Abstract
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 language | English |
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Title of host publication | SENSORS, 2011 IEEE |
Number of pages | 4 |
Publisher | IEEE |
Publication date | 01.10.2011 |
Pages | 187-190 |
ISBN (Print) | 978-1-4244-9290-9 |
ISBN (Electronic) | 978-1-4244-9289-3 |
DOIs | |
Publication status | Published - 01.10.2011 |
Event | 10th IEEE SENSORS Conference 2011 - Limerick, Ireland Duration: 28.10.2011 → 31.10.2011 |