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An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II

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Recent Developments in Metaheuristics

Abstract

Radio frequency identification (RFID) is an automated data collection technology with the aim to facilitate data acquisition and storage without human intervention. RFID process depends on radio-frequency waves to transfer data between a reader and an electronic tag attached to an item, in order to identify objects or persons, which allows an automated traceability. The deployment of RFID readers is an important component in RFID system, and plays a key role in RFID Network Planning (RNP). Therefore, in order to optimize the deployment of RFID reader problem, we propose a new approach based on multi-level strategy using as main objectives the coverage, the number of deployed readers and the interference. In this way, Non-dominated Sorting Genetic algorithm II (NSGA-II) is adopted in order to minimize the total quantity of readers required to identify all tags in a given area. The proposed multi-level approach based on NSGA-II algorithm has a several attractive features which makes it ideal for our research and the simulation results show its effectiveness and performance.

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References

  1. J.M. Arroyo, F.J. Fernández, Application of a genetic algorithm to n-K power system security assessment. Int. J. Electr. Power Energy Syst. 49, 114–121 (2013). doi:10.1016/j.ijepes.2012.12.011

    Article  Google Scholar 

  2. O. Botero, H. Chaouchi, RFID network topology design based on Genetic Algorithms, in 2011 IEEE International Conference on RFID-Technologies and Applications (RFID-TA) (2011), pp. 300–305

    Google Scholar 

  3. T. Brodmeier, E. Pretsch, Application of genetic algorithms in molecular modeling. J. Comput. Chem. 15, 588–595 (1994). doi:10.1002/jcc.540150604

    Article  Google Scholar 

  4. H. Chen, Y. Zhu, K. Hu, Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning. Appl. Soft Comput. 10, 539–547 (2010). doi:10.1016/j.asoc.2009.08.023

    Article  Google Scholar 

  5. H. Chen, Y. Zhu, K. Hu, T. Ku, RFID network planning using a multi-swarm optimizer. J. Netw. Comput. Appl. 34, 888–901 (2011). doi:10.1016/j.jnca.2010.04.004

    Article  Google Scholar 

  6. H. Chen, Y. Zhu, L. Ma, B. Niu, Multiobjective RFID network optimization using multiobjective evolutionary and swarm intelligence approaches. Math. Probl. Eng. 2014, e961412 (2014). doi:10.1155/2014/961412

    Google Scholar 

  7. K. Deb, D. Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms (Wiley, New York, NY, 2001)

    Google Scholar 

  8. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002). doi:10.1109/4235.996017

    Article  Google Scholar 

  9. Y.-J. Gong, M. Shen, J. Zhang, et al., Optimizing RFID network planning by using a particle swarm optimization algorithm with redundant reader elimination. IEEE Trans. Ind. Inf. 8, 900–912 (2012). doi:10.1109/TII.2012.2205390

    Article  Google Scholar 

  10. C.-C. Hsu, P.-C. Yuan, The design and implementation of an intelligent deployment system for RFID readers. Expert Syst. Appl. 38, 10506–10517 (2011). doi:10.1016/j.eswa.2011.02.109

    Article  Google Scholar 

  11. S. Lu, S. Yu, A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm. J. Netw. Comput. Appl. 39, 280–291 (2014). doi:10.1016/j.jnca.2013.07.015

    Article  Google Scholar 

  12. L. Ma, H. Chen, K. Hu, et al., Hierarchical artificial bee colony algorithm for RFID network planning optimization, hierarchical artificial bee colony algorithm for RFID network planning optimization. Sci. World J. 2014, e941532 (2014). doi:10.1155/2014/941532

    Google Scholar 

  13. L. Ma, K. Hu, Y. Zhu, H. Chen, Cooperative artificial bee colony algorithm for multi-objective RFID network planning. J. Netw. Comput. Appl. 42, 143–162 (2014). doi:10.1016/j.jnca.2014.02.012

    Article  Google Scholar 

  14. A.P. McCabe, Constrained optimization of the shape of a wave energy collector by genetic algorithm. Renew. Energy 51, 274–284 (2013). doi:10.1016/j.renene.2012.09.054

    Article  Google Scholar 

  15. S. Wang, M. Liu, A genetic algorithm for two-stage no-wait hybrid flow shop scheduling problem. Comput. Oper. Res. 40, 1064–1075 (2013). doi:10.1016/j.cor.2012.10.015

    Article  Google Scholar 

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Correspondence to Abdelkader Raghib .

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Raghib, A., Majd, B.A.E., Aghezzaf, B. (2018). An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II. In: Amodeo, L., Talbi, EG., Yalaoui, F. (eds) Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-319-58253-5_27

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