Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination
The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and s...
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| Published in | IEEE transactions on industrial informatics Vol. 8; no. 4; pp. 900 - 912 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.11.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1551-3203 1941-0050 |
| DOI | 10.1109/TII.2012.2205390 |
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| Abstract | The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms. |
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| AbstractList | The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms. |
| Author | Yue-Jiao Gong Jun Zhang Zhi-Hui Zhan Wei-Neng Chen Meie Shen Kaynak, O. |
| Author_xml | – sequence: 1 surname: Yue-Jiao Gong fullname: Yue-Jiao Gong email: gongyuejiao@gmail.com organization: Dept. of Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China – sequence: 2 surname: Meie Shen fullname: Meie Shen email: MeieShen@gmail.com organization: Dept. of Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China – sequence: 3 surname: Jun Zhang fullname: Jun Zhang email: junzhang@ieee.org organization: Dept. of Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China – sequence: 4 givenname: O. surname: Kaynak fullname: Kaynak, O. email: okyay.kaynak@boun.edu.tr organization: Dept. of Electr. & Electron. Eng., Bogazici Univ., Istanbul, Turkey – sequence: 5 surname: Wei-Neng Chen fullname: Wei-Neng Chen email: cwnrau1634@gmail.com organization: Sch. of Comput. Sci., Beijing Inf. Sci. & Technol. Univ., Beijing, China – sequence: 6 surname: Zhi-Hui Zhan fullname: Zhi-Hui Zhan email: zhanapollo@163.com organization: Dept. of Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China |
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| SubjectTerms | Algorithm design and analysis Algorithms Deletion Interference Mutations Networks Operators Optimization Particle swarm optimization Particle swarm optimization (PSO) Radio frequency identification radio frequency identification (RFID) Radiofrequency identification Readers redundant reader elimination RFID network planning (RNP) Studies |
| Title | Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination |
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