This document compares various energy optimization clustering algorithms for wireless sensor networks (WSNs), focusing on the challenges of limited energy supplies and the need to prolong network lifetime. It discusses different clustering mechanisms, their classifications, and performance analyses based on parameters like energy efficiency, network lifetime, and reliability. The paper concludes that hierarchical routing and probabilistic clustering algorithms are among the most effective approaches for enhancing the reliability and efficiency of WSNs.

1. ICRTEDC-2014 20
IJCSIT, Vol. 1, Spl. Issue 2 (May, 2014) e-ISSN: 1694-2329 | p-ISSN: 1694-2345
GV/ICRTEDC/05
COMPARISON OF ENERGY OPTIMIZATION
CLUSTERING ALGORITHMS IN WIRELESS
SENSOR NETWORKS
1
Manpreet Kaur, 2
Jagroop Kaur
1,2
Computer Science Department, UCOE, Punjabi University, Patiala, India
1
manpreetaulakh65@gmail.com, 2
jagroop_80@rediffmail.com
ABSTRACT : Fast growth of wireless services in recent
years is an indication that considerable value is placed on
wireless networks. Wireless devices have most utility when
they can be used anywhere at any time. One of the greatest
challenges is limited energy supplies. Therefore, energy
optimization is one of the most challenging problems in
wireless networks. In recent years, Wireless Sensor
Networks have gained growing attention from both the
research community and actual users. As sensor nodes are
generally battery-energized devices, so the network lifetime
can be widespread to sensible times. Therefore, the crucial
issue is to prolong the network lifetime. In this paper, two
Energy Optimization Schemes Clustering and Direct
Diffusion for Wireless Sensor Networks (WSN) has been
compared on the basis of different parameters like
scalability, energy efficiency and reliability etc. on the basis
of this comparison we can use better Optimization technique
according to the situation.
Keywords— mobile ad hoc network, two-tiered architecture,
clustering, system lifetime, CH-rotation, Node mobility, In
cluster Topology.
I. INTRODUCTION
WSN consists of spatially distributed sensor to monitor
physical or environmental conditions. It described as a
network of nodes that cooperatively sense and may control
the environment enabling interaction between persons or
computers. The Wireless Sensor Network consists of
numerous applications for monitoring different
environments. Sensor node’s energy source is provided by
battery power. The lifetime of a sensor node is expected to
be months to years, because replacing or recharging a node
is complicated and unfavourable. So efficiently using energy
from the nodes has become a crucial challenge. So we here
explain different clustering algorithms for optimize use of
energy.
A. Clustering
Neighbouring sensor nodes generally have the data of
similar events because they collect events within a specific
area. If each node individually transmits the collected data
to the sink node, a lot of energy will be wasted to transmit
similar data to the sink node. The sensor nodes are
organized into a number of clusters in order to avoid such
energy wastes. In a clustering mechanism, the nodes that are
adjacent geographically are grouped to form a cluster. The
cluster head takes care of transferring the data to other
clusters within the network. The member nodes report their
data to the respective CHs. The CHs aggregate the data and
send them to the central base through other CHs.
B. Limitations in WSNs, that clustering schemes must
consider
 Limited Energy
 Network Lifetime
 Limited Abilities
 Application Dependency
C. Sensor network components
1) Sensor Node: A sensor node is the core component
of a WSN.
2) Clusters: Clusters are the organizational unit for
WSNs.
3) Cluster heads: Cluster heads are the organization
leader of a cluster.
4) Base Station: It provides the communication link
between the sensor network and the end-user.
5) End User: The data for a particular application may
make use of the network data over the internet,
using a PDA, or even a desktop computer.
Fig 1 General Sensor Network Architecture
II. CLASSIFICATION OF CLUSTERING
ALGORITHMS
There are many different classifications based on the
characteristics and functionality of the sensors in the
cluster:-

