Source localization by spatially distributed electronic noses for advection and diffusion

Matthes, Jörg; Gröll, Lutz; Keller, Hubert B.

doi:10.1109/tsp.2005.845423

Cited by 89 publications

(57 citation statements)

References 23 publications

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“…The passing route is determined according to various estimation performance metrics including CRB. In geometric trilateration approach [14,4], the measurement of a sensor is mapped to the distance from the sensor to the diffusion source. The source location can then be estimated by trilateration among multiple sensors.…”

Section: Related Workmentioning

confidence: 99%

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Wang¹,

Tan²,

Xing³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class robotic sensor platforms.

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Section: Related Workmentioning

confidence: 99%

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Wang¹,

Tan²,

Xing³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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“…Ziel der vorliegenden Arbeit ist es, die genannten Probleme bei der Quellenlokalisierung auf der Basis analytischer Ausbreitungsmodelle durch einen neuen, geometrischen Zugang [36,71,72,73] In einem isotropen Medium sind die Diffusionskoeffizienten richtungsunabhängig (…”

Section: Ziele Und Gliederung Der Arbeitunclassified

Eine neue Methode zur Quellenlokalisierung auf der Basis räumlich verteilter, punktweiser Konzentrationsmessungen

Matthes¹

2004

At - Automatisierungstechnik

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“…Typically, the inverse modeling problem is recast as an optimization problem such as the weighted linear or nonlinear least squares (e.g., Singh and Rani, 2014;Matthes et al, 2005), simulated annealing (e.g., Thomson et al, 2007), or pattern search method (e.g., Zheng and Chen, 2010). Cervone and Franzese (2010) studied several error functions to identify suitable measures and cost functions for optimizations and Kovalets et al (2011) used a fluid dynamics model to build up a cost function which could be optimized.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

et al. 2017

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Abstract. In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model.

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Source localization by spatially distributed electronic noses for advection and diffusion

Cited by 89 publications

References 23 publications

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Eine neue Methode zur Quellenlokalisierung auf der Basis räumlich verteilter, punktweiser Konzentrationsmessungen

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

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Source localization by spatially distributed electronic noses for advection and diffusion

Cited by 89 publications

References 23 publications

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Eine neue Methode zur Quellenlokalisierung auf der Basis räumlich verteilter, punktweiser Konzentrationsmessungen

Bayesian inverse modeling and source location of an unintended &lt;sup&gt;131&lt;/sup&gt;I release in Europe in the fall of 2011

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Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011