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The use of robotics in distributed field monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of mobile sensor deployment includes sampling algorithms at locations most likely to yield useful information about a spatio-temporal field variable of interest. This paper proposes to use robotic nodes to estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The authors' proposed algorithm, "EKF-NN-GAS", is based on neural networks, the Extended Kalman Filter and greedy search heuristics.
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