Highly Scalable Energy Efficient Distributed Clustering Mechanism in Wireless Sensor Networks Based on Hierarchical Approach

Asian Journal of Applied Science and Technology (AJAST)
Volume 1, Issue 1, Pages 24-29, February 2017
2017 AJAST All rights reserved. www.ajast.net
Page | 24
Highly Scalable Energy Efficient Distributed Clustering Mechanism in Wireless
Sensor Networks Based on Hierarchical Approach
J.K.Deepak Keynes#
and Dr.D.Shalini Punithavathani*
#Research Scholar, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India.
*Principal, Government College of Engineering, Tirunelveli, Tamil Nadu, India.
Article Received: 02 February 2017 Article Accepted: 13 February 2017 Article Published: 16 February 2017
1. INTRODUCTION
A wireless sensor node consists of low power processor, tiny
memory, radio frequency module, various types of sensing
devices and limited powered batteries. More amount of
energy consumption in a WSN happens during wireless
communications. The energy consumption when transmitting
a single bit of data corresponds to thousands of cycles of CPU
operations. These wireless sensor nodes assemble data from a
sensing area which is possibly inaccessible for humans. Data
gathered from the sensing field are usually reported to a
remotely located base station (BS). This high redundancy of
sensing power can greatly improve the sensing resolution and
make sensor networks robust to swiftly changing
environment. Some budding applications of wireless sensor
networks are wildlife habit study, environmental observation
and health care monitoring. Since wireless sensor nodes are
power-constrained devices, long-haul transmissions should
be kept to minimum in order to expand the network lifetime
[3-6]. Thus, direct communications between nodes and the
base station are not intensely encouraged. An effective
methodology to perk up efficiency is to arrange the network
into several clusters (figure 1), with each cluster electing one
node as its leader or cluster head (CH). A cluster head collects
data from other sensor nodes in its cluster, directly or hopping
through other nearby nodes. The data collected from nodes of
the same cluster are extremely correlated. Data can be
amalgamated during the data aggregation process. The fused
data will then be transmitted to the base station directly or by
multi-hop fashion. In such an arrangement, only cluster heads
are required to transmit data over larger distances.
This paper gives a profound description about energy
efficient hierarchical distributed clustering algorithm. The
remaining nodes will need to do only short-distance
transmission. To distribute the workload of the cluster heads
amidst the wireless sensor nodes, cluster heads will be
reelected from time to time. Clustering follows some
projected advantages like localizing route setup within a
particular cluster radius, efficient topology maintenance,
energy efficiency, utilization of communication bandwidth
efficiently and makes best use of network lifetime. Since
clustering makes use of the mechanism of data aggregation,
unnecessary communication between the sensor nodes, CH
and BS is avoided [7-12]. Energy consumption of wireless
sensor nodes is greatly trimmed down and the overall network
lifetime can thus be prolonged [13-18]. The rest of the paper
is organized as follows. A literature review of existing
distributed clustering algorithms is given in Section 2. The
hierarchical distributed clustering algorithm giving
inspiration to this work is described in Section 3. Section 4
elaborates the details of the simulation results. Finally the last
section gives the conclusion.
Fig.1. Clustering mechanism in a wireless sensor network
2. REVIEW OF CLUSTERING ALGORITHMS
Extensive research efforts have been made to minimize the
energy consumption and to prolong the lifetime of WSNs.
The algorithms described in this section are completely
ABSTRACT
Extending the longevity, is a significant job to be accomplished by these sensor networks. The traditional routing protocols could not be applied here,
due to its nodes powered by batteries. Nodes are often clustered in to non-overlapping clusters, so as to provide energy efficiency. A concise overview
on clustering processes, within wireless sensor networks is given in this paper. But it is difficult to replace the deceased batteries of the sensor nodes.
A distinctive sensor node consumes much of its energy during wireless communication. This research work suggests the development of a
hierarchical distributed clustering mechanism, which gives improved performance over the existing clustering algorithm LEACH. The two hiding
concepts behind the proposed scheme are the hierarchical distributed clustering mechanism and the concept of threshold. Energy utilization is
significantly reduced, thereby greatly prolonging the lifetime of the sensor nodes.
