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Foundations of Value of Information: A Semantic Metric for Networked Control Systems Tasks (2403.11927v1)

Published 18 Mar 2024 in cs.IT, math.IT, and math.OC

Abstract: In this chapter, we present our recent invention, i.e., the notion of the value of information$\unicode{x2014}$a semantic metric that is fundamental for networked control systems tasks. We begin our analysis by formulating a causal tradeoff between the packet rate and the regulation cost, with an encoder and a decoder as two distributed decision makers, and show that the valuation of information is conceivable and quantifiable grounded on this tradeoff. More precisely, we characterize an equilibrium, and quantify the value of information there as the variation in a value function with respect to a piece of sensory measurement that can be communicated from the encoder to the decoder at each time. We prove that, in feedback control of a dynamical process over a noiseless channel, the value of information is a function of the discrepancy between the state estimates at the encoder and the decoder, and that a data packet containing a sensory measurement at each time should be exchanged only if the value of information at that time is nonnegative. Finally, we prove that the characterized equilibrium is in fact globally optimal.

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References (36)
  1. E. Uysal, O. Kaya, A. Ephremides, J. Gross, M. Codreanu, P. Popovski, M. Assaad, G. Liva, A. Munari, B. Soret, T. Soleymani, and K. H. Johansson, “Semantic communications in networked systems: A data significance perspective,” IEEE Network, vol. 36, no. 4, pp. 233–240, 2022.
  2. J. Baillieul and P. J. Antsaklis, “Control and communication challenges in networked real-time systems,” Proceedings of IEEE, vol. 95, no. 1, pp. 9–28, 2007.
  3. P. Park, S. C. Ergen, C. Fischione, C. Lu, and K. H. Johansson, “Wireless network design for control systems: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 978–1013, 2017.
  4. E. A. Lee, R. Akella, S. Bateni, S. Lin, M. Lohstroh, and C. Menard, “Consistency vs. availability in distributed cyber-physical systems,” ACM Transactions on Embedded Computing Systems, vol. 22, no. 5s, pp. 1–24, 2023.
  5. Springer Science & Business Media, 2007.
  6. K. J. Åström and B. Bernhardsson, “Comparison of Riemann and Lebesgue sampling for first order stochastic systems,” in Proc. IEEE Conf. on Decision and Control, pp. 2011–2016, 2002.
  7. Y. Tsividis, “Event-driven data acquisition and digital signal processing–A tutorial,” IEEE Trans. on Circuits and Systems II: Express Briefs, vol. 57, no. 8, pp. 577–581, 2010.
  8. D. V. Dimarogonas, E. Frazzoli, and K. H. Johansson, “Distributed event-triggered control for multi-agent systems,” IEEE Trans. on Automatic Control, vol. 57, no. 5, pp. 1291–1297, 2012.
  9. M. Meinel, M. Ulbrich, and S. Albrecht, “A class of distributed optimization methods with event-triggered communication,” Computational Optimization and Applications, vol. 57, no. 3, pp. 517–553, 2014.
  10. H. Li, Z. Chen, L. Wu, H.-K. Lam, and H. Du, “Event-triggered fault detection of nonlinear networked systems,” IEEE Trans. on Cybernetics, vol. 47, no. 4, pp. 1041–1052, 2017.
  11. T. Soleymani, Value of Information Analysis in Feedback Control. PhD thesis, Technical University of Munich, 2019.
  12. T. Soleymani, J. S. Baras, and S. Hirche, “Value of information in feedback control: Quantification,” IEEE Trans. on Automatic Control, vol. 67, no. 7, pp. 3730–3737, 2022.
  13. T. Soleymani, J. S. Baras, S. Hirche, and K. H. Johansson, “Value of information in feedback control: Global optimality,” IEEE Trans. on Automatic Control, vol. 68, no. 6, pp. 3641–3647, 2023.
  14. O. C. Imer and T. Başar, “Optimal estimation with limited measurements,” Intl. Journal of Systems, Control and Communications, vol. 2, no. 1-3, pp. 5–29, 2010.
