Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management.By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field.However, limited resources of Fill-In Fibers sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field.This paper presents Kitchen Towels a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process.
To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure.We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach.