Hybrid Cluster Based Congestion Aware (HCBCA) Approaches in Wireless Sensor Network

.
Hybrid Cluster Based Congestion Aware (HCBCA) Approaches
in Wireless Sensor Network
V.Perumal
Ph.D Research Scholar,
Department of Computer Science,
Erode Arts & Science College(Autonomous),
Erode, Tamilnadu, India.
Email:rishiperumal89@gmail.com
Dr.K.Meenakshi Sundaram
Associate professor,
Department of Computer Science,
Erode Arts & Science College (Autonomous),
Erode,Tamilnadu,India.
Email: lecturerkms@yahoo.com
ABSTRACT
In most efficient clustering technique for WSN has been proved as a congestion control and hierarchical
based cluster head selection process. The cluster head reduces the energy wastage and additionally that improves
the receiving of data and collection of data from their member sensor nodes. Also transmitting the collected data
to the base station (BS). In proposed method hybrid cluster based congestion aware (HCBCA) is mostly focused
on traffic that affects the continuous flow of data, Arrival of data from the source to destination delay time,
Avoid packet losses and energy consumption process. Mainly congestion happens in the intra cluster to do the
process of transmitting the destination of packets in many to one manner form sensor node to CH. The main
reason for occurrence of congestion is communication path, nodes energy level and nodes buffer size. When
these above it are successful done the congestion, does not exist or otherwise congestion will occur. The purpose
of WSN congestion control is to improve the packet delivery ratio and energy consumption.
Keywords: Sensor node, Hybrid Cluster, Congestion Avoidance, WSN.
I.INTRODUCTION
In Wireless Sensor Network (WSN) the
sensor nodes are usually scattered over a sensor
field and are capable of sensing, processing and
transmitting to the base station, based on the
requirement application. The major constraints of
WSNs are the limited power sources of the sensor
nodes. The battery operated sensors are often
deployed in an unattended hostile environment, so
replacement of their battery is almost impossible
which make the sensor node energy constraint.
Clustering sensor node is one of the most
effective techniques which is employed to conserve
energy of sensor node. In the process of clustering
the network is divided into many groups, called
Cluster Head (CH). CH responsible for collecting
the data from their members sensor nodes within
the clusters, aggregate them and send it to a remote
base station (BS) directly or through other CHs.
The base station is connected to a public network
such as internet for public notification of the event.
The congestion generally whilst a sensor node
utilized as a relay node for multiple flows. Another
possible reason of congestion is the unfair
distribution of data traffic in the network. The
possible effect of unfair traffic utilization will result
in unstable paths that can overload the nodes and
soon deplete the energy of some sensor nodes,
which consequently partition the network [1][2].
In several aspects based on congestion
possibly will happen, such as contention due to
concurrent transmission, overflow in buffers and
time varying wireless channel condition [2]. The
congestion can occur while collecting the data and
sending it towards the central location over the
WSN. Congestion happens mainly in the sensor to
base station direction. When packets are transported
is a many to one manner. It has negative impact of
on network performance and application objective
indiscriminate packet losses, increased packet
delay, wastage node energy and severe fidelity
degradation [3].
The congestion organizes technique in
WSN are classified under two categories: Link
level congestion and node level congestion. Node
level congestion arises from buffer overflow in the
node, which results in packet loss. The link level
congestion is related to wireless channels shared by
several nodes through competitive MAC layer
protocol. Link level congestion control can achieve
by using multiple access technique such as CSMA,
FDMA, TDMA and CDMA to prevent congestion
by exercising light degree buffer management [4].
The most challenging congestion
mechanisms are congestion Avoidance, detection
and alleviation. The congestion avoidance is
referring to as proactively routing protocol plays an
important role to select best nodes and to route the
International Journal of Computer Science and Information Security (IJCSIS),
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ISSN 1947-5500

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data traffic from the source to destination.
Congestion detection in a timely manner during
data forwarding, sensor nodes monitor the buffer
occupancy and the channel utilization. On the other
hand, congestion alleviation schemes control
congestion reactively either by adjusting the source
traffic rate or by re-discovering the new route. All
these three mechanisms are able to increase the
performance and to balance the traffic load in
multi-hop WSNs.
Congestion avoidance when a source node
is triggered by the application; the first step that is
performed by the sensor node is to check the
availability of route to the desired location through
a check route availability process. Congestion
avoidance is measuring the data accuracy and data
redundancy. Congestion detection process monitors
the state of the node and the link between the nodes
in initiated in order to detect congestion. If the node
or link between the nodes that it will be congested
in the near future, then the process to notify the
source or precursor node is triggered. The
congestion notification process is invoked by the
sensor node when congestion or low energy is
detected. It measures by an aggregated of two
matrices: buffer occupancy and channel utilization.
Congestion alleviation is activated in ripple search
based when a sensor node receiving a notification
message. In this process of congested node or link
is bypassed in order to maintain a route. Another
procedure to alleviate congestion is to Re-route the
traffic to an alter route congestion aware and energy
efficient route. It measured, unlike the resource
control and traffic control that it will alleviate
congestion by adjusting the traffic rate at the source
node or intermediate nodes [5][6].
II.RELATED WORKS
Azlan Awang et al [1]. Congestion-aware
energy efficient and traffic Load Balancing Scheme
(CLS) for routing in WSNs is proposed. This
scheme utilizes the ignored information during the
route discovery process and considers a composite
metric that incorporates the consumed energy E,
participation level P of the node and signal strength
S of the link between the nodes. In addition, a
separate field is maintained in the packet for each
routing metric in the case of multiple metrics that
might overload the node. In this paper, a new
congestion aware, energy efficient and traffic load
balancing scheme (CLS) for routing has been
designed. The proposed scheme compares the
proposed routing metric over a new route discovery
mechanism, using weighted additive composition
approach and lexical approach. The optimum next
hop is selected based on a combination of three
different metrics such as energy E, participation
level P and signal strength S during forward route
formation. Using this approach, a least congested
and an energy efficient route is discovered that
maintains the minimum routing information.
Furthermore, this approach increases the PDR,
decreases the energy consumption and an ETE
delay of the entire network.
Srinivasan et al [2]. proposed an energy
efficient cluster head selection algorithm which is
based on particle swarm optimization (PSO) called
PSO-ECHS. The algorithm is developed with an
efficient scheme of particle encoding and fitness
function. For the energy efficiency of the proposed
PSO approach, we consider various parameters
such as intra-cluster distance, sink distance and
residual energy of sensor nodes. We also present
cluster formation in which non cluster sensor nodes
join their CHs based on derived weight function.
The algorithm is tested extensively on various
scenarios of WSNs, varying number of sensor
nodes and the CHs.
Raheleh Hashemzehi et al [3]. The
Suggested The unique characteristics of WSN such
as coherent nature of traffic to base station that
occurs through its many-to-one topology and
collision in physical channel are main reasons of
congestion in wireless sensor networks. Also, when
sensor nodes inject sensory data into network the
congestion is possible. Congestion affects the
continuous flow of data, loss of information, delay
in the arrival of data to the destination and
unwanted consumption of significant amount of the
very limited amount of energy in the nodes.
Therefore, Congestion in wireless sensor networks
(WSN) needs to be controlled in order to prolong
system lifetime improve fairness, high energy-
efficiency, and improve quality of service (QoS). It
has mainly described the characteristic and the
content of congestion control in wireless sensor
network and surveys the research related to the
Congestion control protocols for WSNs.
Chia-Hsu Kuo et al [4]. Proposed a
distributed congestion control protocol called traffic
aware congestion control protocol (TACCP).
Through the buffer management mechanism
TACCP for adaptively allocating an appropriate
forwarding rate to potentially jammed sensors for
mitigating the congestion load. TACCP can be used
to avoid packet loss caused by traffic congestion,
reduce the power consumption of nodes, and
improve the throughput of the entire network.