Keywords: Wireless sensor network, sensor node, cluster head, base station, residual energy, energy utilization and network lifetime.

Page | 25
distributed and CH changes from node to node based on some
parameters. They tend to vary mainly in the methodology by
which the CH is elected.
Bandyopadhyay and Coyle anticipated EEHC, which is a
randomized clustering algorithm which organizes the sensor
nodes into hierarchy of clusters with an objective of
minimizing the total energy spent in the system to
communicate the information gathered by the sensors to the
information processing center. It has variable cluster count,
the immobile CH aggregates and relays the data to the BS. It is
applicable for extensive large scale networks. The peculiar
drawback of this algorithm is that, few nodes stay
un-clustered throughout the entire clustering process.
Barker, Ephremides and Flynn proposed LCA [19], which is
mainly developed to avoid the communication collisions
among the nodes by using a TDMA time-slot. It makes use of
single-hop scheme, attains better degree of connectivity when
CH is selected randomly. The updated version of LCA, the
LCA2 was implemented to decrease the number of nodes
compared to the original LCA algorithm. The main weakness
of this algorithm is, the single-hop clustering results in the
creation of numerous clusters and much energy is washed out.
Nagpal and Coore proposed CLUBS [20], which is
implemented with an idea to form overlapping clusters with
maximum cluster diameter of two hops. The clusters are
formed by local broadcasting and its convergence depends on
the local density of the wireless sensor nodes. This algorithm
can be implemented in asynchronous environment without
reducing efficiency. The main problem is the overlapping of
clusters, clusters having their CHs within one hop range of
each other, both clusters will collapse and CH election
process will restart.
Demirbas, Arora and Mittal brought out FLOC [21], which
exhibits double-band nature of wireless radio-model for
communication. The nodes can commune reliably with the
nodes in the inner-band range and unreliably with the nodes
that are in the outer-band range. It is scalable and thus exhibits
self-healing capabilities. It achieves re-clustering in constant
time and in a local manner, thereby finds valid in large scale
networks. The key drawback of the algorithm is, the nodes in
the outer band exercise unreliable communication and the
messages have the utmost probability of getting vanished
during communication.
Ye, Li, Chen and Wu proposed EECS [22], which is based on
the guessing that all CHs can communicate directly with BS.
The clusters have variable size, such that those closer to the
CH are larger in size and those farther from CH are smaller in
size. It is greatly energy efficient in intra-cluster
communication and excellent improvement network lifetime.
EEUC [23] is proposed for uniform energy consumption
within the network. It forms dissimilar clusters, with an
assumption that each cluster can have variable sizes.
Probabilistic selection of CH is the focal drawback of this
algorithm. Few nodes may be gone without being part of any
cluster, thereby no guarantee that every node takes part in
clustering mechanism. Yu, Li and Levy proposed DECA [25],
which selects CH based on residual energy, connectivity and
node identifier. It is greatly energy efficient, as it uses fewer
messages for CH selection. The main trouble with this
algorithm is that high possibility of wrong CH selection which
leads to discarding of all the packets sent by the sensor node.
Ding, Holliday and Celik proposed DWEHC [24], which
elects CH based on weight, a combination of residual energy
and its distance to neighboring nodes. It generates well
balanced clusters, independent on network topology. A node
possessing largest weight in a cluster is nominated as CH. The
algorithm constructs multilevel clusters and nodes in every
cluster reach CH by relaying through other intermediate
nodes. It shows an enormous improvement in intra-cluster and
inter-cluster energy consumption. The major problem occurs
due to much energy utilization by several iterations until the
nodes settle in most energy efficient topology.
HEED [2] is a well distributed clustering algorithm in which
CH selection is done by taking into account the residual
energy of the nodes and the intra-cluster communication cost
leading to prolonged network lifetime. It is clear that it can
have variable cluster count and supports heterogeneous
sensor nodes. The CH is stationary which carries out data
aggregation and relaying of the fused data to the BS. The
problems with HEED are its application limited only to static
networks, the assumption of complex probabilistic methods
and multiple clustering messages per node for cluster head
selection even though it prevents random selection of cluster
head.
