Skip to main content

Towards Intelligent Optimization of Design Strategies of Cyber-Physical Systems: Measuring Efficacy Through Evolutionary Computations

  • Chapter
  • First Online:
Computational Intelligence in Emerging Technologies for Engineering Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 872))

Abstract

Designing of effective cyber-physical system (CPS) encompassing different vertical applications solicits different components of design. Most of the components are uncertain and dynamic in nature. They either could be in the form of hardware sensors, optimization process and their scheduling nature. In this chapter, we investigate various levels of CPS formulation driven by machine learning and evolutionary algorithms with their strategic similarities. We argue that how far intelligent optimization in the level designing a CPS should be viable? Thus, suitability of appropriate evolutionary and machine learning algorithms is discussed in the context of different design uncertainty of CPS. The efficacy of auto-adaptive or self-organization principle is also discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://data.montgomerycountymd.gov/api/views/4mse-ku6q/rows.csv?accessType=DOWNLOAD, https://data.montgomerycountymd.gov/Public-Safety/Traffic-Violations/4mse-ku6q.

References

  1. Ning, H.S., Liu, H., Ma, J.H., Yang, L.T., Huang, R.H.: Cyber-physical-social-thinking hyperspace based science and technology. Futur. Gener. Comput. Syst. 56, 504–522 (2016)

    Article  Google Scholar 

  2. Botta, A., De Donato, W., Persico, V., Pescap, A.: Integration of cloud computing and Internet of things: a survey. Futur. Gener. Comput. Syst. 56, 684–700 (2016)

    Article  Google Scholar 

  3. Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F., Xu, B.: An IoT-oriented data storage framework in cloud computing platform. IEEE Trans. Ind. Inf. 10(2), 1443–1451 (2014)

    Article  Google Scholar 

  4. Tang, B., He, H., Ding, Q., Kay, S.: A parametric classification rule based on the exponentially embedded family. IEEE Trans. Neural Netw. Learn. Syst. 26(2), 367–377 (2015)

    Article  MathSciNet  Google Scholar 

  5. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2013)

    MATH  Google Scholar 

  6. Valero-Mas, J.J., Calvo-Zaragoza, J., Rico-Juan, J.R.: On the suitability of prototype selection methods for kNN classification with distributed data. Neurocomputing 203, 150–160 (2016)

    Article  Google Scholar 

  7. Simula Research Laboratory: Understanding uncertainty in cyber-physical systems: a conceptual model. Technical Report 2015-3 Feb (2016)

    Google Scholar 

  8. Wang, G.G., Cai, X., Cui, Z., Min, G., Chen, J.: High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans. Emerg. Top. Comput. (2017) in press. https://doi.org/10.1109/TETC.2017.2703784

  9. Andersson, J., de Lemos, R., Malek, S., Weyns, D.: Modeling dimensions of self-adaptive software systems. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-adaptive Systems. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, Dagstuhl, Germany, pp. 27–47 (2009). https://doi.org/10.1007/978-3-642-02161-9_2

    Chapter  Google Scholar 

  10. Banerjee, S., Qaheri, H., Bhatt, C.: Handling uncertainty in IoT design: an approach of statistical machine learning with distributed second-order optimization. In: Healthcare Data Analytics and Management Advances in Ubiquitous Sensing Applications for Healthcare, pp. 227–243. Elsevier BV, Amsterdam (2019)

    Google Scholar 

  11. Gerostathopoulos, I., Bures,T., Hnetynka, P., Hujecek, A., Plasil, F., Skoda, D.: Strengthening adaptation in cyber-physical systems via meta-adaptation strategies. ACM Trans. Cyber-Phys. Syst. 1(3), 13 (2017)

    Article  Google Scholar 

  12. Ciccozzi, F., Spalazzese, R.: MDE4IoT: Supporting the Internet of things with model-driven engineering. In: International Symposium on Intelligent and Distributed Computing, pp. 67–76 (2016)

    Google Scholar 

  13. Guerriero, M., Tajfar, S., Tamburri, D.A., Di Nitto, E.: Towards a model-driven design tool for big data architectures. In: ACM Proceedings of the 2nd International Workshop on BIG Data Software Engineering, pp. 37–43 (2016)

    Google Scholar 

  14. Pal, R., Prasanna, V.: The STREAM mechanism for CPS security the case for the smart grid. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(4), 537–550 (2017)

