Efficient Cluster Head Selection in Wireless Sensor Networks. Efficient Cluster Head Selection in Wireless Sensor Networks.

1. 
Abstract— Wireless sensor network (WSN) refers to a
group of spatially dispersed and dedicated sensors for
monitoring and recording the physical conditions of the
environment and organizing the collected data at a
central location. Monitoring is common application of
WSN network. One can see large number of
applications of WSN involves area monitoring, health
care monitoring, environmental monitoring like air
pollution monitoring, forest fire detection, waterquality
monitoring, landslide detection etc. and industrial
monitoring like machinehealthmonitoring, data center
monitoring, data logging etc.. Delivery of Sensor data
must follow the time constraints so that appropriate
observations can be made or actions taken. Very few
results exist who meet real time requirements in WSN.
Most protocols either ignore real-time or simply
attempt to process as fast as possible ignoring data
fusion, data transmission, target and event detection
and classification, query processing, and security. In
wireless sensor network, certain areas are covered by
large number of sensornodes. Sensornodes are small in
size with limited battery power, less processing power,
less bandwidth. Wireless sensor networks need to
minimize energy consumption to increase network
lifetime. Clustering sensors can save energy and hence
increase the lifetime of sensornodes. Clustering sensors
is one of the important methods to prolong the network
lifetime in wireless sensor networks. It includes
grouping of sensor nodes and then electing one cluster
head from each cluster to collect data from each node,
aggregate the data and then forward the aggregated
data to base station. This helps in decreasing the energy
of sensor node and save it for further use. Hence
selection of cluster head node is becoming more
important in order to increase lifetime of network and
remaining energy level. Honey-Bee Mating algorithm
executes faster in the process of cluster head selection
and even is energy efficient. Particle Swarm
Optimization Algorithm is inefficient for cluster head
selection. The Breeding Fish Swarm Optimization
Algorithm and the Firefly Algorithm increases the
network lifetime whereas the Genetic algorithm
increases the complexity. Naïve Bayes Classifier
algorithm used for selection of cluster head increases
the network lifetime but the actual clustering of sensor
nodes is not efficiently done. Modified Honey-Bee
Mating Optimization Algorithm can be made use of,
where firstly instead of selecting the cluster head
randomly we can apply some algorithm where we can
select the positive properties of the node and then
instead of applying heuristic search we can apply
classification algorithm to get better results. A small
effort is taken here, to group all algorithms for energy
efficient cluster head selection.
Keywords— Wireless Sensor Networks, Cluster Head
Selection, Honey-Bee Mating Optimization.
I. INTRODUCTION
Wireless Sensor Network is defined as a network
of devices that communicates all the information
gathered from a monitored field through wireless
links. The data is transmitted through multiple nodes
and the data is connected to other networks through a
gateway like Ethernet. It consists of base station and
multiple nodes. Depending on the type of
environment, Wireless sensor networks are divided
into five types,
1. Terrestrial WSN’s: In this type, there are
hundreds and thousands of wireless sensor
nodes connected to the base station in structured
or unstructured manner. Minimum battery
power issue is achieved by using low duty cycle
operations, minimizing delays and optimal
routing.
2. Under-Ground WSN’s: Here, nodes are
deployed underground to monitor conditions
occurring there and to relay the conditions there
sink nodes are located above the ground. The
limited battery power is difficult to recharge and
hence creates a challenge of heavy loss of
energy and signal loss.
3. Under-Water WSN’s: In this type, sensor nodes
are deployed under water to gather data. This
creates long propagation delay, bandwidth and
sensor failures.
4. Multimedia WNS’s: These are enabled to track
and monitor the events in the form of
multimedia. Here, nodes are equipped with
microphones and cameras. It consumes high
energy, high bandwidth, data processing and
Efficient Cluster Head Selection in Wireless
Sensor Networks.

2. compressing techniques.
5. Mobile WSN’s: It is a collection of sensor
nodes that move on their own and are connected
in a physical environment. It includes better and
improved coverage and is more energy efficient
compared to others.
Challenges of WSN are as follows:
Energy: Energy is consumed for node operations
such as sensing, data collection and network
operations like data communications via different
communication protocols. Batteries are small and
need to be replaced or recharged, which is not always
possible.
Harsh Environment Conditions: Due to harsh
environment conditions, sensors can malfunction and
give inaccurate information to other nodes.
