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

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 realworld datasets have verified the superiority of the proposed approach.