Omer chughtai et al [5]. The developed
CTLS protocol avoids congestion proactively by
modifying the traditional route discovery
mechanism in order to select the best node during
the forward route formation. It detects congestion
in a timely manner by monitoring either the
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remaining space of the buffer, the interval between
the consecutive packets and the link utilization
based on the number of times a node goes into the
Back off stage of CSMA/CA. The CTLS either
bypasses the congested node/link through a local
repair technique or deviates the traffic to the detour
path in order to alleviate congestion. The
simulation results show that the CTLS performs
better as compared to the congestion avoidance,
detection and alleviation and no congestion control
schemes in terms of packet delivery ratio, ETE
delay, throughput, and energy consumption per data
packet in a resource constraint wireless network.
Ji-ming CHEN et al [6]. Proposed a
congestion control scheme CADA for congestion
avoidance, detection and alleviation in wireless
sensor networks. The key objective is to provide
high transmission quality for the data traffic under
conditions of congestion. The scheme comprises
three main mechanisms. Firstly, it attempts to
suppress the source traffic from event area by
carefully selecting a set of representative nodes to
be data sources. Secondly, the onset of congestion
is indicated in a timely way by jointly checking
buffer occupancy and channel utilization. Lastly,
the network attempts to alleviate congestion in the
traffic hotspot by either resource control or traffic
control, which is dependent on the specific
congestion Condition.
Vaibhav Eknath Narawade et al [7]. The
Survey of the congestion control and avoidance
mechanisms are investigated in terms of their
appropriateness in congestion detection and inform
the related nodes with the intention that a proper
control can be taken. Based on the usage, several
methods are applied to manage the congestion. To
satisfy the application requirements, either traffic
control by throttling the node rates or resource
control by utilizing the unused resources are used
Different issues and challenges regarding the
congestion control protocols were studied which
will be useful for further research in this field.
Venugopal K R [8]. Proposed MCDR
techniques is effectively mitigates congestion by
considering the parameters such as minimum
Queue Length, the depth, the distance and
maximum residual energy of each node while
scattering the traffic towards the sink from the
congested area. Improved network throughput is
achieved by maintaining the minimum congestion
rate due to fair queue length at each node in the
network. The looping problem has been drastically
reduced by selection of each node that is based on
the minimum distance to scatter the traffic towards
the sink. The reduction in looping results in lower
latency and minimizes energy utilization. The
results of our proposed algorithm show that
improved network throughput and packet delivery
rate for both high and low load conditions and also
fulfill the fidelity requirement of different
applications.
Majid Gholipour et al [9]. Proposed a hop-
by-hop gradient-based routing scheme to evenly
distribute traffic in WSNs with non-equivalent sink.
The key concept herein is to utilize the number of
hops and the current traffic loading of neighbors to
make routing decisions reduces the number of
packet retransmissions and packets dropped by
preventing nodes with overloaded buffers from
joining in routing calculation. Simulation results are
indicate improves network performance such as
end-to-end packet delay, packet delivery ratio, and
average energy consumption in comparison to other
routing schemes including SPF, CODA, ESRT, and
GRATA. To address practical concerns, the
proposed routing algorithm can be easily
implemented on existing devices without major
changes. The limitation of the new method is that
the values of traffic factors (α, β, and φ) are chosen
based on simulation experiments. Moreover,
overhead is a common drawback of proposed
algorithms.
Buddha Singh et al [10]. Suggested by a
Particle Swarm Optimization (PSO) approach for
generating energy aware clusters by optimal
selection of cluster heads. The PSO eventually
reduces the cost of locating optimal position for the
head nodes in a cluster. In addition, it has
implemented the PSO based approach with in the
cluster rather than base station, which makes it a
semi distributed method. The selection criteria of
objective functions are based on the remaining
energy, intra cluster distance, node degree and head
count of the portable cluster head. Furthermore,
influence of the expected number of packet
transmission along the estimated path towards the
cluster head is also reflected in our PSO energy
consumption model.
