Publication

@inproceedings{ieee_itsc_mimosense,

title = {MiMoSense: An Open Crowdsensing Platform for Micro-Mobility},

author = {Zengyi Han and Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://2021.ieee-itsc.org/},

doi = {10.1109/ITSC48978.2021.9564524},

year  = {2021},

date = {2021-09-19},

booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},

pages = {1-6},

publisher = {IEEE},

abstract = {The use of micro-mobility (e.g., bicycle and scooter) and their data for urban sensing and rider assessment is becoming increasingly popular in research. However, different research topics require different sensor setups; no general data collecting tools for the micro-mobility makes the researcher who wishes to collect data has to build their own collecting system from scratch. To this end, we present MiMoSense, an open crowdsensing platform for micro-mobility. MiMoSense consists of two components: (1) MiMoSense server, which is set up on the cloud, and used to manage sensing studies and the collected data for research and sharing. (2) MiMoSense client, uses micro-mobility carrying various sensors and IoT devices to collect multiple kinds of data during traveling. As a reusable open-source software, MiMoSense shifts the researcher's focus from software development to sensing data analysis; it can help researchers quickly develop an extensible platform for collecting micro-mobility's raw sensing data and inferring traveling context. We have evaluated MiMoSense's battery consumption, message latency and discuss its use.},

keywords = {e-scooter, Micro-Mobility, Mobile Sensing Toolkit},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}