Self-Management: WSN consists of large number of
sensornodes generally deployed statically. But due to
failure of nodes WSN topology changes frequently. It
is required that a sensor network systembe adaptable
to changing connectivity.
Hardware and Software Issues: Due to tiny size
and limited amount of energy source, the nodes have
also restricted resources such as CPU performance,
memory, communication bandwidth and range.
Heterogeneity: Heterogeneity arises when two
completely different WSN communicate with each
other. Heterogeneity can create new issues in
communications and network configuration.
Data Freshness: Various WSN applications require
real time operations; to achieve this data should reach
to sink within the tolerable time limit.
Quality of Service (QoS): QoS is the measure for
competence of Sensor network in meeting application
specific requirements. QoS network perspective
refers to problem of effectively managing the energy
and bandwidth, along with satisfying application
requirements.
Deployment: Deployment means implementing
sensor nodes in real world scenarios.
Operating System (OS): The OS of sensor must be
capable of providing basic memory management and
resource management features, but should be less
complex as compared to general OS.
Security: Confidentiality means nodes should
encrypt sensed data, prior to its transmission to relay
node or base station.
Fault Tolerance: The property of fault tolerance
implies WSN should remain operational in case of
faulty sensors and death of sensor nodes.
Localization: The problem of localization deals with
learning the physical location of the deployed nodes.
Localization is performed with the help location
discovery algorithms.
Energy consumption is important in Wireless Sensor
Network using some algorithms and by doing some
hardware configurations for energy efficiency. There
are various methods used to achieve energy
efficiency in WSN. Following are various methods
used:
1. Duty Cycling
2. Data Handling
3. Reliable Routing Protocol and Overhead
Reduction
4. Mobility
5. Fast communication and Energy Efficient
Forwarding Scheme
6. Topology Management
7. Energy Efficiency Based on QoS.
Moving ahead with clustering,
II. LITERATURE SURVEY
As discussed in introduction, we will discuss the
research done on energy efficient cluster head
selection. Here, we will concentrate on all the
algorithms that contribute to energy efficiency.
Jafarizadeh et. al. [1], has made use Naïve Bayes
algorithm for classification to find cluster head node
which is efficient and increases the network lifetime
in wireless sensor network. The parameters used to
create dataset to select cluster head node consists of
the position of cluster node, remaining energy/power
level of node, distance from base station, and the
class. After dataset creation Naïve Bayes classifier is
applied using MATLAB for simulation. Some default
values and assumptions were made and the
performance was evaluated. The results obtained led
to the conclusion that Naïve Bayes Classifier gave
better outcomes than LEACH.
Zahedi et. al. [2], has shown the effect of using
reservation to reduce message transmitting energy
and dissipation. By using the reservation mechanism,
the number of communication messages can be
reduced. Author has proposed reservation-based
clustering approach, which shows significant
difference in reduction of energy dissipation. By
adding reservation phase at start of network
configuration, it saves energy and lifetime of network
at first. But, later the energy decreased and reduced
the network’s control messages effectively.
K. Vijayalakshmi et. al. [3], has proposed a
method based on Particle Swarm Optimization and
Tabu Search algorithm. It has helped in routing the
optimal path selection to increase the lifetime of the
network. The results shown has improved the quality
of cluster formation, percentage of live nodes and

3. reduced the rate of packet loss as well as the delay.
The comparison of proposed hybrid heuristic
approach of Tabu Search and Particle Swarm
Optimization algorithm was done with LEACH (Low
Energy Adaptive Clustering hierarchy) algorithm
proved that the Multi-hop LEACH protocol was
found to be inefficient.
Selvi et. al. [4], has made use of the Honey Bee
Optimization technique in order to increase the
network lifetime and throughput and gives better
performance related to node’s scalability, quality and
energy efficiency. The technique used finds an
optimal path that has a low cost to reduce energy
consumption. Hence construction of energy clusters
was done from the inspiration of biologically
efficient Bee Colony approach. But after
implementation the packet delivery rate was found to
be higher than other approaches.
Sengottuvelen et. al. [5], proposed an improved
Artificial Fish Swarm Optimization algorithm in
which the cluster head selection was done in the
optimized way. The results that were obtained had
fast convergence, better fault tolerance capability and
did better local search for optimization. The proposed
algorithm reduced packet loss and the network
lifetime was also improved.
