RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization

The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to re...

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Vydané v:IEEE internet of things journal Ročník 8; číslo 5; s. 3099 - 3107
Hlavní autori: Cao, Bin, Gu, Yu, Lv, Zhihan, Yang, Shan, Zhao, Jianwei, Li, Yujie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Collision dynamics
Computational modeling
Controllability
Cooperative co-evolution
Evolutionary algorithms
grouping method
Internet of Things
Load modeling
Optimization
parameter adjustment
Particle swarm optimization
Protocols
Radio frequency identification
radio-frequency identification (RFID) reader anticollision
Radiofrequency identification
Random numbers
Resource management
Resource scheduling
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources to circumvent collision issues. In this article, an improved RFID reader anticollision model is constructed by modifying the measure index, introducing a constraint function, and simultaneously considering collisions among readers and between readers and tags. The dense deployment of large numbers of readers increases the number of variables to be encoded, resulting in a high-dimensional problem that cannot be effectively and efficiently solved by traditional algorithms. Accordingly, distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) is proposed. The inertia weight and learning factors are adjusted during evolution, and an improved grouping strategy is presented. Moreover, various combinations of random number generation functions are tested. For improved efficiency, DPCCPSO is implemented with distributed parallelism. Experimental verification shows that the proposed novel algorithm exhibits superior performance to existing state-of-the-art algorithms, particularly when numerous RFID readers are deployed.
Bibliografia:ObjectType-Article-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.3033473