Exploiting correlations for efficient content-based sensor search

R. Mietz, K. Römer


Billions of sensor (e.g., in mobile phones or tablet pcs) will be connected to a future Internet of Things (IoT), offering online access to the current state of the real world. A fundamental service in the IoT is search for places and objects with a certain state (e.g., empty parking spots or quiet restaurants). We address the underlying problem of efficient search for sensors reading a given current state - exploiting the fact that the output of many sensors is highly correlated. We learn the correlation structure from past sensor data and model it as a Bayesian Network (BN). The BN allows to estimate the probability that a sensor currently outputs the sought state without knowing its current output. We show that this approach can substantially reduce remote sensor readouts.
Original languageEnglish
Title of host publicationSENSORS, 2011 IEEE
Number of pages4
Publication date01.10.2011
ISBN (Print)978-1-4244-9290-9
ISBN (Electronic)978-1-4244-9289-3
Publication statusPublished - 01.10.2011
Event10th IEEE SENSORS Conference 2011 - Limerick, Ireland
Duration: 28.10.201131.10.2011


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