1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME
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CORONA BASED ENERGY EFFICIENT CLUSTERING IN WSN
Syed Abdul Sattar1
, Mohamed Mubarak.T2
, Vidya PV3
, Appa Rao4
1
Royal Institute of Technology and Science, Dean,Department of Computer Science
Hyderabad,Andra Predesh State,India
2
Royal College of Engineering, Asst Professor,Department of Computer Science
Trichur,Kerala state,India
3
Royal College of Engineering, B.Tech Student, Department of Computer Science
Trichur,Kerala state,India
4
GITAM Institute of Technology,GITAM University ,Professor and Head,Department of
Computer Science, Visakhapatnam,Andra Predesh State ,India
ABSTRACT
Wireless sensor network represents one of the most interesting research areas with profound
impact on technological development. Here we implement an energy efficient approach for optimal
cluster head selection and clustering. We also pinpoint cluster head rotation technique using the
concept of back off timer that prolongs the life time of the network. Addition and deletion of nodes
can be done in the clustered network without affecting the existing infrastructure.
Index Terms - Wireless Sensor Networks, Corona, Clustering, Cluster Head, Routing, Nodes, Sink,
Back off Timer, VCCB, Optimal Distance.
I. INTRODUCTION
Many future applications will increasingly depend on embedded wireless sensor networks. A
sensor network consists of numerous sensor/actuator devices. Wireless Sensor Networks have
emerged as an important new area in wireless technology. In the near future, the wireless sensor
networks are expected to consist of thousands of inexpensive nodes, each having sensing capability
with limited computational and communication power, which enable us to deploy a large-scale sensor
network. A critical aspect of applications in wireless sensor network is network lifetime.
Wireless sensor network are usable as long as they can communicate sensed data to processed
node. Sensing and communication are important activities and they consume energy. So power
management and sensor scheduling can effectively increase the networks lifetime. The use of wireless
sensor networks is increasing day by day and at the same time it faces the problem of energy
constraints in terms of limited battery lifetime.
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WSN has considerable technical challenges in data processing and communication to deal
with dynamically changing Energy, Bandwidth and Processing power. Another important issue in
Wireless Sensor Network is to maximize Sensor Network lifetime. The vital issue in WSN is to
maximize the network operational life. In order to achieve this, it is necessary to minimize the energy
utilization of a node. Most of the energy consumption in wireless sensor node is attributed to
transmitting/receiving, processing, and forwarding the data to neighboring nodes.
Clustering has been shown to improve network lifetime, a primary metric for evaluating the
performance of a sensor network. The clustering techniques proposed for data processing typically
consider many parameters, such as the distance between the nodes, and assume that nodes are more
reliable.
The sensors are capable of sensing the data from the environment in which they are deployed,
processes that data and transmit it to the base-station (BS). The sensor circuit senses the environment
and converts the signals into electrical signals which are then transmitted to the BS using a transmitter
via a routing node. In clustering, nodes with higher power levels perform the fusion of data gathered
from the other sensor nodes and transmit the aggregated data to the base-station (BS) while the nodes
with low power levels only perform the sensing of the environment. They transmit the sensed data to
the higher node, known as the cluster-heads (CHs) which are at a lesser distance to the base station.
The cluster formation and the assignment of special tasks to the cluster heads (CHs) reduce the power
dissipation within a particular cluster, which improves the scalability of the sensor network. Also by
aggregating the sensed data, the amount of data to be transmitted to the base-station (BS) is reduced
and the lifetime of the overall sensor network is increased.
II. RELATED WORKS
Paper [1] proposes a model for energy efficient clustering and cluster head selection method.
It is based on a circular monitoring area with a uniform node density and a sink node at the center.
The area is divided into concentric circles known as corona, each of width R/2, where R is the
transmission range of the sensor node. Cluster head selection is performed by finding out the optimal
distance from VCCB, where VCCB lies at the midway between two concentric circles.
