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26882 Proximity Graphs for Crowd Movement Sensors
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Chilipirea, C. and Petre, A. and Dobre, C. and van Steen, M. (2015) Proximity Graphs for Crowd Movement Sensors. In: Proceedings of the 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2015), 3-6 Nov 2015, Krakow, Poland. pp. 310-314. IEEE Computer Society. ISBN 978-1-4673-9473-4

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Official URL: http://dx.doi.org/10.1109/3PGCIC.2015.147

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Abstract

Sensors are now common, they span over different applications, different purposes and some over large geospatial areas. Most data produced by these sensors needs to be linked to the physical location of the sensor itself. By using the location of a sensor we can construct (mathematically) proximity graphs that have the sensors as nodes. These graphs have a wide variety of applications including visualization, packet routing, and spatial data analysis. We consider a sensor network that measures detections of WiFi packets transmitted by devices, such as smartphones. One important feature of sensors is given by the range in which they can gather data. Algorithms that build proximity graphs do not take this radius into account. We present an approach to building proximity graph that takes sensor position and radius as input. Our goal is to construct a graph that contains edges between pairs of sensors that are correlated to crowd movements, reflecting paths that individuals are likely to take. Because we are considering crowd movement, it gives us the unique opportunity to construct graphs that show the connections between sensors using consecutive detections of the same device. We show that our approach is better than ones that are based on the positioning of sensors only.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:COMMIT/EWIDS: Very large wireless sensor networks for well-being
ID Code:26882
Status:Published
Deposited On:17 March 2016
Refereed:Yes
International:Yes
More Information:statisticsmetis

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