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Xu, B., Li, J., Yang, Y., Postolache, O. & Wu, H. (2018). Robust modeling and planning of radio-frequency identification network in logistics under uncertainties. International Journal of Distributed Sensor Networks. 14 (4), 1-11

B. Xu et al.,  "Robust modeling and planning of radio-frequency identification network in logistics under uncertainties", in Int. Journal of Distributed Sensor Networks, vol. 14, no. 4, pp. 1-11, 2018

@article{xu2018_1714894390631,
	author = "Xu, B. and Li, J. and Yang, Y. and Postolache, O. and Wu, H.",
	title = "Robust modeling and planning of radio-frequency identification network in logistics under uncertainties",
	journal = "International Journal of Distributed Sensor Networks",
	year = "2018",
	volume = "14",
	number = "4",
	doi = "10.1177/1550147718769781",
	pages = "1-11",
	url = "https://doi.org/10.1177%2F1550147718769781"
}

TY  - JOUR
TI  - Robust modeling and planning of radio-frequency identification network in logistics under uncertainties
T2  - International Journal of Distributed Sensor Networks
VL  - 14
IS  - 4
AU  - Xu, B.
AU  - Li, J.
AU  - Yang, Y.
AU  - Postolache, O.
AU  - Wu, H.
PY  - 2018
SP  - 1-11
SN  - 1550-1329
DO  - 10.1177/1550147718769781
UR  - https://doi.org/10.1177%2F1550147718769781
AB  - To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification networkin logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and arobust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage isestablished by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference iscalculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. Inrobust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forwardto improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploita-tion speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; theexploitation 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 ismore conducive to enhance the coverage rate and reduce interference and cost.
ER  -