    Article  Google Scholar 

  15. Khan, A.H., Khan, Z.H., Khan, S.H.: Optimized reconfigurable autopilot design for an aerospace CPS. In: Khan, Z.H., Shawkat Ali Zahid Riaz, A.B.M. (eds.) Computational Intelligence for Decision Support in Cyber-Physical Systems, vol. 540. Springer, Heidelberg (2014)

    Google Scholar 

  16. Olteanu, S.C., et al.: Fuel cell diagnosis using Takagi-Sugeno observer approach. In: International Conference on Renewable Energy for Developing Countries (REDEC), pp. 1–7 (2012)

    Google Scholar 

  17. Neuman, C.: Challenges in security for cyber-physical systems. In: DHS Workshop on Future Directions in Cyber-Physical Systems Security (2009)

    Google Scholar 

  18. Pasqualetti, F., Dorfler, F., Bullo, F.: Attack detection and identification in cyber-physical systems –part I: models and fundamental limitations (2012). arXiv preprint arXiv:1202.6144

    Google Scholar 

  19. Diaz, J., Bielza, C., Ocana, J.L., Larranaga, P.: Development of a cyber-physical system based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control. In: Niggemann, O., Beyerer, J. (eds.) Machine Learning for Cyber Physical Systems. Selected Papers from the International Conference ML4CPS (2015)

    Google Scholar 

  20. Bockenkamp, A., Weichert, F., Stenzel, J., Lunsch, D.: Towards autonomously navigating and cooperating vehicles in cyber-physical production systems. In: Niggemann, O., Beyerer, J. (eds.) Machine Learning for Cyber Physical Systems. Selected Papers from the International Conference ML4CPS (2015)

    Google Scholar 

  21. Walther, C., Beneke, F., Merbach, L., Siebald, H., Hensel, O., Huster, J.: Machine-specific approach for automatic classification of cutting process efficiency. In: Niggemann, O., Beyerer, J. (eds.) Machine Learning for Cyber Physical Systems. Selected Papers from the International Conference ML4CPS (2015)

    Google Scholar 

  22. Walther, C.: Multikriteriell evolutionär optimierte Anpassung von unscharfen Modellen zur Klassifikation und Vorhersage auf der Basis hirnelektrischer Narkose-Potentiale. Shaker Verlag, Aachen (2012)

    MATH  Google Scholar 

  23. Abbasi, Z., Jonas, M., Banerjee, A., Gupta, S., Varsamopoulos, G.: Evolutionary green computing solutions for distributed cyber physical systems. In: Evolutionary Based Solutions for Green Computing. Studies in Computational Intelligence, vol. 432, pp. 1–28. Springer, Berlin (2013)

    Google Scholar 

  24. Pop, P., Raagaard, M.L., Craciunas, S.S., Steiner, W.: Design optimisation of cyber-physical distributed systems using IEEE time-sensitive networks. IET Cyber-Phys. Syst. Theory Appl. 1(1), 86–94 (2016)

    Article  Google Scholar 

  25. Michalewicz, Z.: Quo vadis, evolutionary computation? In: IEEE World Congress on Computational Intelligence, pp. 98–121. Springer, Berlin/Heidelberg (2012)

    Google Scholar 

  26. Hofmeyer, H., Davila Delgado, J.M.: Coevolutionary and genetic algorithm based building spatial and structural design. Artif. Intell. Eng. Des. Anal. Manuf. 29, 351–370 (2015)

    Article  Google Scholar 

  27. Van der Vegte, W.F., Vroom, R.W.: Considering cognitive aspects in designing cyber-physical systems: an emerging need for transdisciplinarity. In: Proceedings of the International Workshop on the Future of Transdisciplinary Design TFTD, vol. 13, pp. 41–52 (2013)

    Google Scholar 

  28. Ray, A.: Autonomous perception and decision-making in cyber-physical systems. In: 2013 8th International Conference on Computer Science & Education, pp. 1–10. IEEE, Piscataway (2013)

    Google Scholar 

  29. Luo, S.: An improved intelligent ant colony algorithm for the reliability optimization problem in cyber-physical systems. J. Softw. 9(1), 20–25 (2014)

    Article  Google Scholar 

  30. Huang, C.,Wang, D., Chawla, N.: Scalable uncertainty-aware truth discovery in big data social sensing applications for cyber-physical systems. IEEE Trans. Big Data PP(99), 1–1 (2017). https://doi.org/10.1109/TBDATA.2017.2669308

  31. Khazaeni, Y., Cassandras, C.G.: Event-driven Trajectory optimization for data harvesting in multiagent systems. IEEE Trans. Control Netw. Syst. 5(3), 1335–1348 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  32. Taylor, B.N.: Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. rev. Diane Publishing, Darby (2009)

