Publication

@inproceedings{kitazato2018study,

title = {A Study of the Detection of Pedestrian Flow Using Bluetooth Low Energy},

author = {Tomoya Kitazato and Masaki Ito and Kaoru Sezaki},

doi = {10.1109/PERCOMW.2018.8480336},

year  = {2018},

date = {2018-10-08},

booktitle = {2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},

pages = {716--721},

publisher = {IEEE},

address = {Athens, Greece},

organization = {IEEE},

abstract = {Analysis of human mobility data provide us many important insights. For example, a detailed analysis of human mobility in urban space can provide important insights into gathering events. This insight is useful when addressing urban planning and public safety issues, and serves as a powerful tool for solving traffic congestion, early detection of social unrest, and so on. The analysis of human mobility in an indoor space such as in a museum exhibition, can assist us in anticipating the behavior of visitors; and in the early recognition of potential problems, such as the buildup of foot traffic at specific points. However, there is no universally accepted method for easily sensing human mobility. To address this problem, we developed a novel method to detect pedestrian flow using Bluetooth Low Energy (BLE). Our approach is based on the assumption that a BLE beacon is always affixed to the pedestrian. Thus, the individual's velocity can be readily determined by analyzing the Received Signal Strength Indicator (RSSI) of their BLE beacon. Apart from velocity, the direction of the pedestrian can also be determined by detecting their BLE beacon with multiple sensors. In this investigation, we evaluated the proposed method using both experimental real-world data and simulations. Participants with BLE beacons walked straight in a hall while the RSSI of their beacons was monitored from a moving sensor. This information was used to estimate the velocities of the beacons. We also simulated the RSSIs of the beacons and estimated their velocities under various conditions. Our results indicate that the proposed method can precisely detect the velocities of pedestrians.},

keywords = {Bluetooth, Directive antennas, Global Positioning System, Laser radar, Proposals, sensors, Wireless fidelity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{6483674,

title = {Limited Negative Surveys: Privacy-preserving participatory sensing},

author = {Shunsuke Aoki and Masayuki Iwai and Kaoru Sezaki},

doi = {10.1109/CloudNet.2012.6483674},

isbn = {978-1-4673-2797-8},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), 28-30 Nov. 2012, Paris, France},

journal = {2012 1st IEEE International Conference on Cloud Networking, CLOUDNET 2012 - Proceedings},

pages = {158-160},

abstract = {Participatory sensing is a crowd-sourcing technique that relies on participants' active contribution. By aggregating data sets from each participant, we can collect statistical information of the environment or phenomenona in the cloud. To promote use of participatory sensing in healthcare, research, and other useful applications, the protection of privacy is important to consider. The invasion of privacy in participatory sensing would have dire consequences because mobile phone used by these applications have sensitive data about users daily life. In this paper, we suggest a privacy-preserving participatory sensing scheme for real-world data sets with large numbers of categories by using Limited Negative Surveys. By using our method described in this paper, the server can reconstruct the probability distributions of the original distributions of sensed values without knowing the personal information of citizens. Furthermore, our research has the capablity to change, according to the features of data, especially the number of categories. We evaluate how this scheme of aggregate information can be still useful while protecting the privacy of users' original data.},

keywords = {Base stations, Data privacy, Educational institutions, Mobile handsets, Privacy, Protocols, sensors},

pubstate = {published},

tppubtype = {inproceedings}

}