LEACH [1] is one of the most popular clustering mechanisms
for WSNs and it is considered as a representative energy
efficient protocol. In this protocol, sensor nodes are grouped
together to form a cluster. In every clusters, one sensor node is
chosen arbitrarily to act as a cluster head (CH), which collects
data from its member nodes, aggregates them and then
forwards to the base station. It separates the operation unit
into several rounds and each round consists of two phases,
namely set-up phase and the steady state phase. During the
set-up phase, clusters are created and cluster heads are
selected.
Fig.2. Evaluation of LEACH algorithm

Page | 26
Gone selecting itself as a CH, the node generally broadcasts
an advertisement message which contains its own ID. The
non-cluster head nodes can make a decision, which cluster to
join according to the strength of the received advertisement
signal. After the decision is made, every non-cluster head
node must transmit a join- request message to the chosen
cluster head to specify that it will be a member of the cluster.
The cluster head produces and broadcasts the time division
multiple-access (TDMA) schedule to swap the data with
non-cluster sensor nodes without any collision after it
receives all the join-request messages. The steady phase
begins after the clusters are fashioned and the TDMA
schedules are broadcasted.
All the sensor nodes throw their data to the cluster head once
per round during their allocated transmission slot based on the
TDMA schedule and in other time, they turn off the radio in
order to reduce energy consumption. However, the cluster
heads must remain awake all the time. Therefore, it can
receive every data from the nodes within their own clusters.
On receiving all the data from the cluster, the cluster head
perform data aggregation and onwards it to the base station
directly. This is the complete process of steady phase. After a
certain predefined time duration, the network will step into
the next round. LEACH is the simplest clustering protocol
which processes cluster approach and it can prolong the
network lifetime when compared with multi-hop routing and
static routing. However, there are still some hiding drawbacks
that should be considered.
LEACH does not take into account the residual energy to
select cluster heads and to construct clusters. As a result,
nodes with lesser energy may be selected as cluster heads and
then die much earlier. Moreover, since a node selects itself as
a cluster head only according to the value of probability, it is
tough to guarantee the number of cluster heads and their
distribution. To overcome the inadequacy in LEACH, a
hierarchical distributed clustering mechanism is proposed,
where clusters are arranged in to hierarchical layers. Instead
of cluster heads directly sending the aggregated data to the
base station, sends them to their next layer cluster heads.
These cluster heads send their data along with those received
from lower level cluster heads to the next layer cluster heads.
The cumulative process gets repeated and finally the data
from all the layers reach the base station.
3. FEATURES OF THE PROPOSED SYSTEM
The initial step in the creation of LEACH (Low Energy
Adaptive clustering of Hierarchy), is the creation of clusters.
More specifically, each sensor nodes decides whether or not
to turn into the cluster head for the current round. The
decision is based on the priority and on the number to time the
node has been a cluster head so for. The cluster nodes brings
together the data and send them to the cluster head. The radio
to each cluster nodes can be turned off when there is no
sensing happens. When all the data have been received, the
cluster head aggregates the data in to single composite signal.
The composite signal is then sent to the base station directly.
Fig.3. Evaluation of the proposed algorithm
LEACH protocol has the weakness, when periodic
transmissions are unnecessary, thus causing pointless power
consumption. The election of cluster head is based on priority,
hence there is a possibility for weaker nodes to be drained
because they are elected to be cluster heads as frequently as
the stronger nodes. Moreover, the protocol is based on the
assumptions that all nodes commence with the same amount
of energy capacity in each election round and all the nodes
can transmit with enough power to reach the base station if
needed. Nevertheless, in many cases these assumptions are
impractical. Also the base station should keep track on the
sensor nodes in order to choose which node has the highest
residual energy. Hence needless transmissions occur between
the base station and cluster nodes, thereby causing increased
power consumption.
