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Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 26

Towards Energy-aware Cyber-Physical Systems Verification and Optimization

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DOI: http://dx.doi.org/10.15439/2021F125

Citation: Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 26, pages 205–210 ()

Full text

Abstract. The environment influences the system's behavior, and neglecting the environmental behavior has indirect negative impact on optimizing the system's behavior. In this work, to increase the system's flexibility, the behavior of the environment is modeled dynamically to apply the disorderliness of its behavior. The resulting models are formally verified.  By examining the past environmental behavior and predicting its future behavior, energy optimization is done more dynamically. The verification results acquired using a UPPAAL-SMC show that the optimization of system behavior by predicting the environmental behavior has been successful. Our approach is demonstrated using a case study within an I4 setting.

References

  1. U. Nations, “The 17 goals for sustainable development,” 2015. [Online]. Available: https://sdgs.un.org/goals
  2. J. Lygeros and M. Prandini, “Stochastic hybrid systems: A powerful framework for complex, large scale applications,” European Journal of Control, vol. 16, p. 583–594, 11 2010.
  3. E.-Y. Kang, D. Mu, and L. Huang, “Probabilistic verification of timing constraints in automotive systems using uppaal-smc,” in Integrated Formal Methods, ser. EuroSys ’10. Cham: Springer International Publishing, 2018, pp. 236–254.
  4. E.-Y. Kang, D. Mu, L. Huang, and Q. Lan, “Model-based analysis of timing and energy constraints in an autonomous vehicle system,” in 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2017, pp. 525–532.
  5. ——, “Verification and validation of a cyber-physical system in the automotive domain,” in 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2017, pp. 326–333.
  6. E.-Y. Kang, L. Huang, and D. Mu, “Formal verification of energy and timed requirements for a cooperative automotive system,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ser. SAC ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 1492–1499. [Online]. Available: https://doi.org/10.1145/3167132.3167291
  7. S. C. Jepsen, T. I. Mørk, J. Hviid, and T. Worm, “A pilot study of industry 4.0 asset interoperability challenges in an industry 4.0 laboratory,” in 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020, pp. 571–575.
  8. “Effimat automated warehouses.” [Online]. Available: https://effimat.com/
  9. “Enabled robotics.” [Online]. Available: https://www.enabled-robotics.com/
  10. “Collaborative robots universal robots.” [Online]. Available: https://www.universal-robots.com/
  11. A. David, K. Larsen, A. Legay, M. Mikučionis, and D. Poulsen, “Uppaal smc tutorial,” International Journal on Software Tools for Technology Transfer, vol. 17, 01 2015.
  12. D. Meike, M. Pellicciari, G. Berselli, A. Vergnano, and L. Ribickis, “Increasing the energy efficiency of multi-robot production lines in the automotive industry,” IEEE International Conference on Automation Science and Engineering, pp. 700–705, 2012.
  13. A. Vergnano, C. Thorstensson, B. Lennartson, P. Falkman, M. Pellicciari, F. Leali, and S. Biller, “Modeling and optimization of energy consumption in cooperative multi-robot systems,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 423–428, 2012.
  14. D. Meike, M. Pellicciari, and G. Berselli, “Energy efficient use of multirobot production lines in the automotive industry: Detailed system modeling and optimization,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 3, pp. 798–809, 2014.
  15. M. Pellicciari, G. Berselli, F. Leali, and A. Vergnano, “A method for reducing the energy consumption of pick-and-place industrial robots,” Mechatronics, vol. 23, no. 3, pp. 326–334, 2013. [Online]. Available: http://dx.doi.org/10.1016/j.mechatronics.2013.01.013
  16. A. Gamatié, G. Sassatelli, and M. Mikučionis, Modeling and Analysis for Energy-Driven Computing using Statistical Model-Checking; Design, Automation and Test in Europe Conference, Virtual, France., 02 2021.
  17. L. Huang and E.-Y. Kang, “Formal verification of safety & security related timing constraints for a cooperative automotive system,” in Fundamental Approaches to Software Engineering, R. Hähnle and W. van der Aalst, Eds. Cham: Springer International Publishing, 2019, pp. 210–227.
  18. A. Kuhnle and G. Lanza, “Application of reinforcement learning in production planning and control of cyber physical production systems,” in Machine Learning for Cyber Physical Systems, J. Beyerer, C. Kühnert, and O. Niggemann, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019, pp. 123–132.
  19. H. Li, “Entropy reduction via communications in cyber physical systems: How to feed maxwell’s demon?” in 2015 IEEE International Symposium on Information Theory (ISIT), 2015, pp. 2206–2210.