    Google Scholar 

  33. Cimatti, A., Micheli, A., Roveri, M.: Timelines with temporal uncertainty. In: AAAI (2013)

    Google Scholar 

  34. Chattopadhyay, S., Banerjee, A., Banerjee, N.: A data distribution model for large-scale context aware systems. In: International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, pp. 615–627 (2013)

    Google Scholar 

  35. Chattopadhyay, S., Banerjee, A., Yu, B.: A utility-driven data transmission optimization strategy in large scale cyber-physical systems. In: 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE) (May 2017)

    Google Scholar 

  36. Sinha, A., Malo, P., Deb, K.: A review on bilevel optimization: from classical to evolutionary approaches and applications (2017), arXiv:1705.06270v1

    Google Scholar 

  37. Wu, X., Conejo, A.J.: An efficient tri-level optimization model for electric grid defense planning. IEEE Trans. Power Syst. 32(4), 2984–2994 (2017)

    Article  Google Scholar 

  38. Yang, Z., Cai, L., Lu, W.S.: Practical scheduling algorithms for concurrent transmissions in rate-adaptive wireless networks. In: INFOCOM, IEEE 2010 Proceedings (2010)

    Google Scholar 

  39. Yang, M., Li, C., Cai, Z., Guan, J.: Differential evolution with auto-enhanced population diversity. IEEE Trans Cybern. 45(2), 302–315 (2015). https://doi.org/10.1109/TCYB.2014.2339495

    Article  Google Scholar 

  40. Bogdan, P., Jain, S., Goyal, K., Marculescu, R.: Implantable pace-makers control and optimization via fractional calculus approaches: a cyber-physical systems perspective. In: ICCPS, pp. 23–32 (2012)

    Google Scholar 

  41. Hong, J., et al.: Integrated anomaly detection for cyber security of the substations. IEEE Trans. Smart Grid 5(4), 1643–1653 (2014)

    Article  Google Scholar 

  42. Yue, K., et al.: An adaptive discrete event model for cyber-physical system. In: Analytic Virtual Integration of Cyber-Physical Systems Workshop, pp. 9–15 (2010)

    Google Scholar 

  43. Hang, C., Manolios, P., Papavasileiou, V.: Synthesizing cyber-physical architectural models with real-time constraints. In: Computer Aided Verification, pp. 441–456. Springer, Berlin (2011)

    Google Scholar 

  44. Bujorianu, M.C., Bujorianu, M.L., Barringer, H.: A formal framework for user centric control of probabilistic multi-agent cyber-physical systems. In: International Workshop on Computational Logic in Multi-Agent Systems, pp. 97–116. Springer, Berlin/Heidelberg (2008)

    Google Scholar 

  45. Goswami, D., Schneider, R., Chakraborty, S.: Co-design of cyber-physical systems via controllers with flexible delay constraints. In: ASPDAC, pp. 225–230 (2011)

    Google Scholar 

  46. Khaitan, S.K., Mccalley, J.: Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9(2), 350–365 (2014)

    Article  Google Scholar 

  47. Farina, M., Deb, K., Amato, P.: Dynamic multi-objective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8(5), 425–442 (2004)

    Article  Google Scholar 

  48. Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multi-objective optimization. IEEE Trans. Evol. Comput. 13(1), 103–127 (2009)

    Article  Google Scholar 

  49. Ren, W., Beard, R.W., Atkins, E.M.: Information consensus in multivehicle cooperative control. IEEE Control Syst. 27, 71–82 (2007)

    Article  Google Scholar 

  50. Cao, X., Cheng, P., Chen, J., Sun, Y.: An online optimization approach for control and communication codesign in networked cyber-physical systems. IEEE Trans. Ind. Inf. 9, 439–450 (2013)

    Article  Google Scholar 

  51. Song, Z., Chen, Y., Sastry, C.R., Tas, N.C.: Optimal Observation for Cyber-Physical Systems: A Fisher-Information-Matrix-Based Approach. Springer Science & Business Media, Berlin (2009)

    Book  MATH  Google Scholar 

  52. Shoukry, Y., Nuzzo, P., Vincentelli, S., Seshia, S.A., Pappas, G.J., Tabuada, P.: SMC: satisfiability modulo convex optimization. In: HSCC’17, Pittsburgh, PA, USA, April 18–20 (2017)

    Google Scholar 

  53. Juditsky, A., Nemirovski, A.: First order methods for nonsmooth convex large-scale optimization, II: utilizing problems structure. Optim. Mach. Learn. 30, 149–183 (2011)