The proposed work suggests a new idea over the existing
techniques. In case of existing technique (figure 2), the
aggregated data is sent to the base election directly by the CH,
which leads to more energy usage. In the proposed algorithm
the aggregated data is forwarded only to the next layer cluster
head (figure 3), cutting down the communication distance
between cluster head and the base station.
Two thresholds are employed namely hard threshold and soft
threshold. Hard threshold is the bare minimum possible value,
of an attribute to trigger a wireless sensor node to switch on its
transmitter and transmit to the cluster head. Soft threshold is a
little change in the value of the sensed attribute that triggers
the node to switch on its transmitter and transmit data. The
hard threshold tries to trim down the number of transmission
by allowing their nodes to transmit only when the sensed
attribute is beyond a critical value. In a similar way, the soft
threshold further lessens the number of transmissions that
might have otherwise occurred when there is little or no
change in the sensed attribute. At each cluster change, the
values of both the thresholds can be changed and thus
enabling the user to control the tradeoff between energy
efficiency and data accuracy. This method reduces unwanted
transmissions, trimming down the energy utilization. The
main actions in the set-up phase are election of candidate
nodes, selection of cluster heads, scheduling at each cluster

Page | 27
and discovery of cluster head for CH-to-CH data
transmission. During set-up phase, every node first decides
whether or not it can become a candidate node in each region
for the current round. This choice is based on the value of the
threshold T(n) as used in LEACH protocol. As seen in
equation 1, p should be given a large value in order to elect
many candidate nodes. The cluster heads are elected among
the candidate nodes. An advertisement message is used to
elect cluster heads. For this, the candidate nodes employ a
CSMA MAC protocol. Each candidate node broadcasts an
advertisement message inside its transmission range and is
dependent on the utmost distance between these levels.
In the proposed scheme, the advertisement range is given
double of the maximum distance to cover other levels. When a
candidate node is located within a × Advertisement Range
where the value of a is predetermined between 0 and 1, it has
to give up qualification of candidate node and has to end up
joining the competition.
An ordinary node, by contrast, decides the cluster to which it
will belong for this round. This choice is based on the signal
strength of the advertisement message. After each node has
decided to which cluster it belongs, node must transmit its
data to the suitable cluster head. After cluster head receives all
the messages from the nodes that would like to be
incorporated in the cluster and based on the number of nodes
contained in the cluster, the cluster head creates a TDMA
schedule and assigns each node a time slot when it can
transmit.
Each cluster head broadcasts this same schedule back to the
nodes in the cluster. After schedule creation, each cluster head
performs cluster head discovery to discover an upward cluster
head to reach the sink. For this, each cluster head uses
two-way handshake technique, with REQ and ACK messages.
Each cluster head broadcasts REQ message within the
advertisement range. Upward cluster head on receiving this
REQ message transmits ACK message back to the cluster
head that had transmitted the REQ message.
The steady-state phase of the proposed scheme is analogous
to other cluster-based protocols. Main activities of this phase
are sensing and transmission of the sensed data. Each nodes
senses and transmits the sensed data to its cluster head
according to their own time schedule.
When all the data has been received, the cluster head perform
data aggregation in order to reduce the amount of data.
Finally, each cluster head transmits data to the sink along the
CH-to-CH routing path which have been fashioned during the
set-up phase. After all the data is transmitted or a definite time
is elapsed, the network goes back into the set-up phase again
and the next round begins by electing the candidate nodes.
4. SIMULATION STUDY
All the simulations were carried using GloMoSim considering
15 sensor nodes. For the simulations, a network model similar
to the one used in the conventional clustering protocols is
assumed with the following properties.
Table 1. Simulation parameter setup
Parameter Acronym Values
Cluster topology (m)
Tx/Rx electronics constant
Amplifier constant
CH energy threshold
Packet size
Number of nodes
Transmission range
Sensing range
Cluster range
Ct
Etx/rx
Eamp
Eth
p
N
Rbc
Rsense
Rcluster
100 x 100 m2
50nJ/bit
10pJ/bit/m2
10-4
J
50 bytes
15
70m
15m
30m
The sensor nodes are outfitted with power control
capabilities. For the experiments, the network parameters and
the communication energy parameters are set as shown in
table 1. The deployment of wireless sensor nodes are shown in
figure 4. Here the nodes are assumed to be static. The nodes
organize into hierarchical group of clusters, short while after
the deployment (figure 5). The cluster heads starts forwarding
the aggregated data to the next higher layered cluster head
immediately after hierarchical layers are formed. The process
gets terminated for one round when all the aggregated data
reaches the base station.
