Cited 3 time in
Cited 3 time in
Impact of sensor measurement error on sensor positioning in water quality monitoring networks
- Impact of sensor measurement error on sensor positioning in water quality monitoring networks
- Kim, Seong Hee; Aral, Mustafa M.; Eun, Yong Soon; Park, Jisu J.; Park, Chul Jin
- DGIST Authors
- Eun, Yong Soon
- Issue Date
- Stochastic Environmental Research and Risk Assessment, 31(3), 743-756
- Article Type
- Article in Press
- Algorithms; Errors; Image Resolution; Location; Measurement Error Models; Measurement Errors; Optimal Sensor Locations; Optimization; Optimization Algorithms; Probabilistic Modeling; Process Control; Quality Control; Sensor Measurement Errors; Sensor Measurements; Sensor Networks; Simulation Optimization; Statistical Process Controls (SPC); Water Quality; Water Quality Monitoring
- This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network. © 2016, Springer-Verlag Berlin Heidelberg.
- Springer New York LLC
- Related Researcher
DSC Lab(Dynamic Systems and Control Laboratory)
Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
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