustment of frame length and examines the fitness of frame length only at an optimal time slot in each read round. The primary advantage of our algorithm is the ability to achieve a good compromise between computation complexity and throughput performance. The results show that the proposed method provides up to 400 tags/s read speed and can obtain 5%–10% time-saving efficiency, as compared with typical dynamic framed-slotted ALOHA.

RFID anti-collision algorithm based on tags grouping

A new tags anti-collision algorithm was proposed for the reader collision problems in the Radio Frequency IDentification(RFID) technology.It divided tags into groups based on the coset partition theory,and restricted each group to response in a fixed timeslot.According to the query codes and collision flag bits,the reader could identify a group of tags in one slot.The Matlab simulation results show that,compared with the binary search algorithm and dynamic frame timeslot algorithm,the proposed algorithm improves the slot utilization and throughput when the number of tags is large.

New adaptive RFID anti-collision algorithm based on collision-tree

Anti-collision algorithm was a key technique to improve identification efficiency in Radio Frequency IDentification(RFID) System.The elementary binary search algorithm and some improved algorithms were analyzed,and an adaptive anti-collision algorithm based on collision-tree(ACT) was proposed.In this algorithm,reader detected collision in the feedback information of tags,and then used the first collision bit as a new node of the collision tree to group the tags.Then reader made use of stack and regressive index technology,and took the beginning and the end collision bit as the parameters of next request.The Matlab simulation results show that the proposed algorithm is effective.This algorithm can wipe off empty timeslot,reduce repetitive information and the number of bits transferred for identifying,especially fit the environment which contains large quantity of tags and tags with long information in RFID system.

An Accurate Tag Estimate Method for Improving the Performance of an RFID Anticollision Algorithm Based on Dynamic Frame Length ALOHA

In a radio-frequency identification (RFID) system, the dynamic frame length ALOHA protocol is widely adopted to solve the anticollision problem. Analysis for the anticollision problem can be divided into two primary parts. The concern of the first part is how to precisely estimate the number of tags. The other part involves determination of dynamic frame length to achieve maximum throughput or channel usage efficiency. In this paper, we present an accurate method for estimating tag quantity. This method is based on the maximum <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> probability decision. We also derive the optimal frame length using radio channel efficiency. Simulation results indicate the tag estimate error of the proposed method is less than 4%. Use of our proposed tag estimate method together with optimal frame length can achieve close to the theoretical maximum throughput of the framed ALOHA algorithm.