University of Oulu

Nishiyama Y. et al. (2020) IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform. In: Streitz N., Konomi S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2020. Lecture Notes in Computer Science, vol 12203. Springer, Cham. https://doi.org/10.1007/978-3-030-50344-4_17

IOS crowd–sensing won’t hurt a bit! : AWARE framework and sustainable study guideline for iOS platform

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Author: Nishiyama, Yuuki1; Ferreira, Denzil2; Eigen, Yusaku3;
Organizations: 1The University of Tokyo, Japan
2University of Oulu, Finland
3Keio University, Japan
4University of Washington, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020112392284
Language: English
Published: Springer Nature, 2020
Publish Date: 2020-11-23
Description:

Abstract

The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-030-50344-4
ISBN Print: 978-3-030-50343-7
Pages: 223 - 243
DOI: 10.1007/978-3-030-50344-4_17
OADOI: https://oadoi.org/10.1007/978-3-030-50344-4_17
Host publication: Distributed, Ambient and Pervasive Interactions : 8th International Conference, DAPI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings
Host publication editor: Streitz, Norbert
Konomi, Shin’ichi
Conference: International Conference on Human-Computer Interaction
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
iOS
Funding: This work was supported by JSPS KAKENHI Grant Number JP18K11274.
Copyright information: © Springer Nature Switzerland AG 2020. This is a post-peer-review, pre-copyedit version of an article published in Distributed, Ambient and Pervasive Interactions : 8th International Conference, DAPI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-50344-4_17.