Mobile sensing based indoor thermal field reconstruction: Test in a virtual environment

Abstract

Environmental monitoring is a prerequisite to evaluate, control, and optimize indoor environmental quality. Compared to stationary sensing that deploys sensors at fixed locations, mobile sensing using an automated moving robot can actively take measurements at locations of interests, which provides a more flexible and efficient way to achieve a high-granularity agile environmental monitoring. Studies have been conducted to design and implement mobile sensing algorithms, however, to deploy on hardware and test the algorithm in the real world is usually expensive and challenging. In this study, we introduced a virtual testbed, AlphaMobileSensing which can be used to test, evaluate, and benchmark mobile sensing algorithms easily and efficiently. Using the virtual testbed, we conducted a test on a spatio-temporal (ST) interpolation algorithm for its robustness in indoor thermal field reconstruction. Two factors, the moving path, and the initial position, were considered, and the corresponding field reconstruction results were compared. The results show that the ST interpolation algorithm can extract similar global trend of a dynamic field regardless of different moving paths and initial locations, however, predictions of field local variations are sensitive to these two factors.