Sensor Rotational Measurement with Virtual Sensors Applying Pearson Generative Adversarial Imputation Nets

Abstract

Recent advances in sensor technology have increased the ability of humans to measure a wide range of phenomena and events. Nevertheless, in some cases, due to a variety of limitations, only a few sensors can be deployed at a given site. Consequently, setting up enough sensors at the right places to provide uniform monitoring can be difficult. In addition, virtual sensing, which is a set of strategies for replacing a portion of physical sensors with virtual sensors, has recently been developed. Therefore, this work leverages the imputation capability of PGAIN-VS to develop a black-box data-driven virtual sensing approach named sensor rotational measurement for the purpose of reducing the number of physical devices to be used in reality while still ensuring monitoring accuracy. The approach takes advantage of the PGAIN-VS and Borda voting methods to determine the subset of real sensors that can take turns observing information within an interval of time. The approach is seen as a black-box objective optimization problem with constraints that is solved by the OpenBox tool. We evaluated our method on several real-world datasets and achieved promising results, with the overall number of physical sensors reduced by up to 20%.