Real-world events often occur in response to some environmental
change. For example, a person entering a room is often correlated
with changes in light or motion, or a flower’s opening with
the presence or absence of sunlight. Multi-modal sensor networks
can use these correlations by triggering a secondary sensor based
on the status of another, in effect nesting one query inside another.
Reducing the duty cycle of some sensors can reduce overall energy
consumption (if the secondary sensor consumes more energy
than the initialsensor, for example as an accelerometer triggering a
GPS receiver) and network traffic (for example, a triggered imager
generates much less traffic than a constant video stream). Alternatively,
in-network processing might choose the best application
of a sparse resource (for example, a motion sensor triggering a
steerable camera).
Real-world events often occur in response to some environmentalchange. For example, a person entering a room is often correlatedwith changes in light or motion, or a flower’s opening withthe presence or absence of sunlight. Multi-modal sensor networkscan use these correlations by triggering a secondary sensor basedon the status of another, in effect nesting one query inside another.Reducing the duty cycle of some sensors can reduce overall energyconsumption (if the secondary sensor consumes more energythan the initialsensor, for example as an accelerometer triggering aGPS receiver) and network traffic (for example, a triggered imagergenerates much less traffic than a constant video stream). Alternatively,in-network processing might choose the best applicationof a sparse resource (for example, a motion sensor triggering asteerable camera).
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