III. PROPOSED METHOD
3.1 Congestion Occurrences in WSN
The WSN is randomly deployed in
particular area with base station(BS), positioned at
coordinate number of sensor nodes are distributed
in particular region of x,y (meters) based on
distance. It is presumed that there are total numbers
of cluster in the sensor network. Further, using to
hybrid node deployment strategy, that means
combination of equal and unequal cluster process.
We assume the network model Fig.1 represents that
intra cluster communication that sends the data
from source to destination while in this process for
an example in the 11th
node the congestion is
occurred.
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Fig.1: Intra Cluster Model
During the congestion, this is the right to
check the nodes energy level and the buffer size.
After this process got over nodes energy level and
buffer size are high means the data will send
through the CH node (or) neighboring CH node via
the BS. Suppose any one the nodes energy level
(or) buffer size is low in that time repeat request
process is activated.
A sensor node is allowed to use different
level of transmission power depending upon its
distance from the target node. The distance can be
estimated from the strength of the signal received
from the destination node. The based station
periodically sends a request to the cluster head to
upload samples collected by the sensors (fig.1) on
receiving the request the cluster head broadcast data
collecting signal to its entire cluster member. The
cluster member nodes are their packets to the CH,
after which the CH processes and aggregates the
collected packet and finally forwards the
information to the Base Station (BS).
In this model summarized as follows
• Calculate the Intra cluster node to base
station of node to congestion node
distance.
• Determination of the number of
communication links between the member
nodes to CH nodes.
• Derivation of total retransmission of the
collided packets in particular simulation
time period.
3.2 Congestion Aware Architecture
The proposed Hybrid Cluster Based
Congestion Aware (HCBCA) Algorithm is
distributed in Hierarchical clustering
communication between sources to destination. It
acquires the hierarchical clustering structure in
order to achieve the congestion Avoidance.
Low High
Greater than Greater
(or) Equal than
Yes NO
Fig.2: Congestion Aware Architecture
Intra Cluster
Base Station
Sensor Node Cluster Head
Congestion Node
2
1
5
7
3
4
6 @
8 9
Start
Sensing all the nodes inWSN
Calculate distance from base
station to all Sensor nodes
Hybrid Cluster
To Check
Congestion
Transmission of data from
source to destination
To Check
Interval
Time
Stored in buffer
(or) Repeat
request
CH to BS (or)
Neighboring CH
node to BS
End
Simulation
Time
Round Round+1
Cluster
Head to
Base
Station
End Process
CH is formed based on the
higher Energy level
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The Architecture of hybrid cluster based
congestion describes the level of congestion and the
performance of congestion in each level. In
HCBCA check the condition in two levels. They
are low and high level, when the congestion level is
low then the packets are directly sends to base
station from CH. In order to describe that
congestion level is high, and then condition occurs
to check the interval time. In mean while condition
falls in less than or equal position then the packets
are transmitted from CH to BS or in another way it
will transmit the data from neighboring CH node to
BS. When the condition is greater than the
maximum interval time, then the packets are stored
in the buffer or repeat request process will be
active. At the last all these processes will meet the
end simulation time, while the simulation ends then
the process will end or otherwise data transmission
will repeat its process from the start to end.
3.3 Determine Congestion Metrics
The clarification of these congestion
metrics and determination of composite congestion
metric are explained in the following subsection.
Distance Calculation
Distance between the nodes source to
destination can be calculating using formula
D(S,R) = (x1 − x2)2 + (y1 − y2)2....(1)
Where d(S,R) is the distance between node S and
Base Station R, (X1,X2) is the X coordinate of
node S and Base Station R and (y1-y2) is the
coordinate of node R and Base Station R.
Find Queues length
The Queue length Q1 is defined as the ratio
of number of packets in the buffer to the maximum
buffer size of node. It can be calculated as
QL(i) =
( )
.......... (2)
Where Q1(i) is the queue length of node i, NP is
number of packet in the buffer, BS(i) is the
maximum buffer size of the node i.
