Fire-LEACH: A Novel Clustering Protocol for Wireless Sensor Networks based on Firefly Algorithm, International Journal of Computer Science: Theory and Application

Vol. 1, No. 1, May 2014
Fire-LEACH: A Novel Clustering Protocol for Wireless
Sensor Networks based on Firefly Algorithm
E. Sandeep Kumar1, S.M. Kusuma1, B.P. Vijaya Kumar2
1 Department of Telecommunication Engg, M.S. Ramaiah Institute of Technology, Bangalore, Karnataka, India.
2 Department of Information Science & Engg, M.S. Ramaiah Institute of Technology, Bangalore, Karnataka, India.
Email: sandeepe31@gmail.com
ABSTRACT
Clustering protocols have proven to increase the network throughput, reduce delay in packet transfer and save
energy. Hence, in this work, we propose a novel clustering protocol that uses firefly algorithm inspired approach
towards improving the existing basic LEACH protocol for reduction in steady-state energy consumption, aiming to
enhance the network lifetime. The simulated results prove that implanting these kinds of computational intelligence
into the pre-existing protocols considerably improves its performance.
KEYWORDS
Clustering Protocol — Firefly Algorithm — LEACH Protocol — Computational Intelligence — Network Lifetime
— Energy Saving.

c 2014 by Orb Academic Publisher. All rights reserved.
1. Introduction
Wireless sensor networks are distributed systems with sensors
used to monitor the environment in which it is being deployed.
The application of sensor networks are vast and to mention few
of them, military monitoring, healthcare monitoring, disaster and
natural calamities monitoring, waste water monitoring, etc. These
sensor nodes are resource constrained and always there is a neces-sity
to construct novel algorithms and protocols that are energy
aware in their operation. Researchers have proposed many pro-tocols
in this direction, and use of computational intelligence is
also one of the wide spread approach. There are many heuristic
and meta heuristic algorithms used for designing the protocol. To
name few of them swarm intelligence including ant colony opti-mization,
bee colony optimization, particle swarm optimization,
genetic algorithms, intelligent water drops, glow worm optimiza-tion,
fire fly optimization, artificial immune systems, evolutionary
algorithms, neural networks, so on. The usage of these computa-tional
algorithms has lead to effective algorithm design in tackling
various issues in wireless sensor networks like routing, security
etc. The usage of firefly algorithms is the recent trend started
from 2008 in wireless sensor networks and few related works are
afore mentioned. Ming Xu et al. [1] proposed a work of using
firefly algorithm for finding optimal route in underwater sensor
networks by considering data correlation and their sampling rate
in sensor nodes. Geoffrey Werner-Allen et al. [2] proposed a
work of using Reachback Firefly Algorithm (RFA) for timing
synchronization and delay compensation in Tiny-OS based motes.
Song Cao et al. [3] proposed a method of using fire fly algorithm
in finding optimal location for sensor nodes. Our recent work
[4] proposes a bio-inpired clustering protocol based on Rhesus
Macaque animal’s social behavior. Bharathi et al. [5] propose
a data aggregation scheme using elephants swarm intelligence.
Bio-inspired computing is slowly gaining its momentum in the
present WSN research.
In this work, we present a methodology of using an algorithm
inspired by the fireflies behavior for energy efficient clustering
in wireless sensor networks, which serves to be a betterment
on the basic LEACH protocol. The algorithm was simulated in
MATLAB and results were compared with the LEACH, with
respect to energy saving in the steady phase energy.
The rest of the paper is organised as follows: section 2 deals
with the basic LEACH protocol, section 3 discusses the fireflies
behavior and algorithm, section 4 highlights the radio model
considered for the calculation of energy consumption, section 5
describes the proposed methodology, section 6 depicts the results
and discussions associated with the protocol, finally the paper
ends with the concluding remarks and the references.
2. LEACH Protocol
This section briefs out the LEACH (Low Energy Adaptive Clus-tering
Hierarchy) protocol which was proposed by W.R. Heinzel-man
et al. [6]. The protocol has two phases: set-up phase and
steady-state phase. The protocol executes in rounds. Each round
in LEACH has predetermined duration, through synchronized
clocks, nodes know when each round starts. The setup consists
of three steps. In Step 1 (advertisement step), nodes decide prob-
12