Daflapurkar et. al. [6], proposed a method
consisting of three steps viz., construction of hop tree
from end to end in sensor nodes cluster head
selection and formation of clusters as well. The goal
was to design and simulate novel tree-based
distribution for efficient energy and aggregation of all
data collected from the sensors. The working was
based on Shortest path tree method for routing. The
obtained results outperformed the existing energy
efficient routing solutions.
Jha et. al. [7], implementation of different
variations of Genetic algorithm was implemented for
data communication on energy models in order to
obtain optimal energy consumption. Battery life of
sensor nodes was extended by the obtained energy
values making use of the parameters during data
communication.
Murugan et. al. [8], proposed Firefly Cyclic Grey
Wolf Optimization for optimal cluster head selection
simulation. The main focus was on energy
stabilization, minimization of distance between two
sensor nodes and the delay. It hybridized two
algorithms i.e., Firefly and Grey Wolf Optimization.
The performance of the algorithm was then compared
with Genetic Algorithm, Group Search Optimization,
Artificial Bee Colony, Fractional Artificial Bee
Colony, Firefly with Cyclic Randomization for
Cluster head selection. The performance of all
algorithms was compared on basis of lifetime of
network, efficiency of energy, statistics of dead
nodes. The proposed algorithm proved the network
lifetime prolonged.
Banakar Vinodkumar et al. [9], considers the
cost of sending as well as processing, therefore they
use short distance path as well as compression of the
data to reduce the power consumption. A robust
TARF (trust-aware routing framework) for dynamic
WSNs is designed and implemented. TARF provides
trustworthy and energy-efficient route without tight
time synchronization or known geographic
information, TARF proves effective against those
harmful attacks developed out of identity deception.
Priyanka Y Shah and et al [10] concludes that in
wireless sensor network energy is a scarce resource.
Concentration of data traffic towards sink causes
nearby nodes to deplete their batteries quicker than
other nodes, and leaves sink stranded. This problem
can be solved by keeping the sink node mobile.
Mobile sink saves more energy compared to
stationary sink node by moving and collecting
information from the field. Authors also proposed
rendezvous node rotation to avoid over utilization of
rendezvous nodes.
Kritika Varma and Sahil Dalwal [11] optimized
energy in WSN using hybrid BSA + LEACH. Results
of test performed proved that the proposed algorithm
surpasses and gives way better results than
WSNCABC, LEACH, and PEGASIS and proved to
be an energy efficient algorithm.
Indu and Sunita Dixit [12] enlist the challenges
and issues confronted by WSN, in which one of the
important constraints is energy optimization. The
paper also states the importance of WSN and its
applications.
Tarun Bala and et al [13] concludes that for
practical implementation of WSN, energy saving is
major concern in the resource constraint
environment. In terms of hardware, sensor network
must be scalable, and capable of fulfilling QoS
requirement, on software front the algorithms and
protocols used should be energy efficient. WSN has
emerged as an active research area, involving various
challenging topics such energy consumption, routing
algorithms, deployment and localization problems.

4. III. OPEN ISSUES
Algorith
m Used
Publicati
on
Findings Limitations
Naïve
Bayes
Springer Use of
Naïve
Bayes
Algorithm
to
determine
Cluster
Head
prolonged
the
Network
Lifetime.
In this, the
clustering
operation was
not carried
out.
LEACH Springe
r
Reduction
in message
message
transmissio
n and
energy
dissipation
Increade in
energy
comspumtion
due to addition
of reservation
phase.
Tabu
Search,
Particle
Swarm
Optimiza
tion
Springe
r
The
algorithms
were
proposed
to optimize
the routing
in WSN.
Honey
Bee
Optimiza
tion
IEEE Building
the energy
clusters
from the
inspiration
of
biological
honey bee
colony
approach.
Packet
delivery rate is
higher than
rest
algorithms.
Breeding
Artificial
Fish
Swarm
Optimiza
tion
Springe
r
Optimized
cluster
head
selection.
Packet loss
reduction and
improved
network
lifetime.
Tree
Based
Distributi
on
IEEE The results
obtained
after tree-
based
distributio
n for
energy
efficiency
and data
aggregatio
Reinforcement
learning for
cluster head
selection was
introduced.
n in WSN
outperform
ed existing
algorithms.
Genetic
Algorith
m
Springe
r
The results
obtained
extension
in battery
life usage.
Complexity of
inter-cluster
communicatio
n increased.
Firefly
and Grey
Wolf
Optimiza
tion
Int. J.