One of the main design goals of WSNs is to carry out data communication while trying to
prolong the lifetime of the network and prevent connectivity degradation by employing aggressive
energy management techniques [2].
The clustering concept offers tremendous benefits for wireless sensor networks. However
when designing for a particular application, designers must carefully examine the formation of
clusters in the network. Depending on the application, certain requirements for the number of nodes in
a cluster or its physical size may play an important role in its operation. This prerequisite may have an
impact on how cluster heads are selected in this application [3].
There are many clustering protocols are exists, such as LEACH, HEED etc. As the need for
efficient use of WSNs on large regions increased in the last decade dramatically, more specific
clustering protocols were developed to meet the additional requirements (increased network lifetime,
reduced and evenly distributed energy consumption, scalability, etc.). The most significant and widely
used representatives of these focused on WSN clustering protocols (LEACH, EEHC, and HEED).
Some of them (such as LEACH, EEHC, and their extensions) follow a random approach for CH
election (the initially assigned probabilities serve as the basis for the random election of the CHs),
whereas others (like HEED and similar approaches) follow a hybrid probabilistic methodology
(secondary criteria are also considered during CH election—i.e., the residual energy) [4].
The LEACH protocol [3] is an application-specific clustering protocol, which has been shown
to significantly improve the network lifetime. It assumes that every node is reachable in a single hop
and that load distribution is uniform among all nodes. LEACH assigns a fixed probability to every
node so as to elect itself as a CH. The clustering process involves only one iteration, after which a
node decides whether to become a CH or not. Nodes take turns in carrying the role of a CH [5].

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HEED (Hybrid Energy-Efficient Distributed clustering), has four primary goals: (i)
prolonging network lifetime by distributing energy consumption, (ii) terminating the clustering
process within a constant number of iterations/steps, (iii) minimizing control overhead (to be linear in
the number of nodes), and (iv) producing well-distributed cluster heads and compact clusters. In
classical distributed systems, a node can either be a server or a source, but not both. A fixed number
of servers are known to every source in the system, and a server is always available for processing. In
our model, every node can act as both a source and a server (cluster head), which motivates the need
for efficient algorithms to select servers according to the outlined system goals [6].
The execution of a clustering algorithm can be carried out at a centralized authority (e.g., a
base station) or in a distributed way at local nodes. Centralized approaches require global information
about the network topology. Banerjee et al. [7] proposed a centralized technique that does not require
knowledge of node locations. Even though t is a spanning tree based idea, we are designing a new
idea which is also a network in which node locations are unknown [4].
III. SYSTEM MODEL
A. NEED FOR CLUSTERING
WSN offer unique systems for creating communications infrastructures on-demand. Their use
is dependent on the effective deployment of these systems to areas of interest. There are several
challenging issues involved in the deployment of WSN, mostly due to their small size and large
number of nodes required to establish proper operation.
WSN is an emerging technology that shows great promise for various futuristic applications
both for mass public and military. The sensing technology combined with processing power and
wireless communication makes it lucrative for being exploited in abundance in future. The inclusion
of wireless communication technology also incurs various types of security threats. A WSN must also
be self-monitoring and able to proactively reconfigure to mitigate certain malfunctions before they
actually occur.
Clustering means grouping of nodes. As each node depends on energy for its activities, this
has become a major issue in wireless sensor networks. The failure of one node can interrupt the entire
system or application. Every sensing node can be in active (for receiving and transmission activities),
idle and sleep modes. In active mode nodes consume energy when receiving or transmitting data. In
idle mode, the nodes consume almost the same amount of energy as in active mode, while in sleep
mode, the nodes shutdown the radio to save the energy.
B.CLUSTERING BASICS
In cluster based architectures, mobile nodes are divided into virtual groups. Each cluster has
adjacencies with other clusters. All the clusters have the same rules. A cluster can be made up of a
Cluster Head node and Cluster Members. In this kind of network, Cluster Head nodes are used to
control the cluster and the size of the cluster is usually about one or two hops from the Cluster Head
node. Grouping sensor nodes into clusters has been widely adopted by the research community to
satisfy the above scalability objective and generally achieve high energy efficiency and prolong
network lifetime in large-scale WSN environments. The corresponding hierarchical routing and data
gathering protocols imply cluster-based organization of the sensor nodes in order that data fusion and
aggregation are possible, thus leading to significant energy savings.

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Fig 1 Clustered Network
The sink node gets all information from the cluster heads those are connected to the other
nodes under control of them. Here is the most challenging part will come to place that, if the
clustering head pass the same events to the sink node it will leads to more energy consumption
.Another fact is that if the clustering node having the capability to compress/data aggregation energy
consumption can be reduced to a optimum level. So, here we select the clusters as the nodes which
having maximum resources and monitoring power.
IV. PROPOSED MODEL
This paper mainly focuses on energy efficient and a secure Wireless Sensor Network. We use
clustering method for communication between nodes and sink, since it is energy efficient when
compared to single hop and multi hop routing.
All non-CH nodes transmit their data to the CH they are connected to, while the CH node
receives data from all the cluster members, performs specific processing functions on the data (e.g.,
aggregation, filtering, compression, etc.), and forwards data to the BS. We implement a clustering
mechanism which performs optimum selection of cluster heads and rotates the role of cluster head in
an energy efficient way.
A. ASSUMPTIONS
The proposed model made some assumptions. These are
• The nodes are deployed over circular monitoring area A of radius Z with uniform node
distribution density ρ.
• The sink node will be plotted at the centre.
• The nodes are deployed randomly on the region.
• All nodes having same energy.
• Selection of CHs based on timer expiration.
B.STEPS NEEDED
The various steps that we follow are:
Step 1: Read the maximum number of nodes that must be deployed, the sensing range and
transmission range of the node, the radius of the circular area, and the coordinates of the centre of the
circular area where the sink node is to be plotted.
Step 2: We randomly choose a set of coordinates that come within the circular region and plot
them in the specified area. For this, we compare the distance between the chosen coordinate and the
centre, and the radius of the circular area. We plot the point only if the distance falls under the given
radius. We count the number of points plotted.

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Step 3: Now we divide the circular area into a number of concentric circles each of width R/2,
where R is the maximum transmission range of the node. Each of these concentric circles is called
corona. We find the Euclidian distance of each node from the sink node and then the sensor nodes are
assigned concentric circle index using the formula
Fig 2: Corona based WSN model
Step 4: In order to implement energy balanced clustering, the concept of Virtual Concentric Circle
Band (VCCB) is introduced, where each sensor node calculates their respective VCCB index using
the formula
Fig 3: Assigning VCCB to nodes
The value of δ depends on node density; If the sensing area is densely populated, the value of δ is
small, for sparsely populated area, the value of δ is large. If the distance of a node from the sink, dsi,

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falls within VCCB index, then the corresponding sensor node is chosen as a probable candidate for
cluster head election. These candidate nodes then calculate their distance from the centre of their
respective VCCB as
Here we use the concept of a back-off timer. Its initial value is proportional to the distance of the
candidate from the centre of VCCB. For a node Ni, the initial value of back off timer is given by
Step 5: The cluster formation phase begins when the sink node sends START message to the
candidates. Upon receiving this start message, each candidate starts its back of timer. Since the back
off timer value is proportional to the distance of the candidate from the centre of VCCB, the back off
timer of the node near to the centre will expire first. The node, whose back of timer expired first, will
send as advertisement to all nodes within the radio range R/4, announcing itself as the cluster head.
All the cluster head candidates within this radio range, will stop their back off timer and all the cluster
head candidates as well as non-cluster head candidates within this range will acknowledge the receipt
of announcement, thus forming a cluster. This process is carried out throughout the network area, thus
forming a number of non uniform clusters.
Step 6: In order to prolong the life time of the network, we change the role of cluster head with in
a cluster when the remaining energy of the current head lowers below a particular threshold value.
Find out the candidate nodes among the members of that cluster. Sink node sends a START message
to those candidates, upon which each candidate starts their back off timer. The nodes whose timer
expires first will be chosen as the new cluster head.
C. IMPLEMENTATION OF SCALABILITY
Many applications of wireless sensor networks adopt hierarchical structure for the scalability and
simplify of management. Clustering is the most popular method that imposes such a scalable
topology. Here we implement addition and deletion of nods to the existing sensor network.
In order to add a new node in to the specified clustered network, we keep track of an array inside
the node structure for every cluster head which stores the ids of the member nodes of the
corresponding cluster heads. Then new node sends a message to sink node about its arrival then it will
forwarded to all cluster heads. After that it will join to the cluster head that has minimum distance
from that node and it should have high monitoring capability.
Deletion means removing out-of-date, redundant, or inconsistent nodes. Then update the nodes in
clusters to eliminate redundancy. In order to perform deletion we can use the same array that defined
for the addition of nodes. Delete the node id from the array for deletion and update the array. The
goals of this scalability approaches are maintaining stable clustering structure, minimizing the
overhead for the clustering set up, maximizing lifespan, and achieving good end to end performance.
V. SIMULATION RESULTS
The network simulation is done in MATLAB environment. A circular area with specified
radius is considered. The nodes have to be deployed independently and randomly. Certain numbers of
nodes are deployed over the area. The nodes which satisfy the criteria specified in the algorithm will
plot over the area. That is, we compare the distance between the chosen coordinate and the centre, and
the radius of the circular area. We plot the nodes only if the distance falls under the given radius.

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The probable candidates and the cluster members are represented with various colors in the
graph. The transmission energy of the nodes while sending k bits of data over the distance will found
out by the equations:
Where is the transmission electronics energy and d0 is the optimum distance.
The energy consumption of sensor node to receiver k bits of data is given by:
The messages exchanged between each and every node is represented by colored lines in the
graph. Probable candidates are those nodes which are more suitable nodes to become cluster heads.
These nodes are selected on the basis of calculated VCCB indices during the execution.
Cluster heads are selected by timer expiration. The candidate node whose back off timer
expires first will be chosen as the cluster heads. After the selection of cluster heads they advertises
these information to the nodes within the R/4 transmission range of the selected cluster head.
0 100 200 300 400 500 600 700 800 900 1000
0
100
200
300
400
500
600
700
800
900
1000
Fig 4 Clustered Network
Here energy is the heterogeneity parameter. Whenever the remaining energy less than a
particular threshold, cluster head rotation will be performed. Cluster head rotation is performed by
finding next powerful probable candidate which has higher monitoring capability. So, the sink node
sends START message to these candidates and they start their back off timer. As in the previous case
the node whose timer expires first, it will become the cluster head.
This kind of cluster head rotation avoids the need for reclustering. Reclustering takes more
energy to maintain the topology and all the nodes will again performs clustering and joins to the
clusters. In our approach, we can avoid reclustering thus avoids the higher energy utilization. Hence
energy consumption of corona approach is less than that of other clustering approaches.
Scalability is one of the major issues when WSN is considered. An efficient sensor network
should be extensible without affecting the performance characteristics. Here we implement addition
and deletion of nodes from the sensor network.

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For performing addition to the wireless sensor network, create an array that stores the IDs of
the member nodes for each cluster head. Also create a variable that stores the number of members.
When a new node wants to join to the sensor network, new node sends the information to the sink
node about its arrival. Then find the distance to each cluster head and join to the cluster head which
has minimum distance from the new node. After join to the cluster head member array of that cluster
head will be update by adding the new node ID.
Deletion means removing the redundant or lower energy nodes from the sensor network.
