13,776 research outputs found
Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network
Earthquake Early Warning (EEW) systems can effectively reduce fatalities,
injuries, and damages caused by earthquakes. Current EEW systems are mostly
based on traditional seismic and geodetic networks, and exist only in a few
countries due to the high cost of installing and maintaining such systems. The
MyShake system takes a different approach and turns people's smartphones into
portable seismic sensors to detect earthquake-like motions. However, to issue
EEW messages with high accuracy and low latency in the real world, we need to
address a number of challenges related to mobile computing. In this paper, we
first summarize our experience building and deploying the MyShake system, then
focus on two key challenges for smartphone-based EEW (sensing heterogeneity and
user/system dynamics) and some preliminary exploration. We also discuss other
challenges and new research directions associated with smartphone-based seismic
network.Comment: 6 pages, conference paper, already accepted at hotmobile 201
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
Group-In: Group Inference from Wireless Traces of Mobile Devices
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, 202
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