18-05-2012, 12:31 PM
Adaptive image transfer for wireless sensor networks (WSNs)
[attachment=22337]
INTRODUCTION
Recently wireless sensor network (WSN) has become
one of the most interesting networking technologies since it
can be deployed without communication infrastructures [1].
The WSNs are based on small sensor nodes and a sink
(figure1). The main characteristic of such networks is nodes
with scarce resources. These nodes consist of sensing, data
processing, and communication components. So, sensor
nodes are embedded system witch sense their environment,
collect sensed data and transmit it to the sink in an
autonomous way using multi-hop communication. However,
they are energized by small and irreplaceable batteries. Under
such energy constraint condition, sensor nodes can only
transmit a finite number of bits in their lifetime.
Consequently, energy consumption and data transmission are
always considered together in WSNs.
RELATED WORK
Image compression is a well-established research
field, but sensor networks present a context in which new
design issues have to be addressed. The main characteristic of
such networks is nodes with scarce resources. In fact, the
primary focus on energy, computational power and allocated
memory call for new approaches. In the case, a large variety
of compression algorithms have been proposed. The image
compression techniques and processing algorithms in a
wireless sensor network are classified in two categories:
Local processing and compression
Distributed processing and compression
Local algorithms are useful only when the complete
processing, including image compression and transmission, is
less energy consumming than the single transmission of
uncompressed image. Some works have demonstrated that
the complexity of certain compression algorithms leads to
greater power consumptions than the simple transmission of
the uncompressed image. For instance, Ferrigno et al
presented in [13] a platform to evaluate the performance of
different traditional algorithms for image compression in a
single sensor node.
GENERAL ARCHITECTURE OF A WIRELESS SENSOR NODE
the architecture of a typical wireless
sensor node, as usually assumed in the literature. It consists
of four main components: (i) a sensing unit including one or
more sensors and an analog-to-digital converters for data
acquisition; (ii) a processing unit including a micro-controller
and memory for local data processing; (iii) a radio subsystem
for wireless data communication (RF unit); and (iv) a power
supply unit. Depending on the specific application, sensor
nodes may also include additional components which are
optional such as a location finding system to determine their
position, a mobilizer to change their location or
configuration.
IMAGE PROCESSING IN WSNs
As the radio subsystem is one of the most power
consuming parts in sensors node, it is obvious that reducing
transmitted data will save energy. However, the most evident
solution is the image compression. The purpose of image
compression is to reduce the number of bits needed to
represent an image by removing the spatial and spectral
© Two level
(d) Tree level decomposition
redundancies as much as possible. In this paper, the proposed
image transmission scheme is based on wavelet image
transform.
[attachment=22337]
INTRODUCTION
Recently wireless sensor network (WSN) has become
one of the most interesting networking technologies since it
can be deployed without communication infrastructures [1].
The WSNs are based on small sensor nodes and a sink
(figure1). The main characteristic of such networks is nodes
with scarce resources. These nodes consist of sensing, data
processing, and communication components. So, sensor
nodes are embedded system witch sense their environment,
collect sensed data and transmit it to the sink in an
autonomous way using multi-hop communication. However,
they are energized by small and irreplaceable batteries. Under
such energy constraint condition, sensor nodes can only
transmit a finite number of bits in their lifetime.
Consequently, energy consumption and data transmission are
always considered together in WSNs.
RELATED WORK
Image compression is a well-established research
field, but sensor networks present a context in which new
design issues have to be addressed. The main characteristic of
such networks is nodes with scarce resources. In fact, the
primary focus on energy, computational power and allocated
memory call for new approaches. In the case, a large variety
of compression algorithms have been proposed. The image
compression techniques and processing algorithms in a
wireless sensor network are classified in two categories:
Local processing and compression
Distributed processing and compression
Local algorithms are useful only when the complete
processing, including image compression and transmission, is
less energy consumming than the single transmission of
uncompressed image. Some works have demonstrated that
the complexity of certain compression algorithms leads to
greater power consumptions than the simple transmission of
the uncompressed image. For instance, Ferrigno et al
presented in [13] a platform to evaluate the performance of
different traditional algorithms for image compression in a
single sensor node.
GENERAL ARCHITECTURE OF A WIRELESS SENSOR NODE
the architecture of a typical wireless
sensor node, as usually assumed in the literature. It consists
of four main components: (i) a sensing unit including one or
more sensors and an analog-to-digital converters for data
acquisition; (ii) a processing unit including a micro-controller
and memory for local data processing; (iii) a radio subsystem
for wireless data communication (RF unit); and (iv) a power
supply unit. Depending on the specific application, sensor
nodes may also include additional components which are
optional such as a location finding system to determine their
position, a mobilizer to change their location or
configuration.
IMAGE PROCESSING IN WSNs
As the radio subsystem is one of the most power
consuming parts in sensors node, it is obvious that reducing
transmitted data will save energy. However, the most evident
solution is the image compression. The purpose of image
compression is to reduce the number of bits needed to
represent an image by removing the spatial and spectral
© Two level
(d) Tree level decomposition
redundancies as much as possible. In this paper, the proposed
image transmission scheme is based on wavelet image
transform.