Latest revision as of 18:57, 28 January 2021

Abstract

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.

Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 2020

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/2005.12848

• http://arxiv.org/pdf/2005.12848

• https://zenodo.org/record/4072930

• http://xplorestaging.ieee.org/ielx7/9108342/9110948/09111012.pdf?arnumber=9111012,

http://dx.doi.org/10.1109/ipsn48710.2020.00-38

• https://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-12848,

https://arxiv.org/pdf/2005.12848,

https://arxiv.org/abs/2005.12848,

https://ui.adsabs.harvard.edu/abs/2020arXiv200512848S/abstract,

https://academic.microsoft.com/#/detail/3035706453