2. 21 ICRTEDC -2014
Fig 2. Classification of Clustering Alrorithms
A. According to Cluster-head selection
Fig 3 Classification based on Cluster-head
1) Heuristic Algorithms:
An heuristic algorithm is an algorithm has goals to Finding
an algorithm with reasonable run-time and With finding the
optimal solution.
2) Weighted Scheme:
These algorithms use a combination of metrics such as the
remaining energy, transmission power, etc.,to achieve more
generalized goals than single-criterion protocols.
3) Hierarchical schemes:
Clustering algorithm in which cluster head candidates
compete for the ability to elevate to cluster head for a given
round.. If a given node does not find a node with more
residual energy, it becomes a cluster head.
4) Grid Schemes:
In this algorithm one of the sinks (called the primary sink),
dynamically, and randomly builds the cluster grid. The
cluster heads are arranged in a grid-like manner.
B. According to Algorithm complexity
1) Variable convergence time algorithms:
Variable convergence time algorithms showed their
suitability for networks having large number of nodes and
enable more control of the cluster properties than the
constant time ones.
2) Constant convergence time algorithms:-
Clustering algorithms that converge completely in a fixed
number of iterations, regardless of the size of the nodes
population are called constant convergence time clustering
algorithms.
Fig.4 Classification based on Algorithm Complexity
C. According to cluster formation criteria
1) Probabilistic Clustering Approaches:
Some algorithm are all probabilistic in nature and their main
objective was to reduce the energy consumption and prolong
the network lifetime. Some of them follow a random
approach for CH election.
1) Non-probabilistic Clustering Approaches:
Here criteria for CHs election and cluster formation, based
on the nodes’ proximity (connectivity, degree, etc.) and on
the information received from other closely located nodes.
Fig.5 Classification based on Algorithm Complexity
III. OVERVIEW OF CLUSTERING ALGORITHMS
A. Linked Cluster Algorithm (LCA)
LCA, was one of the very first clustering algorithms
developed. In LCA, each node is assigned a unique ID
number and has two ways of becoming a cluster head. The
first way is if the node has the highest ID number in the set
including all neighbour nodes and the node itself. The
second way, assuming none of its neighbour are cluster
heads, then it becomes a cluster head.
B. Weighted Clustering Algorithm (WCA)
WCA a corresponding weight-based protocol was proposed
where the CH election process is based on the computation
of a “combined weight” Wv for each node, which takes into
account several system parameters such as the node degree,
the transmission power, mobility, and the remaining energy
of the node: Wv = w1Tv +w2Dv +w3Mv +w4Pv. The node
with the smallest weight in its neighbourhood is chosen as a
CH.
C. Low Energy Adaptive Clustering Hierarchy (LEACH)

3. ICRTEDC-2014 22
It’s an hierarchical, probabilistic, distributed, one-hop
protocol. Initially a node decides to be a CH with a
probability “p” and broadcasts its decision. After its
election, each CH broadcasts an advertisement message to
the other nodes and each one of the other (non-CH) nodes
determines a cluster to belong to, by choosing the CH that
can be reached using the least communication energy. A
node becomes a CH for the current rotation round if the
number is less than following threshold.
D. Energy Efficient Clustering Scheme(EECS)
It is a distributed, k-hop hierarchical clustering algorithm
aiming at the maximization of the network lifetime. Initially,
each sensor node is elected as a CH with probability “p” and
announces its election to the neighbouring nodes within its
communication range. Consequently, any node that receives
such CH election message and is not itself a CH, becomes a
member of the closest cluster.
E. Hybrid Energy-Efficient Distributed Clustering
(HEED)
The algorithm is divided into three phases.
In Initialization phase, percentage value, Cprob, used to
limit the initial CH announcements to the other sensors.
CHprob = Cprob * Eresidual/Emax,
where Eresidual is the current energy in the sensor, and
Emax is the maximum energy.In Repetition phase every
sensor goes through several iterations until it finds the CH
that it can transmit to with the least transmission power.
In Finalization phase each sensor makes a final decision on
its status. It either picks the least cost CH or pronounces
itself as CH.
F. PEGASIS
Power-Efficient GAthering in Sensor Information Systems
is a data-gathering algorithm. The algorithm presents the
idea that if nodes form a chain from source to sink, only 1
node in any given transmission time-frame will be
transmitting to the base station. Data-fusion occurs at every
node in the sensor network allowing for all relevant
information to permeate across the network.
G. GROUP
In this algorithm one of the sinks (called the primary sink),
dynamically, and randomly builds the cluster grid. The
cluster heads are arranged in a grid-like manner. Forwarding
of data queries from the sink to source node are propagated
from the Grid Seed (GS) to its cluster heads, and so on. The
GS is a node within a given radius from the primary sink..
IV. PERFORMANCE ANALYSIS OF CLUTERING
ALGORITHMS
Two major areas:-
A. Power, Energy and Network Lifetime
1) WCA:
• In terms of energy consumption, the algorithm tries
to achieve the most stable cluster architecture.
• This reduces system updates and hence
computation and communication costs
Fig 6.Comparison of Reaffiliations for Heuristic Algorithms
2)LEACH:
• No overhead is wasted making the decision of
which node becomes cluster head as each node
decides independent of other nodes.
• Each node calculates the minimum transmission
energy to communicate with its cluster head and
only transmits with that power level.
3)PEGASIS:
• During a given round, only 1 node in the network is
transmitting data to the base station
• Since each node communicates with its nearest
neighbour, the energy utilized by each node is also
minimized.
4) GROUP :
• Energy conservation is achieved by the lower
transmission distance for upstream data.
• In GROUP, data is transmitted short ranges along
the upstream path.
Fig 7.Comparison of GROUP and LEACH Algorithms
5)HEED:
• In this algorithm, network life time is prolonged
through:Reducing the number of nodes that
compete for channel access;
Cluster head updates, regarding cluster
topology.
B.Quality and Reliability of the Links
1) WCA:
• In terms of quality and reliability, the WCA
algorithm has the flexibility to be adapted to many
applications, assigning different weights to the
parameters of the combined weight
2)LEACH:

