Matador: Mobile task detector for context-aware crowd-sensing campaigns

Abstract

Ubiquity of internet-connected media- and sensor-equipped portable devices is enabling a new class of applications which exploit the power of crowds to perform sensing tasks in the real world. Such paradigm is referred as crowd-sensing, and lies at the intersection of crowd-sourcing and participatory sensing. This has a wide range of potential applications such as direct involvement of citizens into public decision making. In this work we present Matador, a framework to embed context-awareness in the presentation and execution of crowd-sensing tasks. This allows to present the right tasks, to the right users in the right circumstances, and to preserve normal device functioning. We present the design and prototype implementation of the platform, including an energy-efficient context sampling algorithm. We validate the proposed approach through a numerical study and a small pilot, and demonstrate the ability of the proposed system to efficiently deliver crowd-sensing tasks, while minimizing the consumption of mobile device resources.