On heterogeneity in mobile sensing applications aiming at representative data collection

Authors:
Henrik Blunck;Niels Olof Bouvin;Tobias Franke;Kaj Grønbæk;Mikkel B. Kjaergaard;Paul Lukowicz;Markus Wüstenberg
Affiliations:
Aarhus University, Aarhus, Denmark;Aarhus University, Aarhus, Denmark;German Research Center for Artificial Intelligence, Kaiserslautern, Germany;Aarhus University, Aarhus, Denmark;Aarhus University, Aarhus, Denmark;German Research Center for Artificial Intelligence, Kaiserslautern, Germany;Aarhus University, Aarhus, Denmark
Venue:
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Year:
2013

Citing 19
Cited 0

Using the Experience Sampling Method to Evaluate Ubicomp Applications

IEEE Pervasive Computing
Mobiscopes for Human Spaces

IEEE Pervasive Computing
The BikeNet mobile sensing system for cyclist experience mapping

Proceedings of the 5th international conference on Embedded networked sensor systems
Understanding human-battery interaction on mobile phones

Proceedings of the 9th international conference on Human computer interaction with mobile devices and services
Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

Proceedings of the 6th ACM conference on Embedded network sensor systems
UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cooperative Techniques Supporting Sensor-Based People-Centric Inferencing

Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
EnTracked: energy-efficient robust position tracking for mobile devices

Proceedings of the 7th international conference on Mobile systems, applications, and services
Understanding transportation modes based on GPS data for web applications

ACM Transactions on the Web (TWEB)
GreenGPS: a participatory sensing fuel-efficient maps application

Proceedings of the 8th international conference on Mobile systems, applications, and services
Research in the large. using app stores, markets, and other wide distribution channels in Ubicomp research

Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
A survey of mobile phone sensing

IEEE Communications Magazine
Energy-efficient trajectory tracking for mobile devices

MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Understanding human-smartphone concerns: a study of battery life

Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Farther than you may think: an empirical investigation of the proximity of users to their mobile phones

UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Common sense community: scaffolding mobile sensing and analysis for novice users

Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
What is keeping my phone awake?: characterizing and detecting no-sleep energy bugs in smartphone apps

Proceedings of the 10th international conference on Mobile systems, applications, and services
Inferring Crowd Conditions from Pedestrians' Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings

WETICE '12 Proceedings of the 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Computational environmental ethnography: combining collective sensing and ethnographic inquiries to advance means for reducing environmental footprints

Proceedings of the fourth international conference on Future energy systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gathering representative data using mobile sensing to answer research questions is becoming increasingly popular, driven by growing ubiquity and sensing capabilities of mobile devices. However, there are pitfalls along this path, which introduce heterogeneity in the gathered data, and which are rooted in the diversity of the involved device platforms, hardware, software versions and participants. Thus, we, as a research community, need to establish good practices and methodologies for addressing this issue in order to help ensure that, e.g., scientific results and policy changes based on collective, mobile sensed data are valid. In this paper, we aim to inform researchers and developers about mobile sensing data heterogeneity and ways to combat it. We do so via distilling a vocabulary of underlying causes, and via describing their effects on mobile sensing---building on experiences from three projects within citizen science, crowd awareness and trajectory tracking.