International Journal of Computer Science: Theory and Application
abilistically whether or not to become a Cluster Head (CH) for
the current round (based on its remaining energy and a globally
known desired percentage of CHs). Nodes that decide to do so
broadcast a message (adv) advertising this fact, at a level that
can be heard by everyone in the network. To avoid collision, a
carrier sense multiple access scheme is used. In step 2 (cluster
joining step), the remaining nodes pick a cluster to join based
on the largest received signal strength of an adv message, and
communicate their intention to join by sending a join req (join
request) message. Once the CHs receive all the join requests, step
3 (confirmation step) starts with the CHs broadcasting a confirma-tion
message that includes a time slot schedule to be used by their
cluster members for communication during the steady-state phase.
Given that all transmitters and receivers are calibrated, balanced
and geographically distributed clusters should result. Once the
the clusters are formed, the network moves on to the steady-state
phase, where actual communication between sensor nodes and
the Base Station (BS) takes place. Each node knows when is its
turn to transmit (step 4), according to the time slot schedule. The
CHs collect messages from all their respective cluster members,
aggregate data, and send the result to the BS(step 5). The steady-state
phase consists of multiple reporting cycles, and lasts much
longer compared to the setup phase.
3. Firefly algorithm
The section highlights the behavioral aspects of fireflies and the
firefly algorithm.
3.1 Behavior of Fireflies
There are around two thousand firefly species and most fireflies
produce short and rhythmic flashes of light. The pattern of flashes
is often unique for a particular species. The fundamental func-tions
of such flashes are to attract mating partners and preys.
Females respond to male’s unique pattern of flashing within the
same species. The light emitting from their body strictly obeys
the inverse square law i.e. as the distance between two flies in-creases,
the intensity of light decreases. The air absorbs light,
which becomes weaker and weaker as the distance increases. The
bioluminescence from the body of the fireflies is due to ‘luciferin’,
which is a heterocyclic compound.
Figure 1. Firefly (Scientific name: Photuris lucicrescens,
courtesy: Wikipedia.org).
These behaviors of fireflies have lead to implementation of
Firefly Algorithm (FA) that serves to be a metaheuristic algorithm
under computational intelligence.
3.2 Firefly Algorithm
This section highlights the implementation of the fireflies’ be-havior
as described by Xin-She Yang [7]. The algorithm was
formulated by assuming (i) All fireflies are unisexual, so that one
firefly will be attracted to all other fireflies. (ii) Attractiveness is
proportional to their brightness, and for any two fireflies, the less
bright one will be attracted by (and thus move to) the brighter one;
however, the brightness can decreases as the distance between
them increases. (iii) If there are no fireflies brighter than a given
firefly, it will move randomly. The brightness is associated with
the objective function and the associated constraints along with
the local activities carried out by the fireflies is represented by the
following algorithm.
Firefly Algorithm: Pseudo code
Nomenclature
- ui= ith firefly, i 2 [1;n];
- n= number of fireflies;
- max generation= count of the generations of fireflies (indicates
iteration limit);
- Ii= Light Intensity magnitude of ith firefly depending on the
objective function f (x);
- g = absorption co-efficient;
- ri j= distance between ith and jth fireflies.
- f (xi) = objective function of ith firefly, which is dependent on
its location xi that is of d-dimension
begin
Generate initial population of fireflies ui with location xi ,
i = 1;2;3:::n;
Define objective function f (x), where x = (x1;x2; :::xd)T ;
Generate initial population of fireflies xi , i = 1;2;3:::n;
Light intensity Ii of a firefly ui at location xi is determined
by f (xi);
Define light absorption coefficient g;
while (t < max generation) do
/*for all n- fireflies*/
for i=1:n do
/*for all n- fireflies*/
for j=1:i do
if (Ij > Ii) then
move firefly i towards j in d-dimension
else
end
end
Attractiveness varies with the distance r via
exp[gr];
Evaluate new solutions and update light intensity;
end
end
Rank the fireflies and find the current best;
end
Algorithm 1: Firefly Algorithm: Pseudo code.
where d is the dimension of x in space that is also dependent on
the context of the firefly, t is iteration variable. Intensity or the
13

brightness I is proportional to some objective function f (x) and
the location update equation is given by (1).
xi = xi+bexp[gr2
i j](xj xi)+ae (1)
where a is the step controlling parameter, e is the variable that
brings about randomness, g is the attraction coefficient, b is
the step size towards the better solution and xi is the location
information of the observing entity.
4. Radio Model
The proposed methodology uses a classical radio model [6] and
the sensor node is a transceiver. Hence, this radio model gives the
energy consumed for the transmission and reception. The block
diagram representation is shown in fig. 2. The radio model con-sists
of transmitter and receiver equivalent of the nodes separated
by the distance ‘d’ where Etx and Erx are the energy consumed
in the transmitter and the receiver electronics. Eamp is the energy
consumed in the transmitter amplifier in general, and it depends
on the type of propagation model chosen either free space or
multipath with the acceptable bit error rate. We consider Ef s for
free space propagation and Eamp for multipath propagation as the
energy consumed in the amplifier circuitry. The transmitter and
the receiver electronics depends on digital coding, modulation,
filtering and spreading of data. Additional to this there is an
aggregation energy consumption of Eagg per bit if the node is a
cluster head.
Figure 2. Radio Model.
4.1 Energy Consumption
This section describes the energy consumed for communication.
Packet transmission
Et = (LP Etx)+(LP Eamp dn) (2)
where LP is the packet length in bits and n is the path loss
component, which is 2 for free space and 4 for multipath propa-gation.
Suppose a node transmits a packet. Each bit in a packet
consumes Etx amount of transmitter electronics energy, Eamp
amount of amplifier energy. A packet of length LP consumes an
overall energy of Et .
Packet reception
Er = (LP Erx) (3)
where LP is the packet length in bits.
Suppose a node receives a packet. Each bit in a packet con-sumes
Erx amount of receiver electronics energy. A packet of
length Lp, consumes an overall energy of Et .
5. Proposed Methodology
This section deals with the modified firefly algorithm with the
assumptions made for building this novel protocol.
5.1 Assumptions
1. All the nodes can communicate with each other and with
the BS directly.
2. There is a single hop from ordinary node to CH and from
CH to BS.
3. All the nodes are static, where the algorithm run at a particu-lar
time instant and update for next round, and all the nodes
are location aware. They update their location information
to the BS before entering into the set-up phase.
4. 2-D space is considered for sensor node deployment.
5.2 Description of protocol
1. The BS broadcasts the percentage of CHs requirements for
the entire network. Let this be P. Also it broadcasts the
location information of all the nodes to the entire network.
2. After receiving this information, all the nodes will calculate
a random number and compare with T(n) given by the
formula (4).
T(n) =
(
P=(1P(rmod( 1
p ))); n 2 G
0; otherwise
(4)
If the random number is less than T(n) the node declares
itself as the CH. G is the number of ordinary nodes eligible
for becoming a CH in a particular round.
3. First, the declared CHs start broadcasting the packet of
interest. All the CHs learn about the ordinary nodes and
other CHs in the plot. Then they broadcast the packet of
interest by introducing the intensity value that it has calcu-lated
using (5), which serves to be an objective function for
all sensor nodes (fireflies in the proposed work).
I(x) = I0=(1+gx2
i ) (5)
The minimum the value calculated by (5), large is the dis-tance
between the CH and ordinary node. I0 is the initial
intensity value of all the nodes. Hence all the CHs store
the maximum of the intensity values calculated with all the
other ordinary nodes in the network belonging to a particu-lar
round. The value of xi is calculated by using (6) as per
the firefly algorithm [7].
xi = xi+bexp[gr2
i j](xj xi)+a(rand0:5) (6)
14

