Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the Cyber-Physical System, privacy and security issues turned out to be the most significant factor all over the world. This paper demonstrates a complete co-design approach meant for integrating the cyberspace and physical space of a cyber-physical system. Various strategies and models regarding cyber and physical space are established in CPS. Apart from numerous co-design strategies, there are several parameters yet to be resolved and established. It becomes complicated and tricky to examine and explore the extremely best value since these parameters make up a very huge space. Therefore, a metaheuristic algorithm such as improved Fuzzy Harmonic Search Algorithm is proposed to optimize the control parameters so as to obtain a feasible solution. Also, this approach minimizes the cost function using Maximum Allowable Delay Bound (MADB) when subjected to several constraints such as the Sampling period, Horizon length, Routing graph, and scheduling table. Moreover the comparative analyses of various approaches such as Fuzzy Harmony Search (FHS) algorithm, Harmony Search (HS) algorithm, Grey Wolf Optimization Algorithm (GWO), Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm as well as Fuzzy Genetic Algorithm (Fuzzy GA) are evaluated to examine the performances of the proposed approach. A testbed is organized for evaluation and exploration in a manufacturing atmosphere. The result reveals that this proposed approach provides enhanced control performance and communication reliability even under very harsh environmental habitat.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abid, M., Khan, A. Q., & Rehan, M. (2014). TS fuzzy approach for fault detection in nonlinear cyber physical systems. In: Computational intelligence for decision support in cyber-physical systems (pp. 421–447). Singapore: Springer.

  • Al-Janabi, S., & Al-Shourbaji, I. (2016). A study of cyber security awareness in educational environment in the middle east. Journal of Information and Knowledge Management, 15(01), 1650007.

    Article  Google Scholar 

  • Ashibani, Y., & Mahmoud, Q. H. (2017). Cyber physical systems security: analysis, challenges and solutions. Computers and Security, 68, 81–97.

    Article  Google Scholar 

  • Assad, A., & Deep, K. (2016). Applications of harmony search algorithm in data mining: a survey. In: Proceedings of fifth international conference on soft computing for problem solving (pp. 863–874). Singapore: Springer.

  • Brings, J., Daun, M., Bandyszak, T., Stricker, V., Weyer, T., Mirzaei, E., et al. (2019). Model-based documentation of dynamicity constraints for collaborative cyber-physical system architectures: findings from an industrial case study. Journal of Systems Architecture, 97, 153–167.

    Article  Google Scholar 

  • Chang, W., Zhang, L., Roy, D., & Chakraborty, S. (2017). Control/Architecture co-design for cyber-physical systems.

  • Chang, Y. H., Hu, Q., & Tomlin, C. J. (2018). Secure estimation based Kalman filter for cyber–physical systems against sensor attacks. Automatica, 95, 399–412.

    Article  Google Scholar 

  • Chen, H., Wu, J., Jiang, B., & Chen, W. (2020). A modified neighborhood preserving embedding-based incipient fault detection with applications to small-scale cyber–physical systems. ISA Transactions, 104, 175–183.

    Article  Google Scholar 

  • Cheng, S. T., & Chang, T. Y. (2012). A cyber physical system model using genetic algorithm for actuators control. In: Proceedings of the 2012 2nd international conference on consumer electronics, communications and networks (CECNet) (pp. 2269–2272). IEEE.

  • Ding, D., Han, Q. L., Xiang, Y., Ge, X., & Zhang, X. M. (2018). A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing, 275, 1674–1683.

    Article  Google Scholar 

  • Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. SIMULATION, 76(2), 60–68.

    Article  Google Scholar 

  • Giuseppi, A., Tortorelli, A., Germanà, R., Liberati, F., & Fiaschetti, A. (2019). Securing cyber-physical systems: an optimization framework based on OSSTMM and genetic algorithms. In: Proceedings of the 2019 27th mediterranean conference on control and automation (MED) (pp. 50–56). IEEE.

  • Han, S., Zhu, X., Mok, A. K., Chen, D., & Nixon, M. (2011). Reliable and real-time communication in industrial wireless mesh networks. In: Proceedings of the 2011 17th IEEE real-time and embedded technology and applications symposium (pp. 3–12). IEEE.

  • Huang, G., Chen, J., & Khojasteh, Y. (2020). A cyber-physical system deployment based on pull strategies for one-of-a-kind production with limited resources. Journal of Intelligent Manufacturing 1–18.

  • Huang, L., Liang, Y., Huang, F., & Wang, D. (2018). A quantitative analysis model of grid cyber physical systems. Global Energy Interconnection, 1(5), 618–626.

    Google Scholar 

  • Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. In: Proceedings of the 2014 IEEE international conference on automation, quality and testing, robotics (pp. 1–4). IEEE.

