The emerging technology of wireless sensor networks (WSNs) is an integrated, distributed, wireless network of sensing devices. It has the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by providing environmental information at greater spatial and temporal resolutions. Furthermore, due to continuing high-performance computing development, these data may be introduced into increasingly robust and complex numerical models; for instance, the parameters of subsurface transport simulators may be automatically updated. Early field deployments and laboratory experiments conducted using in situ sensor technology and WSNs indicated significant fundamental issues concerning sensor and network hardware reliability-suggesting that investigations should first be conducted in controlled environments before field deployment. A first step in this validation process involves evaluating the predictive capability of a computational advection-dispersion transport model when incorporating concentration data from a WSN simulation. Data quality is a major concern, especially when sensor readings are automatically fed into data assimilation procedures. The appropriate employment of an independent WSN fault detection service can ensure that erroneous data (e.g., missing or anomalous values) do not mislead the model. Parameter estimation regularization techniques may then deal with remaining data noise. The primary purpose of this study is to determine the suitability of WSNs (and other in situ data delivery technologies) for use in contaminant transport modeling applications by conducting research in a realistic simulative environment.

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Increased interest in Wireless Sensor Networks (WSNs) by scientists and engineers is forcing WSN research to focus on application requirements. Data is available as never before in many fields of study; practitioners are now burdened with the challenge of doing data-rich research rather than being data-starved. In-situ sensors can be prone to errors, links between nodes are often unreliable, and nodes may become unresponsive in harsh environments, leaving to researchers the onerous task of deciphering often anomalous data. Presented here is the REDFLAG fault detection service for WSN applications, a Run-timE, Distributed, Flexible, detector of faults, that is also Lightweight And Generic. REDFLAG addresses the two most worrisome issues in data-driven wireless sensor applications: abnormal data and missing data. REDFLAG exposes faults as they occur by using distributed algorithms in order to conserve energy. Simulation results show that REDFLAG is lightweight both in terms of footprint and required power resources while ensuring satisfactory detection and diagnosis accuracy. Because REDFLAG is unrestrictive, it is generically available to a myriad of applications and scenarios.

[1] Emerging in situ sensors and distributed network technologies have the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by sampling at greater spatial and temporal resolutions. Since sensor networks supply data with little or no delay, applications exist where automatic or real-time assimilation of this data would be useful, for example, during smart remediation procedures where tracking of the plume response will reinforce real-time decisions. As a foray into this new data context, we consider the estimation of hydraulic conductivity when incorporating subsurface plume concentration data. Current practice optimizes the model in the time domain, which is often slow and very nonlinear. Instead, we perform model inversion in Laplace space and are able to do so because data gathered using new technologies can be sampled densely in time. An intermediate-scale synthetic aquifer is used to illustrate the developed technique.