    Google Scholar 

  54. Guigues, V., Juditsky, A., Nemirovski, A.: Non-asymptotic confidence bounds for the optimal value of a stochastic program. Optim. Methods Softw. 32(5), 1033–1058 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  55. Yuan, Y., Xu, H., Wang, B., et al.: Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans. Evol. Comput. 20(2), 180–198 (2016)

    Article  Google Scholar 

  56. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1201–1208. ACM, New York (2006)

    Google Scholar 

  57. Zhou, A., Jin, Y., Zhang, Q.: A population prediction strategy for evolutionary dynamic multi-objective optimization. IEEE Trans. Cybern. 44(1), 40–53 (2014)

    Article  Google Scholar 

  58. Koo, W.T., Goh, C.K.: A predictive gradient strategy for multi-objective evolutionary algorithms in a fast changing environment. Memetic Comput. 2(2), 87–110 (2010)

    Article  Google Scholar 

  59. Peng, Z., Zheng, J., Zou, J.: A population diversity maintaining strategy based on dynamic environment evolutionary model for dynamic multiobjective optimization. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 274–281. IEEE, Piscataway (2014)

    Google Scholar 

  60. Deb, K., Abouhawwash, M.: A optimality theory based proximity measure for set based multi-objective optimization. IEEE Trans. Evol. Comput. 20(4), 515–528 (2016)

    Article  Google Scholar 

  61. Birbil, S.I., Frenk, J.B.G., Still, G.J.: An elementary proof of the Fritz-John and Karush-Kuhn-Tucker conditions in nonlinear programming. Eur. J. Oper. Res. 180(1), 479–484 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  62. Mahdavi-Hezavehi, S., Avgeriou, P., Weyns D.: A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements. In: Mistrik, I., Ali, N., Kazman, R., Grundy, J., Schmerl, B. (eds.) Managing Trade-offs in Adaptable Software Architectures, pp. 45–78. Morgan Kaufmann, San Francisco (2016)

    Google Scholar 

  63. Bussmann, S., Schild, K.: Self-organizing manufacturing control: an industrial application of agent technology. In: Proceedings Fourth International Conference on MultiAgent Systems, Boston, MA, pp. 87–94 (2000)

    Google Scholar 

  64. Leitao, P., Barbosa, J.: Adaptive scheduling based on self-organized holonic swarm of schedulers. In: Proceedings of the 23rd IEEE International Symposium on Industrial Electronics, Istanbul, pp. 1706–1711 (2014)

    Google Scholar 

  65. Vrba, P., Marik, V.: Capabilities of dynamic reconfiguration of multiagent-based industrial control systems. IEEE Trans. Syst. Man Cybern. A 40(2), 213–223 (2010)

    Article  Google Scholar 

  66. Zhang, Y., Qian, C., Lv, J., Liu, Y.: Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Trans. Ind. Inf. 13(2), 737–747 (2017)

    Article  Google Scholar 

  67. Deb, K., Abouhawwash, M., Seada, H.: A computationally fast convergence measure and implementation foe single, multiple- and many-objective optimization. IEEE Trans. Emerg. Top. Compt. Intell. 1(4), 280–293 (2017)

    Article  Google Scholar 

  68. Yin, Y., Yu, F., Xu, Y., Yu, L., Mu, J.: Network location-aware service recommendation with random walk in cyber-physical systems. Sensors 17, 2059 (2017)

    Article  Google Scholar 

  69. Fang, Z., Mo, H., Wang, Y., Xie, M.: Performance and reliability improvement of cyber-physical systems subject to degraded communication networks through robust optimization. Comput. Ind. Eng. 114, 166–174 (2017)

    Article  Google Scholar 

  70. Akkaya, I.: Data-driven cyber-physical systems via real-time stream analytics and machine learning, Technical Report No. UCB/EECS-2016-159, Electrical Engineering and Computer Sciences University of California at Berkeley, October 25 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soumya Banerjee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Banerjee, S., Balas, V.E., Pandey, A., Bouzefrane, S. (2020). Towards Intelligent Optimization of Design Strategies of Cyber-Physical Systems: Measuring Efficacy Through Evolutionary Computations. In: Llanes Santiago, O., Cruz Corona, C., Silva Neto, A., Verdegay, J. (eds) Computational Intelligence in Emerging Technologies for Engineering Applications. Studies in Computational Intelligence, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-34409-2_5

Download citation

Publish with us

Policies and ethics