Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis

Pandian, M. Thurai; Chouhan, Kuldeep; Kumar, B. Muthu; Dash, Jatindra Kumar; Jhanjhi, N. Z.; Ibrahim, Ashraf Osman; Abulfaraj, Anas W.

doi:10.3390/electronics11182968

Cited by 6 publications

(3 citation statements)

References 44 publications

(47 reference statements)

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“…To get elite and high security, the RFID strategy regulated with the PEIS system works honourably, and an individual reader goes about as a worker based on bandwidth. Also, alongside the PEIS approach is set up for upgrading the exhibition just as ensuring security highlights and the segments of a regular RFID network [26], [28], [40]. Vitruvius is a juncture that enables clients to produce ongoing applications utilizing street vehicle sensors.…”

Section: Literature Surveymentioning

confidence: 99%

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Pandian,

Somasundaram,

Sahu

et al. 2024

IEEE Access

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This paper focuses on the use of radio frequency signals for non-contact tracking and localization utilising Radio Frequency Identification (RFID) technology. The clustering approach is quite useful when dealing with large RFID readers. This method stimulates the expansion of RFID nodes without compromising the overall network performance. The Cluster Head (CH) is the most critical node in a clustered RFID system. This research presents a method for dynamically selecting the cluster head that takes the connectivity and power of each RFID reader into account. In dynamic mode, selecting a new cluster head is based on Fuzzy Logic. Based on these data, the energy level of 0.443 and centrality of 0.809 are the thresholds at which a node has a 91.8 % chance of becoming a CH. The management of massive RFID networks is also handled, with cluster numbers increasing at different time intervals. The RFID network's effectiveness is determined by measuring throughput, accuracy, delay, success, and error rates. The network may accommodate up to a thousand nodes with 13 node leaders for improved capacity. The results show a 97.8 % success rate, 0.22 % accuracy, a 2.64 % error rate, and a 36.91 second latency.

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Section: Literature Surveymentioning

confidence: 99%

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Pandian,

Somasundaram,

Sahu

et al. 2024

IEEE Access

Self Cite

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“…The duration of this interval significantly influences the system's responsiveness and the immediacy of data transmission. Formula 12 is the expression for delay [70].…”

Section: ) Delaymentioning

confidence: 99%

RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review

Yejiao,

Kamaludin,

Mohammed Alduais

et al. 2024

IEEE Access

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With the development of intelligent manufacturing in China, Radio Frequency Identification (RFID), a key technology for smart factories, has received widespread attention. As RFID applications expand, so does the size of their networks. It makes it more difficult to ensure RFID signal coverage, leads to communication problems, and increases equipment energy consumption and costs, thereby posing challenges in the realm of RFID network planning (RNP). The RNP problem needs to consider multiple objectives and constraints such as coverage, conflicts, economic benefit, and load balance which have been proven to be optimized by swarm intelligent optimization algorithms. Therefore, this study reviews smart factories, RFID technology, swarm intelligence optimization algorithms and RFID network planning. The improvement direction of swarm intelligence optimization algorithms and factors affecting RFID network performance are also explored. In addition, it reviews and analyzes the applications of swarm intelligence algorithms to RNP problems and discusses the innovations and drawbacks of these approaches. Finally, some research limitations and directions are identified.

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“…Usually, the reader is divided into multiple small slots, called slots, with each tag in a different slot responding to the reader's read and write commands, thus completing multi-access. However, for the existing tag recognition system using the ALOHA algorithm, the TREE algorithm, or a combination of the two algorithms, the problem of high latency still exists [108][109][110], so choosing the appropriate transmission protocol and algorithm for optimization can reduce the delay in the RFID communication process, and thus, improve the channel utilization.…”

Section: Future Prospectmentioning

confidence: 99%

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

Wang,

Luo,

Guo

et al. 2023

Electronics

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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.

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Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis

Cited by 6 publications

References 44 publications

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review

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

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