JOURNAL ARTICLE

AWARE: Mobile Context Instrumentation Framework

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.

Keywords:
Computer science Instrumentation (computer programming) Context (archaeology) Scalability Mobile device Data collection Resource (disambiguation) Mobile computing Software Data logger Raw data Embedded system Data science Human–computer interaction World Wide Web Database Operating system Computer network

Metrics

416
Cited By
23.59
FWCI (Field Weighted Citation Impact)
18
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Mobile Crowdsensing and Crowdsourcing
Physical Sciences →  Computer Science →  Computer Science Applications
IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications

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