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.
Nishiyama Yuuki, Ferreira Denzil, Eigen Yusaku, Sasaki Wataru, Okoshi Tadashi, Nakazawa Jin, Dey Anind K., Sezaki Kaoru
A4 Article in conference proceedings
Place of 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
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
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