Sensor deployment strategy for random field estimation: One-dimensional case

Deploying the sensor nodes at the best locations for random field reconstruction via sensor network is a fundamental task. One-dimensional random field is a stochastic process. In this paper, we first propose an optimal sensor deployment strategy for Wiener process estimation. The optimal locations for the deployed sensors are uniformly distributed in the field. In addition, we propose a suboptimal sensor deployment to estimate the Gaussian random field which is described as an Ornstein-Uhlenbeck process. We show that the suboptimal deployment strategy for Gaussian random field is also uniformly distributed. Several simulations show the performance of our proposed deployment strategies.