Distributionally Robust Optimal Sensor Placement Method for Site-scale Methane-emission Monitoring

Recent research in deterministic sensor placement optimization technologies has improved the capability of monitoring large-scale field environments with a limited budget. In traditional stochastic mixed-integer linear programming formulations, minimizing the expectation of detection time can lead t...

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Bibliographic Details
Published in:IEEE sensors journal 2022-12, Vol.22 (23), p.1-1
Main Authors: Zi, Yuan, Fan, Lei, Wu, Xuqing, Chen, Jiefu, Han, Zhu
Format: Article
Language:English
Subjects:
Carbon monitoring
Distributionally robust optimization
Emission
Integer programming
Linear programming
Methane
Methane Sensor
Mixed integer
Mixed integer programming
Monitoring
Optimal sensor placement
Optimization
Placement
Robustness
Sensor placement
Sensors
Stochastic programming
Uncertainty
Wind
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Summary:Recent research in deterministic sensor placement optimization technologies has improved the capability of monitoring large-scale field environments with a limited budget. In traditional stochastic mixed-integer linear programming formulations, minimizing the expectation of detection time can lead to a detector placement with good average behaviour but unexpected worst-case behaviour. The uncertainty factors in the complex environment and sensor system significantly challenge the effects of the placement strategy provided by stochastic programming. These factors include unknown leakage rate and location, sensor delay, and primary uncertainty of wind conditions. This paper introduces a distributionally robust optimization formulation of sensor placement under the uncertainty of wind conditions and improves a sensor network's detection robustness. The method is demonstrated using the atmospheric simulation with site-specific methane emission scenarios that capture partial natural wind conditions and emission characteristics. Distributionally robust optimization techniques are employed to determine sensor locations that minimize the detection time expectation of the emission scenarios with a significantly better worst-case behaviour. Experiment results show that the proposed distributionally robust optimization method outperforms the sensor placement methods based on stochastic programming.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3214176