TY - GEN
T1 - Real-time intragroup familiarity analysis model using beacon based on proximity
AU - Choi, Jung In
AU - Yong, Hwan Seung
N1 - Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A2003764)
Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/7
Y1 - 2016/12/7
N2 - An analysis of the familiarity between users in a group requires large amounts of information. We could determine the degree of familiarity by using personal information gleaned from a social networking service. For a realtime service, we usually use video data. Unfortunately, this data is closely related to a user's privacy, so the user may feel uncomfortable about its use. Therefore, in this study, we set out to devise a real-time familiarity analysis model using a minimal amount of information and a Bluetooth low-energy beacon. Unlike the traditional approach, the devices receiving the beacon signal are placed on a desk, wall, or ceiling and the user carries a beacon. The beacon transmits only its ID and a received signal strength indication (RSSI) signal. Using the device for receiving the beacon signal, a user's location can be monitored so that the server can analyze the intragroup and calculate the degree of familiarity between users. This study addressed those situations arising in a party-like event, in a school, in a company, etc. to attempt to analyze the degree of familiarity by determining a person's location at specific times. This technology could also be applied to exhibitions, parks, and amusement parks to determine the most popular exhibits, spots, and facilities in real time.
AB - An analysis of the familiarity between users in a group requires large amounts of information. We could determine the degree of familiarity by using personal information gleaned from a social networking service. For a realtime service, we usually use video data. Unfortunately, this data is closely related to a user's privacy, so the user may feel uncomfortable about its use. Therefore, in this study, we set out to devise a real-time familiarity analysis model using a minimal amount of information and a Bluetooth low-energy beacon. Unlike the traditional approach, the devices receiving the beacon signal are placed on a desk, wall, or ceiling and the user carries a beacon. The beacon transmits only its ID and a received signal strength indication (RSSI) signal. Using the device for receiving the beacon signal, a user's location can be monitored so that the server can analyze the intragroup and calculate the degree of familiarity between users. This study addressed those situations arising in a party-like event, in a school, in a company, etc. to attempt to analyze the degree of familiarity by determining a person's location at specific times. This technology could also be applied to exhibitions, parks, and amusement parks to determine the most popular exhibits, spots, and facilities in real time.
KW - Bluetooth low-energy beacon
KW - familiarity analysis
KW - indoor positioning
KW - intragroup analysis
UR - http://www.scopus.com/inward/record.url?scp=85010297878&partnerID=8YFLogxK
U2 - 10.1109/UEMCON.2016.7777869
DO - 10.1109/UEMCON.2016.7777869
M3 - Conference contribution
AN - SCOPUS:85010297878
T3 - 2016 IEEE 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2016
BT - 2016 IEEE 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2016
A2 - Saha, Himadri Nath
A2 - Chakrabarti, Satyajit
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2016
Y2 - 20 October 2016 through 22 October 2016
ER -