Sensor Selection Based Routing for Monitoring Gaussian Processes M odeled Spatial Phenomena

This paper addresses the trade-off between the sensing quality and the energy consumption in the wireless sensor network associated with monitoring spatial phenomena. We use a nonparametric Gaussian Process to model the spatial phenomena to be monitored and simulated annealing based approximately heuristic algorithm for sensor selection. Our novel Sensor Selection based Routing (SSR) algorithm uses this model to identify the most informative nodes, which gives the root mean square prediction error less than a specified threshold, to construct the minimal energy-expended routing tree rooted at the sink. Our experiments have verified that the proposed computationally efficient SSR algorithm has significant advantages over conventional techniques.