Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena

Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 1706 - 1711
Issue Date:
2013-08-19
Metrics:
Full metadata record
This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expected-value function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on real-world datasets have verified the superiority of the proposed approach. © 2013 IEEE.
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