Mobile Stream Sampling under Time Constraints

The proliferation of mobile networking and the increasing capabilities of smartphone devices in the recent years have resulted in transforming mobile smartphone devices into ubiquitous sensing platforms. In this new class of “Community-based Participatory Sensing” systems, users actively participate in the data collection and sharing for the benefit of the community, in a wide range of application areas from entertainment, to transportation, to environmental monitoring. These approaches, however, generate large amounts of transient data streams, leading to real-time computational challenges. In this paper we propose sampling algorithms on streams of mobile data generated by ubiquitous sensing devices that need to be processed under time constraints. In our approach users participate in the system by sensing and sharing streams of data. The system then uses a sampling mechanism to select a subset of data streams that preserves the characteristics of the stream data and provides the highest “information gain” to the system, given the real-time, budget and resource constraints. Detailed experimental results illustrate that our approach is practical, efficient and depicts good performance.