Wireles
s and
Mobile
Compu
ting
Selection
of cluster
head
Optimally,
minimizati
on od
distance
between
nodes and
minimizati
on of
delay.
Network
lifetime
prolonged.
TARF Int. J.
of
Compu
ter
Science
and
Mobile
Compu
ting
trustworth
y and
energy-
efficient,
increases
throughput
Mobile
sink
approach
IJAER
D
selection
of capable
nodes so
that all
nodes are
fairly
utilized
The no. of
rendezvous
nodes at each
round remains
same as old
rendezvous
node, so load
balancing is
disturbed
BSA +
LEACH
IJIRCC
E
Highest
level of
energy
optimizatio
n is
achieved
Complications
of system
increased,
unable to save
WSN from
attackers
during steady
phase
IV. CONCLUSION AND FUTURE SCOPE
Clustering sensors is one of the important
methods to prolong the network lifetime in wireless
sensornetworks. It includes grouping of sensornodes
and then electing one cluster head from each cluster
to collect data from each node, aggregate the data and

5. then forward the aggregated data to base station. This
helps in decreasing the energy of sensor node and
save it for further use. Hence selection of cluster head
node is becoming more important in order to increase
lifetime of network and remaining energy level.
Naïve Bayes Classifier algorithm used for
selection of cluster head increases the network
lifetime but the actual clustering of sensor nodes is
not efficiently done. Modified Honey-Bee Mating
Optimization Algorithm can be made use of, where
firstly instead of selecting the cluster head randomly
we can apply some algorithm where we can select the
positive properties of the node and then instead of
applying heuristic search we can apply classification
algorithm to get better results.
V. REFERENCES
[1] Vahid Jafarizadeh,Amin Keshavarzi,
Tajedin Derikvand, “Efficient cluster head
selection using Naive Bayes classifier for
wireless sensor networks”, Springer , 2016.
[2] Abdulhamid Zahedi, Mahdi Arghavani,
Fariborz Parandin,Abbas Arghavani,
“Energy Efficient Reservation-Based
Cluster Head Selection in WSNs”,
Springer , 2018.
[3] K. Vijayalakshmi,P. Anandan , “A multi
objective Tabu particle swarm optimization
for effective cluster head selection in
WSN” , Springer , 2018.
[4] Selvi M,Nandhini C ,Thangaramya K
,Kulothungan K, Kannan A , “HBO Based
Clustering and Energy Optimized Routing
Algorithm for WSN” , IEEE Eighth
International Conference, 2016..
[5] P. Sengottuvelan ,N. Prasath , “BAFSA:
Breeding Artificial Fish Swarm Algorithm
for Optimal Cluster Head Selection in
Wireless Sensor Networks” , Springer
2016.
[6] Pradnya M.Daflapurkar, Dr. Meera Gandhi
, Dr.Bhagwan Patil, “Tree based
Distributed Clustering Routing Scheme for
Energy Efficiency in Wireless Sensor
Networks”, IEEE Conference , 2017.
[7] Sunil Kr. Jha,Egbe Michael Eyong, “An
energy optimization in wireless sensor
networks by using genetic algorithm”
,Springer , 2017.
[8] T. Senthil Murugan and Amit Sarkar,
“Optimal cluster head selection by
hybridisation of firefly and grey wolf
optimisation”,Int. J. Wireless and Mobile
Computing, 2018.
[9] Banakar Vinodkumar, Mrs. Geetha N B,
Mohamed Rafi, “Energy Optimization in
Wireless Sensor Networks”, International
Journal of Computer Science and Mobile
Computing, 2015
[10] Priyanka Y Shah, Samir D Trapasiya,
“Energy Optimization In Wireless Sensor
Network Using Mobile Sink Approach”,
International Journal of Advance
Engineering and Research Development,
2015
[11] Kritika Varma, Sahil Dalwal, “An
Approach to Energy Optimization in WSN
Using Hybrid Leach and Bird Swarm
Algorithm”, International Journal of
Innovative Research in Computer and
Communication Engineering, 2018
[12] Indu, Sunita Dixit, “Wireless Sensor
Networks: Issues & Challenges”,
International Journal of Computer Science
and Mobile Computing, 2014.
[13] Tarun Bala, Varsha Bhatia, Sunita
Kumawat, Vivek Jaglan, “A survey: issues
and challenges in wireless sensor
network”, International Journal of
Engineering & Technology, 2018