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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Poster: Understanding Mobile User Interactions with the IoT
AU - Mikusz, Mateusz
AU - Bates, Oliver
AU - Clinch, Sarah
AU - Davies, Nigel
AU - Friday, Adrian
AU - Noulas, Anastasios
PY - 2016/6/30
Y1 - 2016/6/30
N2 - The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a "physical analytics cookie" could raise significant privacy concerns.However, in many cases a more "human-centric" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights.
AB - The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a "physical analytics cookie" could raise significant privacy concerns.However, in many cases a more "human-centric" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights.
U2 - 10.1145/2938559.2938607
DO - 10.1145/2938559.2938607
M3 - Conference contribution/Paper
SN - 9781450344166
SP - 140
EP - 140
BT - MobiSys '16 Companion Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion
PB - ACM
CY - New York
T2 - 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys)
Y2 - 25 June 2016 through 30 June 2016
ER -