Database Reference
In-Depth Information
SELECT
s
j
,
v
ij
FROM sensor values SAMPLE INTERVAL 1s FOR 10s
Query 2.1: Sensor data acquisition query.
push-based
pull-based
s
10
s
1
energy
efficient
s
3
s
7
s
8
s
6
s
2
s
4
s
5
s
9
user
base station
sensor network
Figure 2.3.
Push- and pull-based methods for sensor data acquisition.
2.3 Pull-Based Data Acquisition
Broadly, there are two major approaches for data acquisition: pull-
based and push-based (refer
Figure 2.3
). In the pull-based sensor data
acquisition approach, the user defines the interval and frequency of data
acquisition. Pull-based systems only follow the user's requirements, and
pull sensor values as defined by the queries. For example, using the
SAMPLE INTERVAL
clause of Query 2.1, users can specify the number of
samples and the frequency at which the samples should be acquired.
2.3.1 In-Network Data Acquisition.
This approach of sen-
sor data acquisition is proposed by TinyDB [45, 44, 43], Cougar [69] and
TiNA [58]. These approaches tightly link query processing and sensor
data acquisition. Due to the lack of space, we shall only discuss TinyDB
in this subsection.
TinyDB refers to its in-network query processing paradigm as
Acquisi-
tional Query Processing
(ACQP). Let us start by discussing how ACQP
processes Query 2.1. The result of Query 2.1 is similar to the table
shown in
Figure 2.2
. The only difference, as compared to
Figure 2.2
, is
that the result of Query 2.1 contains 10
m
rows. The naıve method of
executing Query 2.1 is to simultaneously poll each sensor for its value at
the sampling interval and for the duration specified by the query. This
method may not work due to limited range of radio communication be-
tween individual sensors and the base station.
Data Acquisition using Semantic Overlays:
TinyDB proposes a
tree-based overlay that is constructed using the sensors
S
. This tree-
based overlay is used for aggregating the query results from the leaf
nodes to the root node. The overlay network is especially built for
ecient data acquisition and query processing. TinyDB refers to its
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