Selecting sensing location leveraging spatial and cross-domain correlations

In environmental monitoring applications, selecting appropriate locations to sense is important relating to data quality and Sensing cost. This paper addresses the challenge by collecting data from a subset of locations, then leveraging the spatial and cross-domain correlations to deduce data of other locations, thus can obtain acceptable data quality with lower sensing cost. Referring to active learning, the proposed framework is constructed by two types modules (i.e., estimators and selectors) and a cyclic process of estimating and selecting. Estimators based on kriging interpolation and regression tree are implemented, and their corresponding selectors are designed. We evaluate the effectiveness of the framework by taking air quality sensing as an example. Results show that to reach data quality of about 25% MAPE, the framework only needs 15% locations, while random selector needs 25% locations.