Fig.4. Nodes deployment in the proposed algorithm
Fig.5. Cluster formation in the proposed algorithm
The radio channel is assumed to be symmetrical in manner.
Thus, the energy required to transmit a message from a source
to a destination node is same as the energy required to
transmit the same message from the destination node back to
the source node. Moreover, it is mainly assumed that the
communication medium is contention free. Hence there is no

Page | 28
need for retransmission of data. The initial energy of each
node is assumed to be the identical.
The total system energy usage is the sum total of energy
consumed during communication, processing, etc., which is
the overall energy consumed for the complete clustering
mechanism by the whole sensor network. As discussed in the
previous section, LEACH algorithm uses more energy for
communication between nodes and the cluster heads. It
distributes the loading of cluster heads to all the nodes in the
network by switch the cluster heads from time to time. Due to
two-hop arrangement of the network, a node far from CH will
have to consume more energy than a node nearer to the cluster
head. This introduces a rough distribution of energy among
the cluster members, affecting the total system energy. The
uneven distribution of energy among the cluster members is
avoided in the proposed algorithm by the usage of
hierarchical clustering mechanism.
In the proposed algorithm, fewer communication energy is
required which could be understood from the simulations. It
uses the concept of threshold to further reduce the
communication energy. From the simulation, it is also clear
that the slope of LEACH algorithms is maximum, hence
consuming the available energy easily compared to the
proposed algorithm. Also in the proposed algorithm, parting
among the layers is optimized to use optimum power for each
layer. From figure 6, the system energy usage of the proposed
algorithm is optimum for discrete number of rounds. But in
case of LEACH, the energy usage is in a gradual manner. This
positive performance of the proposed algorithm is mainly by
the reduction in long-haul communications between the
cluster head and base station.
Fig.6. System energy usage versus number of rounds
The node death rate is the measure of the number of nodes die
over a particular number of rounds, from the beginning of the
process. When the data rate enlarges, the node death rate also
increases equivalently. The networks formed by LEACH
show periodical variations in data collection time. This is due
to the selection function reliant on the number of data
collection process. As the CH selection of LEACH is a
function of the number of completed data collection
processes, the number of cluster changes periodically. This
raises up the node death rate. The proposed algorithm uses a
restricted data collection process, as the concept of
hierarchical clustering is employed. Also the proposed
algorithm has an excellent control over the number of
connections between the cluster nodes, cluster heads and base
station. In LEACH, there is no control over the number of
connections, which increases the data collection time, thereby
increasing the data rate and node death rate. From figure 7, all
the nodes die early in 3000 rounds for LEACH algorithm. The
proposed algorithm shows prolonged performance, as all the
nodes die only in 4500 rounds. Hence, the proposed algorithm
shows excellent reduction in the node death rate compared to
LEACH. This is mainly by the usage of soft threshold and
hard threshold concept to reduce the redundant aggregated
data transmission from cluster head to the base station.
Fig.7. Node death rate versus number of rounds
5. Conclusion
This paper is concerned with the introduction of hierarchical
clustering mechanism in wireless sensor networks with the
inclusion of threshold concept within the cluster head. The
main feature of this proposed algorithm compared to the
existing clustering mechanism (LEACH), is that the entire
aggregated data is transmitted by the cluster head to the base
station by forwarding through next higher layer cluster heads.
Also soft threshold and hard threshold concepts are employed
to further reduce the number of transmission from cluster
head to the base station. Hence energy wastage by long
distance transmission is avoided, thereby reducing energy
utilization to much extent. The node death rate is reduced to a
greater extent compared to the existing LEACH algorithm.
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Highly Scalable Energy Efficient Distributed Clustering Mechanism in Wireless Sensor Networks Based on Hierarchical Approach