AWARE: Mobile Context Instrumentation Framework

TLDR

Mobile phones are sensor-rich yet resource-constrained, requiring careful design to enable reliable context collection. The authors introduce AWARE, an open‑source mobile instrumentation toolkit designed to capture, infer, and generate context on mobile devices. AWARE is an extensible, reusable platform that captures sensor data, infers higher‑level context, and produces context‑aware outputs. AWARE reduces researchers’ effort in building mobile data‑logging tools, has minimal battery impact, shifts focus to data analysis, and has been evaluated in multiple case studies for power consumption and scalability.

Abstract

We present a mobile instrumentation toolkit, AWARE, an open-source effort to develop an extensible and reusable platform for capturing, inferring, and generating context on mobile devices. Mobile phones are sensor-rich but resource-constrained, and therefore several considerations need to be addressed when creating a research tool that ensures problem-free context collection. We demonstrate how AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications, with minimal battery impact. By encapsulating implementation details of sensor data retrieval and exposing the sensed context as higher-level abstractions, AWARE shifts the focus from software development to data analysis, both quantitative and qualitative. We have evaluated AWARE in several case studies and discuss its use, power consumption, and scalability.