IEEE PerCrowd 2020

Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity 

Abstract:

Pervasive computing environments are characterized by a plethora 
of sensing and communication-enabled devices that diffuse 
themselves among different users. Built-in sensors and 
telecommunication infrastructure allow co-presence detection. In 
turn, co-presence detection enables context-aware 
applications, like those for social networking among close-by 
users, and for modeling human behavior. We aim to support 
developers building better context-aware applications by a 
deepened understanding of which set of context information is 
appropriate for co-presence detection. We have gathered a 
multimodal dataset for proximity sensing, including several 
proximity verification sets, like Bluetooth, Wi-Fi, and 
GSM encounters, to be able to associate sensor's data with a 
spatial granularity. We show that sensor modalities are suitable 
to recognize the spatial adjacency of users with different 
spatio-temporal granularity. We find that individual user 
mobility has only a minor, negligible effect on co-presence 
detection. In contrast, the heterogeneity of device's sensor 
hardware has a major negative impact on co-presence detection. 
To reveal energy pitfalls with respect to usability, we perform 
an energy analysis pertaining to the usage stemming from 
different sensors for co-presence detection.


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IEEE Library Access

@INPROCEEDINGS{Haus:PerCrowd2020, 
author={M. {Haus} and A. Y. {Ding} and J. {Ott}}, 
booktitle={3rd International Workshop on Context-awareness for Multi-device Pervasive and Mobile Computing}, 
title={Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity},
pages={1-7},
year={2020},
}

M. Haus, A. Y. Ding, J. Ott, "Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity", in Proceedings of IEEE 3rd International Workshop on Context-awareness for Multi-device Pervasive and Mobile Computing (PerCrowd), 2020.