Find the flow of Data
Contribution level P is calculated based on
the total number of flows passing through a node as
P =

…. (3)
A node with more number of flows represents a
high level that is more prone of the congestion as
compared to the node with less member’s flows.
The step for our Proposed Algorithm are
described in Table 1
Table 1: Hybrid Clustering Based Congestion Aware
(HCBCA) ALGORITHM
Initialization:
Min: Minimum Interval, Max: Maximum Interval
RRQS: Repeat Request
Sensor Nodes: {SN1,SN2,…….SNn}
CH: The set of CHs based on Energy level {CH1,CH2…CHn}
Step 1: Start
Step 2: To Form Sensor nodes with Wireless Sensor
Network (WSN).
Step 3: cluster formation is generated based on sensor
Nodes distance.
Step 4: cluster head (CH) is formed based on higher
Energy node.
Step 5: Then, to collect higher energy level of
Neighboring CH node.
Step 6: Transmission of packets between sources to
Destination based on Hybrid Model.
Step 7: Congestion Checking
Step 7(a): Congestion level is low
Then, packets are directly sending from
CH to BS.
Step 7(b): Congestion level is high
Then, Check interval time from source
node to congestion node.
Step 8: If Interval time is less than (or) Equal
Then, packets are directly sending from CH to BS
(or) another way Packets are sending From
Neighboring CH node to base station.
Else
Interval time is greater than maximum
Then, packets are stored in Buffer (or) Repeat
Request Process.
Step 9: To check Simulation Time
If Simulation time is End
Then, end process.
Else
Round Round + 1
Then, following step 2 to 8.
Step 10: End Process
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IV. SIMULATION RESULTS
The suggested hybrid congestion
avoidance methodology is improved by Network
simulator (NS2.34) Environments.
Table 2: Simulation Parameter
Parameters Values
Number of Node 50
Area Dimension 400 * 400(Meter)
Routing Protocol DSDV
Total Energy 150 Joule
Initial Energy 0.5 Joule
Packet Size 4000 bits
Number Of Round 500
Type of the MAC 802.11
Simulation Tool NS2.34
The proposed algorithm of EEBHC is
highlighting on the network energy with new
developed method HCBCA is provide a good
output with respect to the packet delivery ratio, End
to End delay time, Dead node occurrences in
rounds, packet losses and Energy Savings.
Performance of packet delivery ratio
The representation of fig: 4.1 denote that the
existing method was overcome by HCBCA. In this
packet delivery ratio, the packets are transmitted
from the sources to destination by proper routing
path to evaluate the number of packets that are
delivered in WSN like hybrid cluster head
approaches in WSN.
Fig 4.1 packet delivery ratio
While comparing the result of existing method
EEBHC denotes 87.41% and the proposed method
HCBCA denotes 96.85%.
Performance of End to End delay
The packet delay was average Maximum time
to arrive in the destination. It take time maximum
delay when congestion is occur for that time
packets are stored in buffer or Repeat Request
process is activated.
Fig: 4.2 End to End delay
When comparing the existing method EEBHC
the result takes maximum time to transmit the
packet now the proposed work is better for transmit
the packets.
Performance of Dead Node Occurrences
During the packet transmission while the dead
node occurred packet losses are reduced in
proposed work. In the proposed work when dead
node occurs reducing we use buffer management
and retransmission concept.
Fig: 4.3 Dead Node Occurrences
When compared to existing method EEBHC the
dead node occurs during 237th
round and average
packet losses is 12.59%. The proposed HCBCA
method dead node occurred in 273 round and
average packet losses is 3.15%. During the packet
transmission while the dead node delay occurred
means packet losses will be reduced for that
proposed method is used in the effective manner.
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Performance of Remaining Energy
Fig: 4.4 Remaining Energy
Comparing the remaining energy by taking
100% and 150 joules as sample for that, we get the
following results existing and proposed work. The
result for the existing method EEBHC 47.8% and
corresponding joule is 71.760J. So, the proposed
works proves that better for saving the remaining
energy.
