GradeSense: Gradation Aware Storage for Robust Activity Recognition in a Multimodal Smarthome

A wide range of multimodal sensors such as sensors, video cameras, wearables worn by users in an IoT powered smarthome provide important albeit huge amount of data through which applications derive meaningful Activities of Daily Living (ADLs). Storing this massive amount of data is a significant challenge for efficient execution of the corresponding applications to meet real-time demands; there is scope for improving it as it is found that a substantial amount of the data produced may be unimportant. In this paper, we propose an end-to-end system GradeSense, which implements a grading mechanism based on multimodal data fusion by categorizing ‘important’ data. GradeSense is made complete by an applicationindependent storage module that leverages our grading scheme (as opposed to traditional usage-based models) for efficient storing. Activity prediction algorithms perform well (up to 17% improvement) with this now-fused and important data which is a mere fraction of the entire data, achieving 87% data reduction on average in faster storage tier. The throughput of GradeSense, measured through runtime, gives improvement up to 46% due to our enhanced data-categorization and storing mechanism.