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Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems (2310.06224v1)

Published 10 Oct 2023 in cs.IT and math.IT

Abstract: In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors that are monitoring several safety-critical situations (e.g., carbon monoxide density in forest fire detection, machine safety in industrial automation, and road safety). Based on the received sensor updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited communication resources, the sensor updates may not be timely, resulting in the possibility of misunderstanding the current situation. In particular, if a dangerous situation is misinterpreted as safe, the safety risk is high. In this paper, we introduce a novel framework that quantifies the penalty due to the unawareness of a potentially dangerous situation. This situation-unaware penalty function depends on two key factors: the Age of Information (AoI) and the observed signal value. For optimal estimators, we provide an information-theoretic bound of the penalty function that evaluates the fundamental performance limit of the system. To minimize the penalty, we study a pull-based multi-sensor, multi-channel transmission scheduling problem. Our analysis reveals that for optimal estimators, it is always beneficial to keep the channels busy. Due to communication resource constraints, the scheduling problem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. By utilizing relaxation and Lagrangian decomposition of the RMAB, we provide a low-complexity scheduling algorithm which is asymptotically optimal. Our results hold for both reliable and unreliable channels. Numerical evidence shows that our scheduling policy can achieve up to 100 times performance gain over periodic updating and up to 10 times over randomized policy.

