09-10-2014, 10:55 AM
Congestion Detection for Video Traffic in Wireless
Sensor Networks
Congestion Detection.pdf (Size: 318 KB / Downloads: 21)
Abstract
— Congestion control mechanisms include three phases:
congestion detection, congestion notification and rate adjustment.
So far diverse congestion detection methods for sensor networks
are proposed. In this paper we introduce numerous congestion
detection parameters and examine them in various respects;
finally we choose one of them as the best parameter for video
traffic in wireless sensor networks. Some of intended criteria for
comparing the parameters are cost, relation to quality of video,
locality or being global in the network, accuracy and speed of
congestion detection. We simulated and concluded that average
delay is the most suitable parameter for congestion detection in
these networks
INTRODUCTION
Advent of new technologies in sensors, camera and
microphones in smaller scales and with lower energy
consumption led to the design of wireless sensors with the
ability to sense their around environment. Nowadays there is
widespread research in this area and new applications of these
sensors are becoming popular. An example of these
applications is utilization of these sensors to monitor around
environment that led to the advent of Wireless Multimedia
Sensor Networks (WMSNs) [1]. Designing these networks has
many challenges such as nature of wireless media and
multimedia information transmission. Consequently traditional
mechanisms for network layers are no longer acceptable or
applicable for these networks. Transport layer is one of the
main layers in WMSNs which has greatly influenced the
overall performance of received packets. This is because of
limited bandwidth, high data transmission rate, burst nature of
this multimedia traffic and high effect of congestion on these
packets [2]. Proposed methods for transport layer of sensor
networks are classified in two categories [3
CONGESTION DETECTION
So far, various methods are proposed in WSNs each of
which using one parameter for congestion detection. Selecting
this parameter is based on various factors, some of which
being: network structure, data transmission rate, traffic
pattern, congestion probability, type of network applications
and QoS requirements of them, congestion effect on
applications and network resources [3].
Table 1 shows congestion detection parameters along with
their related protocols. Besides, it is illustrated that which
parameter is applied to what type of nodes. In each method to
make out which node is in charge of congestion detection is
dependent on the parameter nature. For example using queue
length is tapped only in intermediate nodes and in networks
with end to end retransmission, retransmission time is only
used in Sink to detect congestion. Naturally parameters that
are applicable to all of nodes are more flexible since
depending on network conditions and requirements it is
possible to determine location of detecting congestion. For
example, if speed of congestion detection is at question, we
can do it in intermediate nodes and if reducing load of sensor
nodes is aimed, it is possible to use sink node for congestion
detection
Locality or globality of parameter
Among parameters of congestion detection, some of
parameters detect congestion only through local information
that is available in node. For example when queue length is
used, each of the nodes detects congestion only based on its
own queue length. But some other parameters use more
information and do not rely on their own information. Among
them, delay parameters use the sum of delay of all nodes in the
path. Needless to say that parameters that use global
information are better than those using local information. In
TABLE IV. we classified parameters based on this criteria.
CONCLUTION
According to what preceded in this paper we conclude that
average delay is the best parameter for congestion detection
for video and other multimedia traffic in WMSNs. Advantages
of using this parameter are as follows:
It has lower cost for congestion detection and besides has a
direct effect on quality of received video. Delay parameter not
only uses local information but also it considers the whole
network status. Furthermore it is accurate in congestion
detection and it quickly detects congestion in network. The
other advantage is that it can be used in either sink or in
intermediate nodes. This leads to more flexibility in usage.
When quick congestion detection is aimed we may use
intermediate nodes to detect and control congestion and if
reducing intermediate nodes overhead is favorable we can set
sink in charge.
One of the disadvantages of delay parameter is the
overhead of synchronization between nodes. We did not
consider this synchronization because this is not a major issue.
Delay can be simulated with some heuristics and it can
become independent of synchronization. So this is left for
future work on the problem