V. CONCLUSION
In this paper, we proposed Hybrid Cluster
Congestion Aware method is concentrated on the
buffer management and Packet retransmission in
WSN. The objective is providing high transmission
of packet delivery ratio has been improved the
network lifetime performance with respect to time,
at the same time packet losses have reduced by
packet retransmission.
The initially energy level of the node is being
lower than 0.5J the node in noted as a dead node.
So, the algorithm HCBCA is used. In that time data
losses or data aggregation can be failed during the
energy level is low. Finally, the simulator has
considered the advantages of HCBCA method and
demonstrated for the significant performance in an
improvement over existing Scheme.
VI. REFERENCES
[1] Azlan Awang, Omer Chugtai, Nasreen
Badruddin “A Congestion-Aware and Energy
efficient Traffic Load balancing Scheme for
Routing in WSNs” Conference paper in
Telecommunication System, IEEE oct-2014.
[2] Haider Banka, Srinivasa Rao p.s, Prasanta,
Jana.K “A Particle Swarm optimization based
energy efficient cluster head selection algorithm
for wireless sensor networks” Wireless Netw-
Springer, pp(1270-07), April-2016.
[3] Rheleh Hashemzehi, Reza Nourmandipour,
Farokh Koroupi “Congestion in Wireless Sensor
Networks and Mechanisms for Controlling
Congestion” Indian Journal of Computer
Science and Engineering(IJCSE) vol.4, pp(204-
207), jun-2013.
[4] Chia-Hsu Kuo, Tzung-Shi Chen, Zheng-Xin
Wu “Congestion Control under Traffic
Awareness in Wireless Sensor Networks”
International Computer Symposium, pp(429-
4343), IEEE-2016.
[5] Omer Chghutai, Nasreen Badruddin, Azlan
Awang, Maaz Rehan “congestion-aware and
traffic load balancing scheme for routing in
WSNs” Telecommun Syst-Springer, January-
2016.
[6] Ji-ming CHEN ,Wei-wei FANG , Lei SHU ,De-
pei QIAN “ Congestion avoidance, detection
and alleviation in wireless sensor networks”
Journal of Zhejiang University – SCIENCE
C(Computer & Electronics), pp(63-73)-2010.
[7] Vaibhav Eknath Narawade , Uttm D.Kolekar
“Congestion Avoidance and Control in Wireless
Sensor Networks: A Survey” IEEE- 2016.
[8] B M Thippeswamy , Raghvendr M, Reshma S
“MCDR: Mitigating Congestion using Distance
based Routing in Wireless Sensor
Networks”IOSR Journal of Computer
Eineeringng (IOSR_JCE) , vol-16, Issue 1,
pp(17-29), Jan-2014.
[9] Majid Gholipour, Abolfazl Toroghi and
Mohammad Reza Meybodi “Hop-by-Hop
traffic-aware routing to congestion control in
wireless sensor networks” EURASIP Journal on
wireless Communication and Networking, pp(1-
13), Springer-2015.
[10] Buddha Singh and Daya Krishan Lobiyal “A
novel energy-aware cluster head selection based
on particle swarm optimization for wireless
sensor networks” Human- Centric Computing
and Information Sciences, pp(1-18), Springer-
2012.
[11] Ji-Yeon Min, Sang-Hwa Chencyung, and
Yuvin Ha “An Energy and Latency Aware WSN
MAC Protocol for Bidirectional Traffic in Data
Collection” International Journal of Distributed
Sensor Networks (IJDSN) – 2015.
[12] Younas Khan, Sheeraz Ahmed, Imran Ahmad
“ETEEM-Extended Traffic Aware Energy
Efficient MAC Scheme” International journal of
Advanced Computer Science and Applications
(IJACSA), vol-7, pp(272-277),2016.
[13] Muzamil Basha S, Vinodh K, Raghu Veer
“Enhanced Congestion Aware Routing for
Congestion Control in Wireless Sensor networks”
International Journal of Scientific and Research
Publications(IJSRP) vol-3, Issue-2, Feb-2013.
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