RFID network planning using a new hybrid ANNs-based approach

References

• Altaf, M. S., Bouferguene, A., Liu, H., Al-Hussein, M., & Yu, H. (2018). Integrated production planning and control system for a panelized home prefabrication facility using simulation and RFID. Automation in Construction, 85(2018), 369–383. https://doi.org/10.1016/j.autcon.2017.09.009.
   Google Scholar
• Azizi, A. (2017). Introducing a novel hybrid artificial intelligence algorithm to optimize network of industrial applications in modern manufacturing. Complexity, 2017, 1–18. https://doi.org/10.1155/2017/8728209.
   Google Scholar
• Azizi, A. (2019). Applications of artificial intelligence techniques in industry 4.0 (Springer Briefs in Applied Sciences and Technology, pp. 27–47). Singapore: Springer. https://doi:10.1007/978-981-13-2640-0.
   Google Scholar
• Azizi, A., Vatankhah Barenji, A., & Hashmipour, M. (2016). Optimizing radio frequency identification network planning through ring probabilistic logic neurons. Advances in Mechanical Engineering, 8(8), 1687814016663476. https://doi.org/10.1177/1687814016663476
   Google Scholar
• Elbasani, E., Siriporn, P., & Choi, J. S. (2020). A survey on RFID in industry 4.0. In Kanagachidambaresan G., Anand R., Balasubramanian E., & Mahima V. (Eds.), Internet of things for industry 4.0 (EAI/Springer Innovations in Communication and Computing). Cham: Springer.
   Google Scholar
• Figueiredo e Silva, P., Kaseva, V., & Lohan, E. S. (2018). Wireless positioning in IOT: A look at current and future trends. Sensors, 18(8), 2470. https://doi.org/10.3390/s18082470
   PubMed Web of Science ®Google Scholar
• Franek, O. (2017). Phasor alternatives to Friis' transmission equation. IEEE Antennas and Wireless Propagation Letters, 17(1), 90–93. https://doi.org/10.1109/LAWP.2017.2776523
   Web of Science ®Google Scholar
• Gong, Y.-J., Shen, M., Zhang, J., Kaynak, O., Chen, W.-N., & Zhan, Z.-H. (2012). Optimizing RFID network planning by using a particle swarm optimization algorithm with redundant reader elimination. IEEE Transactions on Industrial Informatics, 8(4), 900–912. https://doi.org/10.1109/TII.2012.2205390
   Web of Science ®Google Scholar
• Haibi, A., Oufaska, K., El Yassini, K., Boulmalf, M., & Bouya, M. (2022). Systematic mapping study on RFID technology. IEEE Access, 10, 6363–6380. https://doi.org/10.1109/ACCESS.2022.3140475.
   Web of Science ®Google Scholar
• Ke, W.-C., Liu, B.-H., & Tsai, M.-J. (2007). Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete. IEEE Transactions on Computers, 56(5), 710–715. https://doi.org/10.1109/TC.2007.1019
   Web of Science ®Google Scholar
• Ma, L., Cheng, S., & Shi, Y. (2020). Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(11), 6723–6742. https://doi.org/10.1109/TSMC.2020.2963943
   Web of Science ®Google Scholar
• Ma, L., Hu, K., Zhu, Y., & Chen, H. (2014). Cooperative artificial bee colony algorithm for multi-objective RFID network planning. Journal of Network and Computer Applications, 42, 143–162. https://doi.org/10.1016/j.jnca.2014.02.012.
   Web of Science ®Google Scholar
• Ma, L., Wang, X., Huang, M., Lin, Z., Tian, L., & Chen, H. (2017). Two-level master–slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(5), 861–880. https://doi.org/10.1109/TSMC.6221021
   Web of Science ®Google Scholar
• Maimouni, M., & Abou El Majd, B. (2021). New hybrid approach based on artificial neural networks for RFID network planning with random redundant antenna elimination. 2021 Third International Conference on Transportation and Smart Technologies (TST), Tangier, Morocco. https://doi.org/10.1109/TST52996.2021.00011.
   Google Scholar
• Maimouni, M., Abou El Majd, B., & Bouya, M. (2022). Solving the RFID network planning problem under the perturbation effect defined by a new probabilistic power-based model. In 2022 Microwave mediterranean symposium (MMS) (pp. 1–6).
   Google Scholar
• Mei, Z., Guo, Z., Chen, L., Wang, H., & Gao, Y. (2020). Genetic algorithm-based integrated optimization of active control systems for civil structures subjected to random seismic excitations. Engineering Optimization, 52(10), 1700–1719. https://doi.org/10.1080/0305215X.2019.1677632
   Web of Science ®Google Scholar
• Osei-kwakye, J., Han, F., Amponsah, A. A., Ling, Q., & Abeo, T. A. (2022). A hybrid optimization method by incorporating adaptive response strategy for feedforward neural network. Connection Science, 34(1), 578–607. https://doi.org/10.1080/09540091.2021.2025339
   Web of Science ®Google Scholar
• Raghib, A., Abou El Majd, B., & 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). Cham: Springer. https://doi.org/10.1007/978-3-319-58253-5_27.
   Google Scholar
• Raghib, A., Abou El Majd, B., Ouchetto, O., & Aghezzaf, B. (2016). Robustness optimization for solving the deployment of RFID readers problem. 2016 5th international conference on multimedia computing and systems (ICMCS), Marrakech, Morocco. https://doi.org/10.1109/ICMCS.2016.7905627.
   Google Scholar
• Raghib, A., & Majd, B. A. E. (2019). Hierarchical multiobjective approach for optimising RFID reader deployment. International Journal of Mathematical Modelling and Numerical Optimisation, 9(1), 70–88. https://doi.org/10.1504/IJMMNO.2019.096918
   Google Scholar
• Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71(October 2018), 747–782. https://doi.org/10.1016/j.asoc.2018.07.039.
   Google Scholar
• Shang, X., Chao, T., Ma, P., & Yang, M. (2019). An efficient local search-based genetic algorithm for constructing optimal Latin hypercube design. Engineering Optimization, 52(2), 271–287. https://doi.org/10.1080/0305215X.2019.1584618.
   Web of Science ®Google Scholar
• Too, J., & Rahim Abdullah, A. (2020). Binary atom search optimisation approaches for feature selection. Connection Science, 32(4), 406–430. https://doi.org/10.1080/09540091.2020.1741515
   Web of Science ®Google Scholar
• Xiao, L., Xie, S., Han, D., Liang, W., Guo, J., & Chou, W.-K. (2021). A lightweight authentication scheme for telecare medical information system. Connection Science, 33(3), 769–785. https://doi.org/10.1080/09540091.2021.1889976
   Web of Science ®Google Scholar
• Yang, Y., Wu, Y., Xia, M., & Qin, Z. (2009). A RFID network planning method based on genetic algorithm. 2009 international conference on networks security, wireless communications and trusted computing, Wuhan, China. https://doi.org/10.1109/NSWCTC.2009.238.
   Google Scholar
• Zhang, J., & Tai, Y. (2022). Secure medical digital twin via human-centric interaction and cyber vulnerability resilience. Connection Science, 34(1), 895–910. https://doi.org/10.1080/09540091.2021.2013443
   Web of Science ®Google Scholar
• Zhang, Z., Zhang, J., Wu, L., & Song, H. (2019). An improved approach for rfid network planning: Introduction of directional antenna reader. 2019 IEEE international conference on smart manufacturing, industrial & logistics engineering (SMILE), Hangzhou, China. https://doi.org/10.1109/SMILE45626.2019.8965313.
   Google Scholar