This paper investigates the Cyber-Physical behavior of a user in a large indoor shopping center by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the center operators. Our analysis shows that many users exhibit high correlation between their cyber activities and physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from Wikipedia concepts and compute a contextual similarity that represents a customer’s activities with the mall context. We further show the use of cyberphysical contextual similarity in two different applications: user behavior classification and future location prediction. The experimental results demonstrate that the users’ contextual similarity significantly improves the accuracy of such applications.
@inproceedings{DBLP:conf/sensys/KaurSRCTS18,
author = {Manpreet Kaur and
Flora D. Salim and
Yongli Ren and
Jeffrey Chan and
Martin Tomko and
Mark Sanderson},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/sensys/KaurSRCTS18.bib},
booktitle = {Proceedings of the 5th Conference on Systems for Built Environments,
BuildSys 2018, Shenzen, China, November 07-08, 2018},
doi = {10.1145/3276774.3276786},
editor = {Rajesh Gupta and
Polly Huang and
Marta Gonzalez},
pages = {130--139},
publisher = {ACM},
timestamp = {Sat, 19 Oct 2019 01:00:00 +0200},
title = {Shopping intent recognition and location prediction from cyber-physical
activities via wi-fi logs},
url = {https://doi.org/10.1145/3276774.3276786},
year = {2018}
}
© 2021 Flora Salim - CRUISE Research Group.