TY - GEN
T1 - IOS Crowd–Sensing Won’t Hurt a Bit!
T2 - 8th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Nishiyama, Yuuki
AU - Ferreira, Denzil
AU - Eigen, Yusaku
AU - Sasaki, Wataru
AU - Okoshi, Tadashi
AU - Nakazawa, Jin
AU - Dey, Anind K.
AU - Sezaki, Kaoru
N1 - Funding Information:
supported by JSPS KAKENHI Grant Number
Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP18K11274.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Data collection rate
KW - Guideline
KW - Mobile sensing framework
KW - Sustainable sensing
KW - iOS
UR - http://www.scopus.com/inward/record.url?scp=85088742762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088742762&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50344-4_17
DO - 10.1007/978-3-030-50344-4_17
M3 - Conference contribution
AN - SCOPUS:85088742762
SN - 9783030503437
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 223
EP - 243
BT - Distributed, Ambient and Pervasive Interactions - 8th International Conference, DAPI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Streitz, Norbert
A2 - Konomi, Shin’ichi
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -