ENERGY EFFICIENT UNEQUAL CLUSTERING ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORK

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 3 (May-June 2015), PP. 195-198
195 | P a g e
ENERGY EFFICIENT UNEQUAL CLUSTERING
ALGORITHM FOR CLUSTERED WIRELESS
SENSOR NETWORK
Prof. N R Wankhade1, Dr. D N Choudhari2
Associate Professor1
, Professor2
,
1
Department of Computer Engineering, LGNSCOE, Pune University,Nashik, INDIA.
2
Department of Computer Science Engineering, Sant Gadge Baba Amravati University, Amravati, Maharashtra, INDIA.
nileshrw_2000@yahoo.com1
, dnchaudhari2007@rediffmail.com2
Abstract: Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers.
Keywords Wireless sensor networks, Unequal clustering,
Routing, Network lifetime, Hot spot problem.
I. INTRODUCTION
A. Detail Problem Definition
Wireless sensor networks are of tiny, battery powered
sensor nodes with limited processing power and
capabilities. To overcome this problem and to achieve
higher energy efficiency, sensor nodes are grouped into
clusters, where each cluster heads collects all the data and
process them before being sent to the base station. To
achieve network scalability, clustering[1] is the best
solution. The potential applications of sensor networks are
highly varied, such as environmental monitoring, tar- get
tracking, and battlefield surveillance. Sensors in such a
network are equipped with sensing, data processing and
wireless communication capabilities. Distinguished from
traditional wireless networks, sensor networks are
characterized by severe power, computation, and memory
constraints. Due to limited and non-rechargeable energy
provision, the energy resource of sensor networks should
be managed wisely to extend the lifetime[2] of sensors. We
consider a network of energy-constrained sensors that are
deployed over a geographic area for monitoring the
environment. Each sensor periodically produces
information as it monitors its vicinity. The basic operation
in such a network is the systematic gathering and
transmission of sensed data to a base station for further
processing. In order to achieve high energy efficiency and
increase the network scalability, sensor nodes can be
organized into clusters. Data collected from sensors are sent
to the cluster head first, and then forwarded to the base
station.
B. Need of Proposed System
In this paper, we propose and evaluate an Novel Energy
Efficient Unequal Clustering Routing (NEEUC) protocol
for mitigating the hot spot problem in wireless sensor
networks. It is designed for long lived, source driven sensor
network applications, such as periodical environmental
information reporting. NEEUC consists of two parts, one is
an Unequal Clustering algorithm for topology management,
and the other is a greedy geographic and energy aware
routing protocol for inter cluster communication. The main
contribution of the paper is that we provide the first unequal
cluster based routing protocol to mitigate the hot spot
problem and thus prolong the network lifetime. NEEUC is a
self-organized competition- based algorithm, where cluster
heads are selected based on local information (i.e., the
residual energy of neighboring nodes).
II. RELATED WORK
There has been substantial research in the area of
routing in wireless sensor networks. Many energy efficient
clustering algorithms have been proposed to prolong the
network lifetime [3]. We review some of the most relevant
papers. Heinzelman et al. [4] first propose a clustering
protocol called LEACH for periodical data gathering
applications. LEACH uses randomized rotation of cluster
heads to distribute energy consumption over all nodes in the
network. In the data transmission phase, each cluster head
forwards an aggregated packet to the base station directly.
An energy-aware variant of LEACH is proposed in [5][6], in
which the nodes with higher energy are more likely to
become cluster heads. However, the underlying routing
protocol is assumed to be able to propagate node residual
energy through the network. The authors analytically
determine the optimum number of cluster heads. HEED
[8][9] introduces a variable known as cluster radius which
defines the transmission power to be used for intra-cluster
broadcast. The initial probability for each node to become a
tentative cluster head depends on its residual energy, and
final heads are selected according to the intra-cluster
communication cost. HEED terminates within a constant
number of iterations, and achieves fairly uniform
distribution of cluster heads across the network. EECS [10]
introduces a cluster head competitive algorithm without
message exchange iterations. It extends LEACH and HEED
by choosing cluster heads with more residual energy. It also
achieves a decent distribution of cluster heads. While the
clustering problem has been extensively explored,
researchers have only recently started to study the strategies
for balancing the workload among cluster heads while
considering the inter-cluster traffic. In single hop sensor
networks, cluster heads use direct Communication to reach
the base station, and the problem of unbalanced energy
consumption among cluster heads arises. Cluster heads
farther away from the base station have heavier energy
burden due to the long-haul communication links.

196 | P a g e
Consequently, they will die earlier. In EECS [11][12], a
distance based cluster formation method is proposed to
produce clusters of unequal sizes. Clusters farther away
from the base station have smaller sizes, thus some energy
could be preserved for long-haul data transmission to the
base station. On the other hand, the hot spot problem arises
when multi hop routing is adopted when cluster heads
deliver their data to the base station. Heinzelman first
investigate an unequal clustering model for balancing the
energy consumption of cluster heads in multi hop sensor
networks. The work focuses on a Heterogeneous network
where cluster heads (super nodes) are deterministically
deployed at pre-computed locations. Through both
theoretical and experimental analyses, the authors show that
unequal clustering could be beneficial, especially for heavy
traffic applications.
The hot spot problem addressed in this paper is due to
the many-to-one multi hop data forwarding pattern on the
cluster head backbones. Researchers have proposed several
methods to mitigate this kind of hot spot problem. Perillo et
al. [15] analyse two such strategies. Although the network
lifetime can be improved by using a more intelligent
transmission power control policy that balances the energy
consumption of each node, they conclude that it cannot
solve the hot spot problem on its own.
They also investigate the effectiveness and cost
efficiency of using a heterogeneous clustering hierarchy to
mitigate the hot spot problem.
In some works, special assumptions about the sensor
network are made to facilitate solving the hot spot problem.
For example, in homogenous sensor networks, additional
sensor nodes can be deployed in the area near the base
station as reservoirs of energy [16][17].In [18], multiple sink
nodes are deployed to alleviate the hot spot problem in
large-scale sensor networks. It also reduces the energy
dissipation at each node. Recent research begins to exploit
the mobility of some nodes to facilitate the delivery of the
sensed data to the base station. Gandham et al. [19]
investigate the idea of employing multiple mobile sink
nodes to increase the lifetime of sensor networks. Location-
based geographic routing is also attractive in wireless sensor
networks due to its efficiency and scalability, and it is more
energy-efficient for data forwarding on the cluster head
backbone compared to traditional hop based methods.
Geographic routing algorithms have been studied in the
context of wireless networks. In this paper, we study the hot
spot problem existing in the hierarchical (cluster-based)
wireless sensor networks. In our study there is no need for
pre-deployment which greatly simplifies system
deployment. Besides that we also present a novel inter-
cluster routing strategy considering the metrics of both
transmitting distance and residual energy.
III. NETWORK MODEL
The network consists of a set of sensors which are
uniformly deployed over a field to continuously monitor the
environment. We make some assumptions about the sensor
nodes and the network model.
 All the nodes are alike and each node is assigned
with a unique identifier (ID).
 Base station is located at the center of the monitor
field. Sensors and the base station are all
stationary after deployment.
 A node can compute the approximate distance to
another node based on the received signal strength.
 Node can transmit in different power levels to
achieve different communication range.
We use a simplified model presented in [1]. Either the free
space (d2 power loss) or the multi-path (d4 power loss)
channel is used based on the distance between the
transmitter and the receiver. The energy spent for
transmitting h-bit packet over distance d is:
ETX (h,d) = (1)
Where d0 is the critical distance beyond which multi-path
channel mode is used. Energy spent for receiving h-bit
packet is
ERX(h,d) = hEelec (2)
All the cluster head can always aggregate the data coming
from its member into a single length-fixed packet.
IV. NEEUC Routing Algorithm:
A. Assumption
1. Sensors uniformly deploy over a sensor field
2. Sensors and a BS are all stationary
3. Sensors can use power control
4. A node can compute the
5. Approximate distance to another node based on the
received signal strength
The key idea of NEEUC is to mitigate the Hotspot problem.
Fig.1 Unequal clustering mechanism
We introduce a novel energy efficient unequal clustering
protocol for hierarchical routing, called NEEUC. It
organizes the network into clusters of unequal sizes. By
decreasing the number of nodes in clusters with higher relay
load near the base station, we can maintain more uniform
energy consumption among cluster heads in the long run.
B. Novel Unequal clustering algorithm
The Algorithm includes the following phases
a). Design of network structure
b) Determine communication radius
c) Clustering mechanisms
d) Multi hops transmission.
Clustering a wireless sensor network means partitioning its
nodes into clusters, each one with a cluster head and some
ordinary nodes as its members. Similar to LEACH, the
operation of this algorithm is divided into rounds. The task
of being a cluster head is rotated among sensors in each