where xi is the location of the CH and xj is the location
of the ordinary node and only the x co-ordinate is consid-ered
for the intensity calculation as a reference. ri j is the
distance between the CH and an ordinary node, calculated
using Euclidean distance equation and e is (rand 0:5).
b, g and a are the parameters that are adjustable and rand
provides the randomness in the equation (6).
4. The ordinary nodes on receiving the packets from CHs
calculate their intensity values using equations (5), (6) and
(7), and store the maximum value of all the intensity values
calculated with respect to all the CHs in the network. The
ordinary nodes now compare their intensity values with
all the other CHs intensity values and attach to a CH that
is having more intensity value than their values, by send-ing
a join request packet. This process leads to a cluster
formation.
5. After the formation of the clusters, the network enters to the
steady state phase, where the nodes actually start transmit-ting
their sensed values to the based station. This happens
in rounds and usually a steady phase is accompanied by
multiple rounds.
6. After finishing the steady phase, the network enters into the
set-up again and the process repeats. It is to be noticed that
the intra cluster communication is accompanied by TDMA
and CH- BS communication is accompanied by CDMA.
6. Results and Discussions
Table 1. Radio characteristics and other parameters chosen for
simulation.
Parameter Value
Number of nodes 100
Transmitter electronics, Etx 50 nJ/bit
Receiver electronics, Erx 50 nJ/bit
Eamp 0:0013 pJ/bit
Ef s 10 pJ/bit
Eagg 5 nJ/bit
Length of plot 100m
Width of plot 100m
Lp(packet transmitted from CH to BS) 6400bits
Lctr(packet transmitted from ordinary node
200bits
to CH)
Initial energy of the node 0:5J
This section deals with the simulation results obtained for the
proposed method. The simulations were carried out in PC with
Intel I5 processor, and windows operating system. MATLAB
2009 is used as the simulating platform.
Uniform distribution was used to randomly distribute the
nodes in 100m x 100m plot. The BS was located at (50;175)
position. The deployment of sensor nodes is shown in the fig.
3. Table 1 shows various parameters set for the protocol. The
percentage of CHs requirement from the BS was set to 10% for
Figure 3. Network deployment.
Figure 4. Residual energy in the network for 1000 rounds.
all the rounds. The protocol was executed for one cycle of steady-state
phase in each round, with the assumption of all the nodes
having some data to transmit.
The parameters of the firefly algorithm were adjusted as fol-low:
a = 2, b = 2, g = 2, I0 = 5 and rand used was rand()
function of MATLAB which offers an uniform distribution.
The simulation results are shown in fig.4 and fig.5. Graph in
fig.4 shows that, as the simulations reaches approximately 1000th
round, the energy consumed by Basic-LEACH was observed to
be more than the novel Fire-LEACH.
Fig.5 shows that as the simulations reaches approximately
1000th round, the number of dead nodes in the network increases
in the Basic-LEACH compared to the Fire-LEACH.
It was observed from graphs of fig.6, fig.7 and fig.8 that
variation in the constants g, a and b, there were shifts in the
energy curves. Hence by prior adjustments of optimal values for
these constants results in better reduction in the overall network
15

Figure 5. . Number of dead Nodes in the Network for 1000
rounds.
Figure 6. Variations in the network residual energy for different
values of attraction factor g (1000 rounds).
energy consumption. A similar reason can be given for even the
node survival rate graphs shown in fig.9, fig.10 and fig.11.
For the simulations only the steady phase energy was consid-ered
neglecting the set-up phase energy. It has to be noted that
in all the clustering protocols, there is considerable amount of
energy consumption in the set-up phase.
7. Conclusions
The work proposed in this paper demonstrates the use of com-putational
intelligence in improving network performance by
reducing the overall network energy consumption and increasing
the node survival rate. The proposed methodology was applied to
values of a (1000 rounds).
values of b (1000 rounds).
the basic LEACH protocol and simulation results prove that the
algorithm enhances the energy efficiency thereby increasing the
node survival rate and provides a proof that the method can be
implemented in the future networks with ease.
Acknowledgements
Authors like to thank Dept. of Information Science Engg.,
M.S Ramaiah Institute of Technology for providing lab facilities
for conducting the research work. Authors also like to thank
Management and Dr. S.Y. Kulkarni, Principal of M.S Ramaiah
Institute of Technology, for their constant support to carry the
prospective research work.
16

Figure 9. Variations in the number of dead nodes for different
values of g (1000 rounds).
values of a (1000 rounds).
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