This paper presents a framework and demonstrates results from a process detection based approach to tracking an airborne plume in sensor networks. Data integration and pattern detection in large sensor networks measuring gas and radiation plumes suffer from low resolution observations, missed detections, and numerous false positive reports. Large numbers of nodes and the hypothesis management concept of a Process Query System (PQS) can compensate for lower data quality. A result of the process detection based approach to this problem is models that can be implemented in many different scenarios. Plume predictor models are illustrated which allow data association between sensor nodes in typical outdoor
wind conditions.
We demonstrate a simulation of a mobile plume source in a sensor network designed for use in the same PQS. A kinematic model is developed for a vehicle carrying a plume source. Inverse models for this mobile plume source will work in conjunction with the existing software systems, thus allowing PQS to rapidly be adapted to a new problem domain with minimal modifications. This scenario of a mobile airborne plume source approximates a moving container emitting a detectable substance in a transportation network, where the container movement is restricted by existing vehicle corridors.
KEYWORDS: Sensors, Diffusion, Sensor networks, Data modeling, Monte Carlo methods, Atmospheric modeling, Filtering (signal processing), Error analysis, Chemical fiber sensors, Process modeling
A Process Query System (PQS) has the capability of filtering large volumes of real time data originating from a field of networked Physical Sensors. Modern air quality monitoring techniques such as Fourier Transform Infra-Red (FTIR) spectroscopy will eventually provide massively distributed real time contamination data at high fidelity. As large networks of these sensors are deployed, improved techniques of data retrieval and assimilation will be required. The case of detecting a diffusion event such as a hazardous chemical plume is considered. In this scenario, a plume model based on an Ensemble Kalman Filter (EnKF)is submitted to the PQS which manages multiple hypotheses explaining the current observations. The feasibility of such an application is demonstrated and results from preliminary simulations are presented.
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