期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 5, 页码 3099-3107出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3033473
关键词
Radiofrequency identification; Load modeling; Internet of Things; Particle swarm optimization; Computational modeling; Optimization; Protocols; Cooperative co-evolution; grouping method; parameter adjustment; radio-frequency identification (RFID) reader anticollision
资金
- National Natural Science Foundation of China [61976242, 61876059, 61902203]
- Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University [2018002]
- Ministry of Science and Technology of the People's Republic of China [2017YFB1400100]
- Key Research and Development Plan-Major Scientific and Technological Innovation Projects of Shandong Province [2019JZZY020101]
An improved RFID reader anticollision model is proposed in this article, utilizing intelligent computing technologies and distributed parallel cooperative co-evolution particle swarm optimization to address the high-dimensional problem of dense deployment of large numbers of readers.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据