  • Kajati, E., Papcun, P., Liu, C., Zhong, R. Y., Koziorek, J., & Zolotova, I. (2019). Cloud based cyber-physical systems: network evaluation study. Advanced Engineering Informatics, 42, 100988.

    Article  Google Scholar 

  • Khalid, A., Kirisci, P., Khan, Z. H., Ghrairi, Z., Thoben, K. D., & Pannek, J. (2018). Security framework for industrial collaborative robotic cyber-physical systems. Computers in Industry, 97, 132–145.

    Article  Google Scholar 

  • Li, B., Ma, Y., Westenbroek, T., Wu, C., Gonzalez, H., & Lu, C. (2016). Wireless routing and control: a cyber-physical case study. In: Proceedings of the 2016 ACM/IEEE 7th international conference on cyber-physical systems (ICCPS) (pp. 1–10). IEEE.

  • Lins, R. G., de Araujo, P. R. M., & Corazzim, M. (2020). In-process machine vision monitoring of tool wear for cyber-physical production systems. Robotics and Computer-Integrated Manufacturing, 61, 101859.

    Article  Google Scholar 

  • Lu, F., Bi, H., Huang, M., & Duan, S. (2017). Simulated annealing genetic algorithm based schedule risk management of IT outsourcing project. Mathematical Problems in Engineering.

  • Lu, F. Q., Huang, M., Ching, W. K., & Siu, T. K. (2013). Credit portfolio management using two-level particle swarm optimization. Information Sciences, 237, 162–175.

    Article  Google Scholar 

  • Lu, F., Zhu, W., Bi, H., Huang, M., Chen, W., & Zhao, Y. (2019). Two-level Tabu-predatory search for schedule risk control of IT outsourcing projects. Information Sciences, 487, 57–76.

    Article  Google Scholar 

  • Lun, Y. Z., D’Innocenzo, A., Smarra, F., Malavolta, I., & Di Benedetto, M. D. (2019). State of the art of cyber-physical systems security: an automatic control perspective. Journal of Systems and Software, 149, 174–216.

    Article  Google Scholar 

  • Lv, W., Xiong, J., Shi, J., Huang, Y., & Qin, S. (2020). A deep convolution generative adversarial networks based fuzzing framework for industry control protocols. Journal of Intelligent Manufacturing, 32, 1–17.

    Google Scholar 

  • Padmajothi, V., & Iqbal, J. M. (2020). Adaptive neural fuzzy inference system-based scheduler for cyber–physical system. Soft Computing, 24, 17309–17318.

    Article  Google Scholar 

  • Rejeesh, M. R. (2019). Interest point based face recognition using adaptive neuro fuzzy inference system. Multimedia Tools and Applications, 78(16), 22691–22710.

    Article  Google Scholar 

  • Roy, D., Zhang, L., Chang, W., Mitter, S. K., & Chakraborty, S. (2017). Semantics-preserving cosynthesis of cyber-physical systems. Proceedings of the IEEE, 106(1), 171–200.

    Article  Google Scholar 

  • Schranz, M., Elmenreich, W., & Rappaport, M. (2018). Designing cyber-physical systems with evolutionary algorithms. In: Cyber-physical laboratories in engineering and science education (pp. 111–135). Cham: Springer.

  • Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering System, 9(3), 117–126.

    Article  Google Scholar 

  • Sundararaj, V. (2019a). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325–345.

    Article  Google Scholar 

  • Sundararaj, V. (2019b). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.

    Article  Google Scholar 

  • Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., Rejeesh, M. R., & Sundararaj, R. (2020). CCGPA-MPPT: cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 28(11), 1128–1145.

    Article  Google Scholar 

  • Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering, 5(4), 653–661.

    Article  Google Scholar 

  • Wang, L., & Li, L. P. (2013). An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. International Journal of Electrical Power and Energy Systems, 44(1), 832–843.

    Article  Google Scholar 

  • Yang, X. S. (2009). Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm (pp. 1–14). Berlin, Heidelberg: Springer.

  • Zhou, J., Zhou, Y., Wang, B., & Zang, J. (2019). Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering, 5(4), 624–636.

    Article  Google Scholar 

  • Zhu, T., Xiong, P., Li, G., Zhou, W., & Philip, S. Y. (2020). Differentially private model publishing in cyber physical systems. Future Generation Computer Systems, 108, 1297–1306.

    Article  Google Scholar 

Download references

Acknowledgment

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no (RG-1440-026).

Author information

Authors and Affiliations

  1. Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia

    Mustufa Haider Abidi, Hisham Alkhalefah & Usama Umer

Authors
  1. Mustufa Haider Abidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hisham Alkhalefah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Usama Umer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustufa Haider Abidi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abidi, M.H., Alkhalefah, H. & Umer, U. Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0. J Intell Manuf 33, 1795–1812 (2022). https://doi.org/10.1007/s10845-021-01757-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-021-01757-4

Keywords

Search

Navigation