An effective approach in robustness optimization for solving the RFID network planning problem with uncertainty

: pp. 70–83
Received: June 27, 2023
Revised: December 14, 2023
Accepted: December 16, 2023

Ait Lhadj Lamin S., Raghib A., Abou El Majd B. An effective approach in robustness optimization for solving the RFID network planning problem with uncertainty. Mathematical Modeling and Computing. Vol. 11, No. 1, pp. 70–83 (2024)

LMSA Lab, FSR, Mohammed V University in Rabat
LMSA Lab, FSR, Mohammed V University in Rabat
LMSA Lab, FSR, Mohammed V University in Rabat; University of Lille, 59655 Villeneuve–d'Ascq, France

RFID technology enables remote storage and retrieval of data on RFID tags, making it a versatile and efficient tool with widespread applications in various industries.  This paper presents a solution to the challenge of deploying RFID readers, which has been a persistent problem in the RFID technology practical and theoretical communities.  To address the deployment problem, the paper proposes a robust multi-objective approach that optimizes many requested objectives as: coverage, the number of deployed readers, and interference while taking into account uncontrollable parameters in the system.  The simulation results demonstrate the robustness of the approach in solving the deployment problem and optimizing the RFID system under varying and unpredictable conditions.  The proposed approach has the potential to contribute to the RFID technology industry and enable more efficient and effective RFID systems across different sectors.

  1. Raghib A., Abou El Majd B.  Multi-objective decision aid application for RFID network planning.  MATEC Web of Conferences.  200, 00017 (2018).
  2. Raghib A., Abou El Majd B.  Hierarchical multiobjective approach for optimising RFID reader deployment.  International Journal of Mathematical Modelling and Numerical Optimisation.  9 (1), 70–88 (2019).
  3. Raghib A., Abou El Majd B., Aghezzaf B.  An optimal deployment of readers for RFID network planning using NSGA-II.  Recent Developments in Metaheuristics.  Operations Research/Computer Science Interfaces Series.  62, 463–476 (2018).
  4. Ait Lhadj Lamin S., Raghib A., Abou El Majd B.  Robust multi-objective optimization for solving the RFID network planning problem.  Mathematical Modeling and Computing.  8 (4), 616–626 (2021).
  5. Ait Lhadj Lamin S., Raghib A., Abou El Majd B.  Deployment of RFID Readers Using a Robustness Multi-objective Approach.  2021 Third International Conference on Transportation and Smart Technologies (TST), Tangier, Morocco.  90–95 (2021).
  6. Maimouni M., Abou El Majd B., Bouya M.  RFID network planning using a new hybrid ANNs-based approach.  Connection Science.  34 (1), 2265–2290 (2022).
  7. Guan Q., Liu Y., Yang Y. P., Yu W. S.  Genetic Approach for Network Planning in the RFID Systems.  Sixth International Conference on Intelligent Systems Design and Applications, Jian, China.  2, 567–572 (2006).
  8. Chen H., Zhu Y., Ma L., Niu B.  Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches.  Mathematical Problems in Engineering.  2014, e961412 (2014).
  9. Chen H. N., Zhu Y. L.  RFID Networks Planning Using Evolutionary Algorithms and Swarm Intelligence.  2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China.  1–4 (2008).
  10. Chen H., Zhu Y., Hu K.  Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning.  Applied Soft Computing.  10 (2), 539–547 (2010).
  11. Chen H., Zhu Y., Hu K.  RFID networks planning using a multi-swarm optimizer.  2009 Chinese Control and Decision Conference, Guilin, China.  3548–3552 (2009).
  12. Chen H., Zhu Y., Hu K., Ku T.  Dynamic RFID Network Optimization Using a Self-adaptive Bacterial Foraging Algorithm.  International Journal of Artificial Intelligence.  7 (A11), 219–231 (2011).
  13. Ma L., Chen H., Hu K., Zhu Y.  Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization.  The Scientific World Journal.  2014, 941532 (2014).
  14. Oscar B., Chaouch H.  RFID network topology design based on Genetic Algorithms.  2011 IEEE International Conference on RFID-Technologies and Applications, Sitges, Spain.  300–305 (2011).
  15. Gong Y.-J., Shen M., Zhang J., Kaynak O., Chen W.-N., Zhan Z.-H.  Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination.  IEEE Transactions on Industrial Informatics.  8 (4), 900–912 (2012).
  16. Tuba M., Bacanin N., Alihodzic A.  Firefly algorithm for multi-objective RFID network planning problem.  2014 22nd Telecommunications Forum Telfor (TELFOR), Belgrade, Serbia.  95–98 (2014).
  17. Bacanin N., Tuba M., Jovanovic R.  Hierarchical multiobjective RFID network planning using firefly algorithm.  2015 International Conference on Information and Communication Technology Research (ICTRC), Abu Dhabi, United Arab Emirates.  282–285 (2015).
  18. Tuba M., Bacanin N., Beko M.  Fireworks algorithm for RFID network planning problem.  2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA), Pardubice, Czech Republic. 440–444 (2015).
  19. Tuba M., Bacanin N., Beko M.  Multiobjective RFID Network Planning by Artificial Bee Colony Algorithm with Genetic Operators.  ICSI 2015: Advances in Swarm and Computational Intelligence.  247–254 (2015).
  20. Raghib A., Abou El Majd B., Ouchetto O., Aghezzaf B.  Robustness optimization for solving the deployment of RFID readers problem.  2016 5th International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco.  509–513 (2016).
  21. Zhao C., Wu C., Chai J., Wang X., Yang X., Lee J.-M., Kim M. J.  Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty.  Applied Soft Computing.  55, 549–564 (2017).
  22. Tuba V., Alihodzic A., Tuba M.  Multi-objective RFID network planning with probabilistic coverage model by guided fireworks algorithm.  2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania.  882–887 (2017).
  23. Tang L., Cao H., Zheng L., Huang N.  Uncertainty-aware RFID network planning for target detection and target location.  Journal of Network and Computer Applications.  74, 21–30 (2016).
  24. Tang L., Cao H., Zheng L., Huang N.  RFID network planning for wireless manufacturing considering the detection uncertainty.  IFAC-PapersOnLine.  48 (3), 406–411 (2015).
  25. Kumar V. V., Chan F. T.  A superiority search and optimisation algorithm to solve RFID and an environmental factor embedded closed loop logistics model.  International Journal of Production Research.  49 (16), 4807–4831 (2011).
  26. Deb K.  Multi-Objective Optimization Using Evolutionary Algorithms.  New York, John Wiley & Sons (2001).
  27. El Mrabet Z., Ezzari M., Elghazi H., El Majd B. A.  Deep learning-based intrusion detection system for advanced metering infrastructure.  NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security.  58 (2019).
  28. Amodeo L., Talbi E. G., Yalaoui F.  Recent Developments in Metaheuristics.  Springer, Cham (2018).
  29. Beyer H.-G., Sendhoff B.  Robust optimization – A comprehensive survey.  Computer Methods in Applied Mechanics and Engineering.  196 (33–34), 3190–3218 (2007).
  30. Deb K., Jain H.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints.  IEEE Transactions on Evolutionary Computation.  18 (4), 577–601 (2014).