Image Processing Reference
In-Depth Information
Query attributes
Duration
Scope
Result
Snapshot
Long-running
Range
All
Raw
Aggregated
FIGURE .
Attributes specified within a query.
aboveacertainthreshold,orifasensorislocatedinaspeciicareaorataparticulartime
of the day. These constraints may also be specified in various combinations. Note that
“scope” may not necessarily be a static parameter such as location or sensor type. It could
be a dynamic parameter as well such as temperature. For example, if we consider a static
network in an outdoor environment, a query that requires temperature readings from a
particular region will always result in readings coming from the same part of the network
regardless of the time of the day. However, if a user requires humidity readings only if the
temperature goes above a particular threshold, the query might result in different sets of
sensors returning readings depending on the time of the day. Event-based queries make
use of the “scope” parameter as they make use of the temporal constraint, i.e., instead of
returning data all the time, data is sent to the user only when a particular event occurs.
Conversely, a user may require all the readings from the network.
. Result : he “result” attribute refers to the format of the data that is requested by the user.
hedatacaneitherbein“raw”format—inwhichcasetheuserrequireseveryreading
sampled by the sensors chosen by the “scope” attribute, or the user may require aggregated
data, e.g., the average of all the chosen sensors.
Table . gives an overview of how the some of the significant projects/papers in the field of dis-
tributed data extraction address the three attributes described above. Note that the table illustrates
which attributes the particular method is specifically designed for. For example, we have stated in
the table that TinyDB supports snap-shot queries. However, in reality, TinyDB can also accept long-
running queries. But the optimizations built into TinyDB are specifically geared toward snap-shot
TABLE .
A Summary of How Query Attributes Are Managed by Different Projects
Duration
Scope
Result
Name of the Project/Mechanism/Author
Snapshot
Long-Running
Range
All
Raw
Aggregated
Directed diffusion []
Cougar []
TinyDB []
TiNA []
Deligiannakis et al. []
Bonfils et al. []
Coman et al. []
REED []
BBQ []
Ken []
TD-DES []
DTA []
AI-LMAC []
GHT []
DIFS []
DIM []
DOSA []
PAQ []
Chatterjea et al. []
 
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