Publication

@inproceedings{10.1145/3410530.3414369,

title = {Using IOS for Inconspicuous Data Collection: A Real-World Assessment},

author = {Yuuki Nishiyama and Denzil Ferreira and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

url = {https://doi.org/10.1145/3410530.3414369

https://github.com/tetujin/AWAREFramework-iOS},

doi = {10.1145/3410530.3414369},

isbn = {9781450380768},

year  = {2020},

date = {2020-09-12},

booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},

pages = {261–266},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Mexico},

series = {UbiComp-ISWC '20},

abstract = {Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application.},

keywords = {Effective Data Collection, iOS, Mobile Crowd sensing, Mobile Sensing Toolkit, Real-world Assessment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-50344-4_17,

title = {iOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform},

author = {Yuuki Nishiyama and Denzil Ferreira and Yusaku Eigen and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

editor = {Norbert Streitz and Shiníchi Konomi},

url = {https://www.yuukinishiyama.com/2020/07/25/hcii2020/

https://www.yuukinishiyama.com/wp-content/uploads/2020/07/AWARE-iOS_HCII2020_preprint.pdf

https://github.com/tetujin/aware-client-ios-v2

https://github.com/tetujin/AWAREFramework-iOS},

doi = {10.1007/978-3-030-50344-4_17},

isbn = {978-3-030-50344-4},

year  = {2020},

date = {2020-07-10},

booktitle = {Distributed, Ambient and Pervasive Interactions},

pages = {223--243},

publisher = {Springer International Publishing},

address = {Cham},

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.},

keywords = {Data Collection Rate, Guideline, iOS, Mobile Sensing Framework, Sustainable Sensing},

pubstate = {published},

tppubtype = {inproceedings}

}