Robust modeling and planning of radio-frequency identification network in logistics under uncertainties
To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-fr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718769781 |
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| _version_ | 1849698247426179072 |
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| author | Bowei Xu Junjun Li Yongsheng Yang Octavian Postolache Huafeng Wu |
| author_facet | Bowei Xu Junjun Li Yongsheng Yang Octavian Postolache Huafeng Wu |
| author_sort | Bowei Xu |
| collection | DOAJ |
| description | To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost. |
| format | Article |
| id | doaj-art-98227e13c60f42a2ae5fa02eee554e49 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-98227e13c60f42a2ae5fa02eee554e492025-08-20T03:18:56ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-04-011410.1177/1550147718769781Robust modeling and planning of radio-frequency identification network in logistics under uncertaintiesBowei Xu0Junjun Li1Yongsheng Yang2Octavian Postolache3Huafeng Wu4Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, P.R. ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, P.R. ChinaInstitute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, P.R. ChinaInstituto de Telecomunicacoes, ISCTE-IUL, Lisbon, PortugalMerchant Marine College, Shanghai Maritime University, Shanghai, P.R. ChinaTo realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.https://doi.org/10.1177/1550147718769781 |
| spellingShingle | Bowei Xu Junjun Li Yongsheng Yang Octavian Postolache Huafeng Wu Robust modeling and planning of radio-frequency identification network in logistics under uncertainties International Journal of Distributed Sensor Networks |
| title | Robust modeling and planning of radio-frequency identification network in logistics under uncertainties |
| title_full | Robust modeling and planning of radio-frequency identification network in logistics under uncertainties |
| title_fullStr | Robust modeling and planning of radio-frequency identification network in logistics under uncertainties |
| title_full_unstemmed | Robust modeling and planning of radio-frequency identification network in logistics under uncertainties |
| title_short | Robust modeling and planning of radio-frequency identification network in logistics under uncertainties |
| title_sort | robust modeling and planning of radio frequency identification network in logistics under uncertainties |
| url | https://doi.org/10.1177/1550147718769781 |
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