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Value of information for spatially distributed systems: Application to sensor placement

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  • Malings, Carl
  • Pozzi, Matteo

Abstract

This paper investigates how the value of information (VoI) metric can guide information collection and optimal sensor placement in spatially distributed systems. VoI incorporates relevant features to decision-making, such as uncertainty about the state of the system, precision of measurements, the availability of intervention actions, and the overall cost of managing the system. Spatially distributed systems also allow for information propagation, i.e. measurements collected at one location can be used to update knowledge at other related locations. In this paper, while restricting our attention to Gaussian random field and binary state models, we illustrate first how sensor placements depend on the decision-making problem to be addressed, as encoded in a problem-specific loss function, and second how the complexity of VoI computations is impacted by this loss function's characteristics. In doing so, we consider several loss functions and present computational techniques for evaluating VoI under them. Finally, we apply these techniques to efficiently optimize sensor placements by the VoI metric in two example applications.

Suggested Citation

  • Malings, Carl & Pozzi, Matteo, 2016. "Value of information for spatially distributed systems: Application to sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 219-233.
    • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:219-233
    DOI: 10.1016/j.ress.2016.05.010
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    References listed on IDEAS

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    1. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    2. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    3. Memarzadeh, Milad & Pozzi, Matteo, 2016. "Value of information in sequential decision making: Component inspection, permanent monitoring and system-level scheduling," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 137-151.
    4. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
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    Citations

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    Cited by:

    1. Straub, Daniel & Ehre, Max & Papaioannou, Iason, 2022. "Decision-theoretic reliability sensitivity," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Yuan, Xian-Xun & Higo, Eishiro & Pandey, Mahesh D., 2021. "Estimation of the value of an inspection and maintenance program: A Bayesian gamma process model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Xu, Hao & Gardoni, Paolo, 2020. "Conditional formulation for the calibration of multi-level random fields with incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    5. Daneshkhah, A. & Stocks, N.G. & Jeffrey, P., 2017. "Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 33-45.
    6. Kapoor, Medha & Christensen, Christian Overgaard & Schmidt, Jacob Wittrup & Sørensen, John Dalsgaard & Thöns, Sebastian, 2023. "Decision analytic approach for the reclassification of concrete bridges by using elastic limit information from proof loading," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Malings, C. & Pozzi, M., 2019. "Submodularity issues in value-of-information-based sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 93-103.
    8. Jensen, H.A. & Jerez, D.J., 2019. "A Bayesian model updating approach for detection-related problems in water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 100-112.
    9. Lin, Chaochao & Song, Junho & Pozzi, Matteo, 2022. "Optimal inspection of binary systems via Value of Information analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Fauriat, William & Zio, Enrico, 2020. "Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Memarzadeh, Milad & Pozzi, Matteo, 2016. "Value of information in sequential decision making: Component inspection, permanent monitoring and system-level scheduling," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 137-151.
    12. Pozzi, Matteo & Malings, Carl & Minca, Andreea, 2020. "Information avoidance and overvaluation under epistemic constraints: Principles and implications for regulatory policies," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

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