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References (41)
  1. A. Grau, M. Indri, L. L. Bello, and T. Sauter, “Industrial robotics in factory automation: From the early stage to the internet of things,” in IEEE IECON, 2017, pp. 6159–6164.
  2. S. Abdulmalek, A. Nasir, W. A. Jabbar, M. A. Almuhaya, A. K. Bairagi, M. A.-M. Khan, and S.-H. Kee, “IoT-based healthcare-monitoring system towards improving quality of life: A review,” in Healthcare, vol. 10, no. 10, 2022, p. 1993.
  3. M. Seenivasan, M. Arularasu, K. Senthilkumar, and R. Thirumalai, “Disaster prevention and control management in automation: a key role in safety engineering,” Procedia Earth and Planetary Science, vol. 11, pp. 557–565, 2015.
  4. F. Li, Y. Sang, Z. Liu, B. Li, H. Wu, and B. Ji, “Waiting but not aging: Optimizing information freshness under the pull model,” IEEE/ACM Trans. Netw., vol. 29, no. 1, pp. 465–478, 2020.
  5. S. Kaul, R. D. Yates, and M. Gruteser, “Real-time status: How often should one update?” in IEEE INFOCOM, 2012.
  6. A. P. Dawid, “Coherent measures of discrepancy, uncertainty and dependence, with applications to bayesian predictive experimental design,” Department of Statistical Science, University College London, vol. 139, 1998.
  7. F. Farnia and D. Tse, “A minimax approach to supervised learning,” Advances in Neural Information Processing Systems, vol. 29, 2016.
  8. M. K. C. Shisher and Y. Sun, “How does data freshness affect real-time supervised learning?” in ACM MobiHoc, 2022, pp. 31–40.
  9. A. Maatouk, S. Kriouile, M. Assaad, and A. Ephremides, “The age of incorrect information: A new performance metric for status updates,” IEEE/ACM Trans. Netw., vol. 28, p. 2215–2228, oct 2020.
  10. J. Zhong, R. D. Yates, and E. Soljanin, “Two freshness metrics for local cache refresh,” in IEEE ISIT, 2018, pp. 1924–1928.
  11. X. Zheng, S. Zhou, and Z. Niu, “Urgency of information for context-aware timely status updates in remote control systems,” IEEE Trans. Wirel. Commun., vol. 19, no. 11, pp. 7237–7250, 2020.
  12. R. D. Yates, “The age of gossip in networks,” in IEEE ISIT, 2021, pp. 2984–2989.
  13. J. Holm, A. E. Kalør, F. Chiariotti, B. Soret, S. K. Jensen, T. B. Pedersen, and P. Popovski, “Freshness on demand: Optimizing age of information for the query process,” in IEEE ICC, 2021, pp. 1–6.
  14. A. Kosta, N. Pappas, A. Ephremides, and V. Angelakis, “Age and value of information: Non-linear age case,” in IEEE ISIT, 2017, pp. 326–330.
  15. G. Chen, S. C. Liew, and Y. Shao, “Uncertainty-of-information scheduling: A restless multi-armed bandit framework,” IEEE Trans. Inf. Theory, 2022.
  16. M. K. C. Shisher, H. Qin, L. Yang, F. Yan, and Y. Sun, “The age of correlated features in supervised learning based forecasting,” in IEEE INFOCOM Workshops, 2021, pp. 1–8.
  17. D. P. Bertsekas et al., “Dynamic programming and optimal control 3rd edition, volume ii,” Belmont, MA: Athena Scientific, vol. 1, 2011.
  18. G. Chen and S. C. Liew, “An index policy for minimizing the uncertainty-of-information of Markov sources,” arXiv preprint arXiv:2212.02752, 2022.
  19. Y. Sun and B. Cyr, “Sampling for data freshness optimization: Non-linear age functions,” J. Commun. Netw., vol. 21, pp. 204–219, 2019.
  20. Y. Sun, E. Uysal-Biyikoglu, R. D. Yates, C. E. Koksal, and N. B. Shroff, “Update or wait: How to keep your data fresh,” IEEE Trans. Inf. Theory, vol. 63, no. 11, pp. 7492–7508, 2017.
  21. A. M. Bedewy, Y. Sun, S. Kompella, and N. B. Shroff, “Optimal sampling and scheduling for timely status updates in multi-source networks,” IEEE Trans. Inf. Theory, vol. 67, no. 6, pp. 4019–4034, 2021.
  22. T. Z. Ornee and Y. Sun, “Sampling and remote estimation for the ornstein-uhlenbeck process through queues: Age of information and beyond,” IEEE/ACM Trans. Netw., vol. 29, no. 5, p. 1962–1975, oct 2021.
  23. Y. Sun, Y. Polyanskiy, and E. Uysal, “Sampling of the Wiener process for remote estimation over a channel with random delay,” IEEE Trans. Inf. Theory, vol. 66, no. 2, pp. 1118–1135, 2020.
  24. R. D. Yates, Y. Sun, D. R. Brown, S. K. Kaul, E. Modiano, and S. Ulukus, “Age of information: An introduction and survey,” IEEE J. Sel. Areas Commun., vol. 39, no. 5, pp. 1183–1210, 2021.
  25. N. Pappas and M. Kountouris, “Goal-oriented communication for real-time tracking in autonomous systems,” in IEEE ICAS, 2021, pp. 1–5.
  26. Z. Wang, M.-A. Badiu, and J. P. Coon, “A framework for characterizing the value of information in hidden Markov models,” IEEE Trans. Inf. Theory, vol. 68, no. 8, pp. 5203–5216, 2022.
  27. T. Soleymani, S. Hirche, and J. S. Baras, “Optimal self-driven sampling for estimation based on value of information,” in IEEE WODES, 2016, pp. 183–188.
  28. M. K. C. Shisher, B. Ji, I. Hou, Y. Sun et al., “Learning and communications co-design for remote inference systems: Feature length selection and transmission scheduling,” arXiv preprint arXiv:2308.10094, 2023.
  29. I. Kadota, A. Sinha, E. Uysal-Biyikoglu, R. Singh, and E. Modiano, “Scheduling policies for minimizing age of information in broadcast wireless networks,” IEEE/ACM Trans. Netw., vol. 26, no. 6, pp. 2637–2650, 2018.
  30. Y.-P. Hsu, “Age of information: Whittle index for scheduling stochastic arrivals,” in IEEE ISIT, 2018, pp. 2634–2638.
  31. T. Z. Ornee and Y. Sun, “A Whittle index policy for the remote estimation of multiple continuous Gauss-Markov processes over parallel channels,” accepted by ACM MobiHoc 2023.
  32. G. Xiong, X. Qin, B. Li, R. Singh, and J. Li, “Index-aware reinforcement learning for adaptive video streaming at the wireless edge,” in ACM MobiHoc, 2022, pp. 81–90.
  33. Y. Zou, K. T. Kim, X. Lin, and M. Chiang, “Minimizing age-of-information in heterogeneous multi-channel systems: A new partial-index approach,” in ACM MobiHoc, 2021, pp. 11–20.
  34. Y. Chen and A. Ephremides, “Scheduling to minimize age of incorrect information with imperfect channel state information,” Entropy, vol. 23, no. 12, p. 1572, 2021.
  35. J. Yun, A. Eryilmaz, J. Moon, and C. Joo, “Remote estimation for dynamic IoT sources under sublinear communication costs,” IEEE/ACM Trans. Netw., 2023.
  36. C. Papadimitriou and J. Tsitsiklis, “The complexity of optimal queueing network control,” in IEEE CCC, 1994, pp. 318–322.
  37. R. R. Weber and G. Weiss, “On an index policy for restless bandits,” Journal of applied probability, vol. 27, no. 3, pp. 637–648, 1990.
  38. P. Whittle, “Restless bandits: activity allocation in a changing world,” Journal of Applied Probability, vol. 25A, pp. 287–298, 1988.
  39. A. Nedic and A. Ozdaglar, “Subgradient methods in network resource allocation: Rate analysis,” in IEEE CISS, 2008, pp. 1189–1194.
  40. I. M. Verloop, “Asymptotically optimal priority policies for indexable and nonindexable restless bandits,” 2016.
  41. B. K. Muirhead, “Mars rovers, past and future,” in IEEE aerospace conference, vol. 1, 2004.
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