Enabling Real-Time In-Situ Processing of Ubiquitous Mobile-Application Workflows

Abstract

The heterogeneous sensing and computing capabilities of sensor nodes, mobile handhelds, as well as computing and storage servers in remote data centers can be harnessed to enable innovative mobile applications that rely on real-time in-situ processing of data generated in the field. There is, however, uncertainty associated with the quality and quantity of data from mobile sensors as well as with the availability and capabilities of mobile computing resources on the field. Data and computing-resource uncertainty, if unchecked, may propagate up the "raw-data→information→knowledge" chain and have an adverse effect on the relevance of the generated results. A unified uncertainty-aware framework for data and computing-resource management is proposed to enable in-situ processing of application workflows on mobile sensing and computing platforms and, hence, to generate actionable knowledge from raw data within realistic time bounds. A two-phase solution that captures the propagation of data-uncertainty up the data-processing chain using interval arithmetic in the first phase and that employs multi-objective optimization for task allocation in the second phase is presented and evaluated in detail.