4. 23 ICRTEDC -2014
• The CSMA mechanism is used to avoid
collisions.Periodic rotation of cluster heads extend
the network lifetime, guaranteeing full connectivity
in the network for longer periods than conventional
algorithms.
3)PEGASIS:
• In PEGASIS each node communicates with its
nearest neighbour. This implementation may be
more susceptible to failure due to gaps in the
network.
4)EECS:
• Since EECS offers improved energy utilization
throughout the network, full connectivity can be
achieved for a longer duration.
5)GROUP:
• When a node fails in its attempt to communicate
with its cluster head it will send a broadcast
message to search and establish a new cluster head.
6)HEED:
• This is because HEED uses intra-cluster
communication cost in selecting its cluster heads.
Therefore the node distribution does not impact the
quality of communication.
V. COMPARISON OF PROPOSED ALGORITHMS
We compare clustering Algorithms LCA, WCA, LEACH,
EECS, HEED, PEGASIS, GROUP on the basis of Time
complexity, Node mobility, Cluster overlap, In cluster
topology, Cluster count, Clustering process, Ch-rotation,
multilevel parameters to choose best one approach.
Table 1.COMPARISON of CLUSTERING ALGORITHMS
Clustering
Approaches
Time
Complexity
Node
Mobility
LCA Variable Possible
WCA Constant No
LEACH Constant Limited
EECS Constant Limited
HEED Constant No
PEGASIS Variable No
GROUP Variable No
Clustering
Approaches
Clustering
process
CH-
Rotation
Multilevel
LCA Distributed No No
WCA Distributed No No
LEACH Distributed Yes No
EECS Distributed Yes No
HEED Distributed Yes No
PEGASIS Hybrid Yes No
GROUP Hybrid No No
VI. CONCLUSION
In wireless sensor networks, the energy limitations , Quality
of Service metrics expose reliability issues when designing
recovery mechanisms for clustering schemes. Hierarchical
routing and data gathering protocols, regarded as the most
efficient approach to support scalability in WSNs. To
prolong the lifetime of the network, the combined need for
fast convergence time and minimum energy consumption
led to appropriate fast distributed probabilistic (clearly
random or hybrid) clustering algorithms which quickly
became the most popular and widely used in the field.
VII. FUTURE WORK
Further improvements in reliability should examine possible
modifications to the re-clustering mechanisms following the
initial cluster head selection. In addition, other mechanisms
such as the ability of nodes to maintain membership in
auxiliary clusters can reinforce the current state of sensor
network reliability.
ACKNOWLWDGEMENT
First and foremost, I would like to thank God almighty for
life itself. All that I have is due to His grace and I give all
glory to him. With deep sense of gratitude I express my
sincere thanks to my esteemed and worthy supervisor Er.
Jagroop Kaur, Assistant professor, Department of Computer
Engineering, Punjabi University, Patiala for her valuable
guidance in carrying out this work.
REFERENCES
[1] A.A. Abbasi and M. Younis,” A survey on clustering
algorithms for wireless sensor networks”, Computer
Communications, 30, 2826–2841, 2007.
[2] K. Sohrabi et al., “Protocols for self-organization of a wireless
sensor network”, IEEE Personal Communications, 7(5), 16–27,
2000.
[3] R. Min et al.,” Low power wireless sensor networks”, in
Proceedings of International Conference on VLSI Design,
Bangalore, India, January 2001.
[4] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan,
“Energy Efficient Communication Protocol for Wireless Micro
Sensor Networks,” Proceedings of IEEE HICSS, Jan 2000.
[5] C. F. Chiasserini, I. Chlamtac, P. Monti, and A. Nucci, “Energy
Efficient Design of Wireless Ad Hoc Networks,” Proceedings of
European Wireless, Feb 2002.
[6]S. Bandyopadhyay and E. J. Coyle, “An Energy Efficient
Hierarchical Clustering Algorithm for Wireless Sensor Networks,”
IEEE INFOCOM, April 2003.
[7] D. J. Dechene, A. El Jardali, M. Luccini, and A. Sauer “A
Survey of Clustering Algorithms for Wireless Sensor
Networks”,Manual for University Course
[8] Ossama Younis, Marwan Krunz, and Srinivasan
Ramasubramanian “Node Clustering in Wireless Sensor Networks:
Recent Developments and Deployment Challenges”
Clustering
Approaches
Cluster
Overlap
In Cluster
Topology
Cluster
Count
LCA No 1-hop Variable
WCA No k-hop Variable
LEACH No 1-hop Variable
EECS No 1-hop Variable
HEED No 1-hop Constant
PEGASIS No 1-hop Constant
GROUP No k-hop Controlled

5. ICRTEDC-2014 24
[9] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirc”
Wireless sensor networks:a survey”; COMNET Vol. 38(2002), pp.
393422
[10] Do-hyun Nam, hong-ki min,” An Efficient Ad-Hoc Routing
Using a Hybrid Clustering Method in a Wireless Sensor Network”,
Wirelessand Mobile Computing, Networking and
Communications, pp. 60-60, Oct 2007
[11] Ma Chaw Mon Thein, Thandar Thein ,” An energy efficient
Cluster Head Selection for Wireless Sensor network”, 2010 Int.
Conf. on Intelligent systems, Modelling and simulation