  15. G. M. Lipsa and N. C. Martins, “Remote state estimation with communication costs for first-order LTI systems,” IEEE Trans. on Automatic Control, vol. 56, no. 9, pp. 2013–2025, 2011.
  16. A. Molin and S. Hirche, “Event-triggered state estimation: An iterative algorithm and optimality properties,” IEEE Trans. on Automatic Control, vol. 62, no. 11, pp. 5939–5946, 2017.
  17. J. Chakravorty and A. Mahajan, “Fundamental limits of remote estimation of autoregressive Markov processes under communication constraints,” IEEE Trans. on Automatic Control, vol. 62, no. 3, pp. 1109–1124, 2016.
  18. M. Rabi, G. V. Moustakides, and J. S. Baras, “Adaptive sampling for linear state estimation,” SIAM Journal on Control and Optimization, vol. 50, no. 2, pp. 672–702, 2012.
  19. N. Guo and V. Kostina, “Optimal causal rate-constrained sampling for a class of continuous Markov processes,” IEEE Trans. on Information Theory, vol. 67, no. 12, pp. 7876–7890, 2021.
  20. J. Sijs and M. Lazar, “Event based state estimation with time synchronous updates,” IEEE Trans. on Automatic Control, vol. 57, no. 10, pp. 2650–2655, 2012.
  21. J. Wu, Q.-S. Jia, K. H. Johansson, and L. Shi, “Event-based sensor data scheduling: Trade-off between communication rate and estimation quality,” IEEE Trans. on Automatic Control, vol. 58, no. 4, pp. 1041–1046, 2013.
  22. L. He, J. Chen, and Y. Qi, “Event-based state estimation: Optimal algorithm with generalized closed skew normal distribution,” IEEE Trans. on Automatic Control, vol. 64, no. 1, pp. 321–328, 2018.
  23. D. Han, Y. Mo, J. Wu, S. Weerakkody, B. Sinopoli, and L. Shi, “Stochastic event-triggered sensor schedule for remote state estimation,” IEEE Trans. on Automatic Control, vol. 60, no. 10, pp. 2661–2675, 2015.
  24. C. Ramesh, H. Sandberg, and K. H. Johansson, “Design of state-based schedulers for a network of control loops,” IEEE Trans. on Automatic Control, vol. 58, no. 8, pp. 1962–1975, 2013.
  25. A. Molin and S. Hirche, “On the optimality of certainty equivalence for event-triggered control systems,” IEEE Trans. on Automatic Control, vol. 58, no. 2, pp. 470–474, 2013.
  26. B. Demirel, A. S. Leong, V. Gupta, and D. E. Quevedo, “Tradeoffs in stochastic event-triggered control,” IEEE Trans. on Automatic Control, vol. 64, no. 6, pp. 2567–2574, 2018.
  27. R. A. Howard, “Information value theory,” IEEE Trans. on Systems Science and Cybernetics, vol. 2, no. 1, pp. 22–26, 1966.
  28. G. J. Stigler, “The economics of information,” Journal of political economy, vol. 69, no. 3, pp. 213–225, 1961.
  29. J. P. Gould, “Risk, stochastic preference, and the value of information,” Journal of Economic Theory, vol. 8, no. 1, pp. 64–84, 1974.
  30. M. Avriel and A. Williams, “The value of information and stochastic programming,” Operations Research, vol. 18, no. 5, pp. 947–954, 1970.
  31. M. A. H. Dempster, “The expected value of perfect information in the optimal evolution of stochastic systems,” in Stochastic Differential Systems (M. Arató, D. Vermes, and A. V. Balakrishnan, eds.), pp. 25–40, Springer, 1981.
  32. M. Davis, “Anticipative LQG control,” IMA Journal of Mathematical Control and Information, vol. 6, no. 3, pp. 259–265, 1989.
  33. M. Davis, “Anticipative LQG control II,” in Applied Stochastic Analysis (M. H. Davis and R. J. Elliott, eds.), pp. 205–214, Gordon & Breach, 1991.
  34. K. J. Åström, Introduction to Stochastic Control Theory. Dover Publications, 2006.
  35. Y. Bar-Shalom and E. Tse, “Dual effect, certainty equivalence, and separation in stochastic control,” IEEE Trans. on Automatic Control, vol. 19, no. 5, pp. 494–500, 1974.
  36. T. Başar and G. J. Olsder, Dynamic Noncooperative Game Theory. Society for Industrial and Applied Mathematics, 1998.
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