Deletion of such nodes improves the performance of the network because, these nodes can’t
monitoring the area efficiently and sending messages to these kinds of nodes will leads to unnecessary
energy consumption. Here deletion is done based on the residual energy. Whenever energy of the
existing node reduces than the threshold value, the IDs of such nodes are maintained in an array.
Already we have count of the members under each cluster head. We check the ID of the node to be
deleted with the member IDs. Then delete the ID of the node to be removed from its CH’s member
array. Update the array after deletion.
COMPARISON WITH LEACH
LEACH is called “Energy efficient Adaptive protocol for clustered Wireless sensor
networks”. Low Energy Adaptive Clustering Hierarchy (LEACH) is the first energy efficient routing
protocol for hierarchical clustering. LEACH is a self organized protocol based on a probability
approach. LEACH mainly focuses homogeneous environments. The probability to become a CH after
once it is selected is higher because in LEACH all nodes get chance to become CH and it leads to
reduced lifetime. In our protocol, we select only certain number of efficient candidates to become CH
and it leads to enhanced lifetime of cluster. LEACH sends JOIN request to all other nodes over
deployed area leads to higher energy consumption. In corona-based approach we send JOIN message
only to those nodes that are within the radio range which leads low energy consumption.
The following figure shows the comparison between LEACH and corona based approach
based on the number of nodes deployed and number of probable candidates to become CH.
100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
NoofProbableCandidatesforCH
No of Nodes Deployed
LEACH
Corona-based Algorithm
Fig 5 Number of Nodes Deployed vs. Number of Probable Candidates for CH Graph
From the analysis of above graph, it is clear that, in corona based approach we select only
optimum number of cluster heads.

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The following table describes the various statistics of the corona based approach:
Number of
nodes deployed
Number of
probable
candidates
Number of
CHs selected
during round 1
Number of
nodes failed after
round 1
Number of
CHs selected
during round 2
100 64 26 0 0
200 128 34 21 21
300 190 34 27 27
400 251 39 33 33
500 331 40 36 36
600 378 40 36 36
Table 1 Simulation Analysis-1
During each round of CH rotation, the number of CHs selected equals the number of CHs
failed during the previous round. This ensures a clustering method that effectively monitors all the
nodes in the network even after a certain number of rounds.
100 150 200 250 300 350 400 450 500 550 600
26
28
30
32
34
36
38
40
NoofCHs(Round1)
No of Nodes Deployed
Corona-based Algorithm
Fig 6 Number of Nodes Deployed vs. Number of CHs Selected (During Round 1)
After the first round of CH selection, a certain number of CHs fail as their energy fall below a
particular threshold value. Those nodes are deleted and the same numbers of CHs are selected in the
second round of CH rotation.
100 150 200 250 300 350 400 450 500 550 600
0
5
10
15
20
25
30
35
40
NoofCHs(Round2)
No of Nodes Deployed
Corona-based Algorithm
Fig 7 Number of Nodes Deployed vs. Number of CHs Selected (During Round 2)

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The time elapsed for first round of cluster head selection and second round of cluster head rotation
for various numbers of nodes deployed can be tabulated as follows:
Number of nodes deployed
Time taken for first round
of CH selection
(in seconds)
Time taken for second
round of Ch rotation
(in seconds)
100 329.631565 0.756115
200 446.454966 190.816695
300 681.083411 266.269403
400 692.684151 264.298860
500 863.258635 319.014914
600 1024.648643 387.028500
Table 2 Simulation Analysis-2
VI. CONCLUSIONS
Node clustering is a useful topology-management approach to reduce the communication
overhead and exploit data aggregation in sensor networks. Clustering is highly efficient because the
nodes are deployed over a large area which is also insecure. So, monitoring is one of the critical tasks
over a large area. In our corona based approach the best monitoring can be assured because even
though one CH is drained, we have a possibility to rotate them and the monitoring is more specific
due to different corona exist over a region. The major concern of scalability can also be resolved by
dynamic addition and deletion of nodes in the network, maintaining the same performance efficiency
thereby aiding a wide range of applications.
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