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ET1 =Eelec *Ksum +ϵamp *ksum *d12 (7)
And
ET2 =Eelec *Ksum +ϵamp *ksum *d22
(13)
round to distribute the energy consumption across the
network. NEEUC is a distributed cluster head competitive
algorithm, where the cluster head selection is primarily
based on the residual energy and distance to the base station.
The algorithm for determine scope of each layer is follows.
a) Design Network Structure
The BS broadcasts a “hello” message to all nodes.
1. Each node computes the distance to the BS based on
signal strength and determine layer using following formula.
Upper = dmin + i *dmin/m , i=1,2,-----n (3)
Lower = dmin +(i-1)*dmax-dmin/m , i=1,2,----n
Where dmin & dmax –shortest and longest distance
between node and base station.
Assume distance between node and base station is greater
than lower boundary & less than upper boundary then node
belongs to layer i.
b) Determine cluster Radius
In this phase cluster radius of each node computed based on
distance to the base station and residual energy.
Radius= [ε1 (1-c* dmax −d (Si,BS)/ dmax-dmin) + ε2*
ETh/Einit]*Ro (4)
Where dmin & dmax –shortest and longest distance between
node and base station.
d (Si,BS) - distance from node I to base station.
C, ε1, ε2 - constant between 0-1
Ro- system setting of cluster radius.
Radius varies as residual energy & distance between node to
BS varies.
c) Clustering algorithm
Step-1 node generate random number between 0-1
If Random-no< T then node becomes candidate node.
Step-2 Candidate node send message to its radius calculated
in above formula.
Step-3 if node i can receive a message(id,Ri,RE) from node
j & distance between both nodes is less than any competition
radius of each other, then node i store node j in Sct.. Each
tentative CH maintains a set SCH of its “adjacent” tentative
CH.
Where Ri- radius RE-residual energy
Step-4 Node i compare its own energy with the energy of
each node in Sct…If the energy of node i > any node satored
in Sct.. then node becomes CH and release a message Finish
_Elect_Meassage within its cluster radius.
Step-5 According to information within
Compate_Head_Meassage non CH node join with CH.If non
CH received more message then use larger signal strength.
d). Multi Hop communications
C. Algorithm of multipath routing
1: if(network created)
2: if(SourceNode != DestNode)
3: int Size = datasize;
4: Send(data)
5: ACK = data received size;
6: if(ACK == datasize)
7: Data received successfully;
8: Flag = true;
9: else
10: Data is lost in path;
11: Flag = false;
12: if(Flag == false )
13: For all paths;
14: Calculate average of energy and trust
value.
15: if(average==maximum value)
16: shortest path = current path;
Send(data);
Multipath Routing is a routing procedure, which
chooses various ways to convey information in the middle
of source and destination nodes. As the essential
significance of routing means, selecting best way in the
system, multipath routing strategies are utilized to choose
the best path in the network.
C) Data-Aggregated Unequal Layer Inter-Cluster Routing
There is a large number of redundant data among
different clusters in practical application. In this paper,
Data-aggregated Unequal Layer Inter–cluster routing
protocol is proposed to solve this problem. Cluster heads
from outer layer forward to the next inner layer and finally
reach the base station. We will select the MCH (Main
Cluster Head) in layer 0 whose distance are smaller to base
station as less than TD_MAX. The algorithm is as follows.
1)Suppose there are many cluster heads CH1,CH2….CHn,
whose distances are less than threshold TD_MAX,
each cluster head sends their residual energy
E1,E2,…..En and values of data size K1,K2,….Kn.to
base station.
2) Base station calculates the total size of data packet as
Ksum=K1+K2+……..+Kn (5)
3)In order to transmit a K-bit message, the radio expends
ETX(k,d)=Eelec*k+εamp*k*d2 (6)
where Eelec and εamp are the fixed values. In order to
send the data packets from CH1 and CH2, the base station
expends:
ET2 =Eelec *Ksum +ϵamp *ksum *d22
Where d1 and d2 are distances from CH1 and CH2 to
base station.
4) Base station calculates the difference value between
cluster head CH1 and CH2 as
ΔET=εamp*Ksum*(d11- d22) (8)
And the difference value between the residual energy of
cluster head CH1 and CH2 is: ΔE=E–E2 (9)
5) Compare ΔE with ΔET,
if ΔE is greater than ΔET
CH1 chosen as the preliminary main cluster head (PMCH).
Final PMCH will become the main cluster head (MCH).
MCH will collect all the data and it will check for
redundancy, before being sent to the base station. Thus it
efficiently balances the energy consumption and increases
the network lifetime.
V. RESULTS AND DISCUSSION
A. Energy Efficiency
We observe that the energy consumed by CH’s and
normal nodes in EEUC is greater than NEEUC.

198 | P a g e
VI. CONCLUSION
In this paper, we have introduced a novel unequal
clustering routing protocol for wireless sensor networks. The
hot spot problem arises when employing the multi hop
routing in a clustered sensor network. To address the hotspot
problem, we first introduce an unequal clustering algorithm.
Cluster heads closer to the base station have smaller cluster
sizes than those farther from the base station, thus they can
preserve some energy for the purpose of inter-cluster data
forwarding. As well as we introduced aggregated unequal
layer inter clustering.
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Authors profile:
[1] Prof. N. R. Wankhade completed
his post-graduation from bharti
vidyapith, Pune Maharashtra .Presently
he is working at LGN Sapkal college
of engineering, Nashik, Maharashtra,
India as a professor and head of
computer engineering department .He
has presented papers at National and
International conferences and also published paper in
national and international journals on various aspect of the
computer engineering and WSNs. His research of interest
include computer networks, network security, wireless
sensor network.
[2] Dr. Dinesh N Chaudhari, is
working as Professor and Dean in
computer ngineering department of
Jawaharlal Darda Institute of
Engineering & Technology, Yavatmal.
He is recognized Ph.D. guide at
Amravati University and has more than
20 years of academic experience. His
interests are in cloud computing,
computer networking and security; and
he has written many papers in
national/international Conferences and
journals.

ENERGY EFFICIENT UNEQUAL CLUSTERING ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORK

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ENERGY EFFICIENT UNEQUAL CLUSTERING ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORK