An Improved LEACH-C Algorithm for Energy Efficiency in WSN Routing

IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 07, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 607
An Improved LEACH-C Algorithm for Energy Efficiency in WSN
Routing
Richa Budhiraja1
Saranjeet Singh2
1
M. Tech Student 2
Assistant Professor
1,2
Department of Electronics Engineering
1,2
Galaxy Global Group of Institutions, Ambala
Abstract— this paper considered a multi-objective LEACH-
C algorithm in the selection of Cluster Head (CH) in such a
way so that its energy is used uniformly with load balancing
among clusters for delayed disintegration of network.
LEACH-C algorithm based single objective clustering
approach has been replaced by multi-objective clustering
approach where we not only considered the residual energy
of nodes but the size of cluster in creating a cluster structure.
The improved LEACH-C protocol has been compared with
random LEACH and Max Energy LEACH or existing
LEACH-C algorithm for energy equi-distribution and load
balancing among clusters. Wireless sensor network (WSN)
is simulated using a MATLAB programming and power
consumption algorithms take into consideration all aspects
of power consumption in the operation of the node. The
modified LEACH-C routing protocol shows improvements
in lifetime as well as in network disintegration criterion.
Key words: LEACH-C algorithm, Cluster Head, MATLAB.
I. INTRODUCTION
Since early 1990s, the research on wireless sensor networks
has intensified due to their inherent capability of distributed
computing and the types of applications they support such as
target tracking, remote environmental monitoring,
biomedical health monitoring and natural disaster relief.
Other applications which can be identified with WSN
include Environmental Monitoring, remote ecosystems
Monitoring, Collect weather forecast data, industrial
processes etc. In the recent past, wireless sensor networks
have found their way into a wide variety of applications and
systems with enormously varying multi-dimensional
requirements and characteristics making it increasingly
difficult to discuss typical requirements regarding hardware
issues and software support. Various design constraints
faced in the process of finalizing various type of architecture
of WSN are cost, energy, heterogeneity, deployment,
mobility, resources, security, modality, infrastructure,
topology, coverage, connectivity, size, lifetime and Quality
of Service.
In WSNs, all nodes is fed by a small battery
making energy saving as primary goal in designing WSN
structure with an objective of maximizing network lifetime
as it is impractical to change or replace exhausted batteries.
Two competing objectives in the design of WSNs are the
capability to exchange large amount of data between nodes
and base station and minimizing the energy consumption.
We require efficient routing protocols in WSNs to manage
these objectives. Therefore, many routing algorithms have
been proposed due to the challenges in designing an energy
efficient network. Out of these hierarchical routing protocols
greatly satisfy the limitations and constraints in WSNs.
Hierarchical routing protocols consist of two layer
architecture where one layer is responsible for cluster head
selection and the other works for routing. A cluster head
(CH) is a node which is responsible for collecting data from
other nodes in the cluster, aggregating or preprocess data
and then sending the processed data to the base station.
This paper is organized into six chapters. In Section
1, Introduction to WSN is included. In this, we have
discussed about the various characteristics of WSN and
some of challenging issues in WSN. It also gives an
overview of various routing protocols and brief discussion
of LEACH protocol. Section 2 Reviews starts by relating
the work to other available surveys in the literature.
Section3 describes methodology, simulation environment
and parameters used in analysis. Section4 deals with result
and discussion. Section5 concludes the simulation results.
II. ROUTING PROTOCOLS IN WSN
Two main classes of routing protocols in Wireless Sensor
Networks can be named as flat and hierarchical. In flat
routing, all nodes are assigned equal roles and similar
functionality whereas in hierarchical routing, they are
assigned different roles. In hierarchical protocols clusters
are formed.
In flat routing protocols nodes play the same role
and have similar functionality in transmitting and receiving
data. In this type of network it is not possible to assign a
global identifier to each node due to large number of nodes.
Therefore, base station send queries to different part of the
field and waits for the data from sensors in selected parts of
the field. This approach is called data centric routing. SPIN
(Sensor Protocols for Information via Negotiation) [1] and
DD (Direct Diffusion) [2] are two examples of the data
centric routing protocols that save energy by data
negotiation and omitting the redundant data.
In Hierarchical (Cluster-based) Routing method,
nodes play different roles in transmitting and receiving data.
Some of the nodes are responsible for processing and
communication, while other nodes can be used for sensing
the target area. Hierarchical routing is mainly considered as
two layer architecture where one layer is engaged in cluster
head selection and the other layer is responsible for routing.
Cluster head in hierarchical routing is the node which is
responsible for collecting data from other nodes in the
cluster, aggregating all data and sending the aggregated data
to the base station. Creating clusters and assigning
communication task to cluster heads contributes to a more
scalable and energy efficient network. The main goal of all
the hierarchical routing protocols is to appropriately create
clusters and choose cluster heads in order to reserve energy
in the network.
Recently there has been increased interest in
studying energy-efficient clustering algorithms in the
context of both ad hoc and sensor networks. The main aim
of clustering protocols in ad hoc networks is to generate the
minimum number of clusters while maintaining network
connectivity. In these algorithms the election of CHs is

An Improved LEACH-C Algorithm for Energy Efficiency in WSN Routing
(IJSRD/Vol. 2/Issue 07/2014/138)
based mainly on the identity of nodes, the degree of
connectivity or the connected dominating set. These
techniques are discussed in depth in. In the case of WSNs,
the main objective of clustering protocols is to minimize
energy consumption by the network in order to extend the
network lifetime. The surveys dealing with WSN clustering
protocols can be found in [7]. The WSN clustering protocols
can be classified into two categories: probabilistic and
deterministic. In probabilistic clustering protocols a node
becomes a CH with a certain probability, which requires an
exchange of overhead messages for the CH’s election. The
EEHC [9], EECS [10], and HEED [11] fall in the
probabilistic class and PEGASIS [12], and TASC [13] are
categorized in the deterministic class.
III. PROPOSED METHODOLOGY
Low Energy Adaptive Clustering Hierarchy (LEACH) is a
clustering-based protocol that utilizes randomized rotation
of local cluster base station (CH) to evenly distribute the
energy load among the sensors in the network [8]. LEACH
assumes every node can directly reach a base station by
transmitting with sufficiently high power.
LEACH protocol runs with many rounds; Cluster
setup phase and Steady phase. In setup phase, each node
decides whether or not to become a cluster head for current
round. The selection depends on decision made by the node
itself by choosing a random number between 0 and 1.The
threshold is set as:
 If
 otherwise
Where, p is the probability of the node being
selected as a cluster-head node, r is the number of rounds
passed, G is the set of nodes that have not been cluster-
heads in the last 1/p rounds mod denotes modulo operator.
In LEACH the following steps are taken to transfer
data.
 First cluster heads (CHs) are selected
randomly. This is called cluster head selection
stage.
 Then each node adds itself to a particular CH
based on its distance with CH. This is called
cluster formation stage.
 Once cluster are formed then data is sent from
non-cluster head node to CH and then CH to
base station BS. This can be simulated by
Energy consumption stage in simulator.
The non-cluster nodes send the message containing
their IDs by using CSMA (carrier sensing multiple access)
to join a cluster with strongest signal strength. After that,
each CH knows its own member nodes information
including the numbers and IDs. Based on the message, the
CH creates TDMA schedule table and broadcasts it to the
cluster members. So all the member-nodes know their idle
slots, and then the steady-state phase starts.
During the Steady-state phase, each node can turn
off its radio until it senses the necessary data. The member
nodes can send their data to CH during their allocated
schedule table created during the set-up phase. As for the
CHs, they have to keep up their communication status at all
times so as to receive the data from their member nodes.
When the CH receives all the data sent by their members, it
will aggregate them at first and then send the aggregating
data packets to BS in order to save energy.
The problems of LEACH Algorithm are manifolds.
Neither it is fulfilling equi-space distribution nor is it
fulfilling equi-energy distribution requirement for better
energy efficiency. This algorithm was extended as LEACH-
C by Heinzelman et al. in 2002 in which Base station takes
the role of deciding about cluster heads (CHs) and cluster
formation. As from the starting node locations are fixed and
can be fed to BS for calculation a lot of energy can be saved
by WSN in such computation by nodes. Once CHs are
calculated and clusters are formed this information in
advertised to all nodes in WSN to prepare them for real data
communication in steady state.
LEACH protocol is achieved base on many
assumptions, such as assuming that all nodes in the network
have the same structure and start with the same energy, and
nodes can be aware of their residual energy, and so on. The
LEACH improves a lot as compared to direct transmission
of data but there remain several problems in this algorithm.
Because the election strategy of cluster head is random, it
may lead to misdistribution of cluster head and imbalanced
clusters. Thus cluster head load is not balanced consequently
this may lead to high energy consumption and early death of
individual cluster head.
Clustering is the main factor responsible for the
energy conservation in LEACH algorithm. Main objectives
of clustering are equal distribution of energy and equal
distribution of nodes in space so that less energy is
consumed and early deaths of nodes can be delayed. In
LEACH both of these objectives can’t be achieved. Further
to achieve these objectives a Max-Energy LEACH was
proposed, in which CHs are chosen based on residual energy
instead of random selection. Max-Energy LEACH steps are
shown below:
 Cluster head selection stage: CHs are chosen based
on residual energy. Highest energy nodes are selected
to work as CH.
 Cluster formation stage: similar to LEACH.
 Energy consumption stage in simulator.
Max-Energy LEACH is able to achieve energy
equi-distribution but not space equi-distribution because CH
can be selected from one region only leading to large energy
consumption by nodes to send data to CHs. The clustering
algorithm while doing its work should pay attention toward
the number of nodes a cluster is having. If we can equi
distribute all nodes to cluster then we assume that it may
lead to better energy efficiency. This is our hypothesis.
In [5] and [6], the cluster head is elected by
obtaining the energy consumption in Intra-cluster and Inter-
cluster, and then we could find the average energy of overall
network. Finally, we proposed the re-electing cluster heads
method for balancing local clusters. This method uses the
information which the cluster heads have. This information
is the number of member nodes and distance between the
member nodes and the cluster head. This is called balanced
energy based clustering. We propose to balance clustering in
terms of number of nodes in clusters.
We hereby propose to make efficient clustering of
WSN nodes by using LEACH-C technique in such a way so
that transmission energy of nodes used to send data to

cluster head (CH) is minimized. Our algorithm will consist
of the following steps:
 Step 1: Find the nodes that are alive. Nodes that
are not of type dead nodes are alive.
 Step 2: In the next step, from each cluster CH is
chosen on the basis of surplus energy. So our
proposed scheme involves both things spatial
distribution as well as energy distribution in the
network architecture which may ultimately
improve the network life and its quality.
A. Experimental setup
To simulate LEACH, we have used random 100-node
networks for our simulations with similar parameters used in
[4]. We placed the BS at a far distance from all other nodes.
For a 50m x 50m plot, our BS is located at (25, 150) so that
the BS is at least 100m from the closest sensor node.
We use the same energy model as discussed in [3]
which is the first order radio model. In this model, a radio
dissipates Eelec = 50 nJ/bit to run the transmitter or receiver
circuitry and Eamp = 100 pJ/bit/m2
for the transmitter
amplifier. The radios have power control and can expend the
minimum required energy to reach the intended recipients.
The radios can be turned off to avoid receiving unintended
transmissions. The equations used to calculate transmission
costs and receiving costs for a k-bit message and a distance
d are shown below:
1) Transmitting
ETx (k, d) = Eelec*k + Eamp * k* d2
…..(1)
2) Receiving
ERx(k) = Eelec*k………………(2)
Receiving is also a high cost operation, therefore,
the number of receives and transmissions should be
minimal. LEACH also uses the same constants (Eelec, Eamp ,
and k) for calculating energy costs, therefore the LEACH
achieves its energy savings by minimizing d and the number
of transmissions and receives for each node.
In our simulations, we used a control packet length
k of 200 bits to send information from non-CH node to CH
node. Size of packet length K of 6400 bits is fixed to send
information from CH node to BS With these radio
parameters, when d2
is 500, the energy spent in the amplifier
part equals the energy spent in the electronics part, and
therefore, the cost to transmit a packet will be twice the cost
to receive. It is assumed that the radio channel is symmetric
so that the energy required to transmit a message from node
i to node j is the same as energy required to transmit a
message from node j to node i for a given signal to noise
ratio (SNR).
B. Proposed Modified LEACH-C Algorithm
The above LEACH-C algorithm considers only residual
energy as objective function to decide the selection of
cluster head. This serves the concept of equi-energy
distribution but doesn't address the concept of load
balancing among clusters. For load balancing a multi-
objectives criterion is required. The following steps show
this concept:
 Step 1: Find the nodes that are alive. Nodes that are
not of type dead nodes are alive.
 Step 2: In alive nodes find p*livenodes number of
node which have maximum energy in nodes
architecture is selected as a CH. Probability p of
selecting a node as CH is pre-decided.
 Step3: Attach nodes to nearest CH to form cluster
by finding distance matrix to form cluster. In every
cluster only a fix number of nodes can have
membership. This fix number is decided by
dividing live nodes by total number of clusters.
So our proposed scheme involves two things; equi-
energy distribution as well as load balancing in the network
architecture which may ultimately improve the network life
and its quality.
IV. RESULTS ANALYSIS
Following table shows the results obtained from the
experimentations done as per the setup explained in the
previous. Four algorithms have been implemented in this
thesis. In first algorithm i.e. Random LEACH algorithm is
implemented where CHs are selected randomly based on a
probability function. We have taken this probability as 10%.
It is further improved by using a fair distribution of energy
by selecting maximum energy nodes to be CHs. This
method is called LEACH-C. In this method a fix number of
CHs are selected based on the number of nodes that are
living. After selection of CHs, each non-CH nodes attach
itself to a particular cluster thus making a cluster structure.
Another modification is made in the improved algorithm
where CHs are selected based on the similar residual energy
consideration but nodes are clustered based on distance and
as well as balancing. Once a fixed numbers of nodes are part
of a cluster further membership is restricted to that particular
cluster. We measure algorithms’ efficiency by assessing
total no. of rounds up to which network survives. A network
is assumed to be live if more than 25% nodes are alive with
total energy greater than zero.
Wsn
Routing
Algorithm
Network
Life (In
Rounds)
Rounds
In
Which
First
Node
Dead
Rounds
In
Which
50%
Node
Dead
No Of
Packets
Sent In
Total
Rounds
Remaining
Energy
After 75%
Node Is
Dead
(Joules)
Random
LEACH
701 108 350 11865 3.47
LEACH-
C
1286 1243 1279 12759 1.31
Modified
LEACH-
C
1350 1308 1345 13389 1.29
Table 5.1: Experimentation Results
In the table 5.1, it is clearly shown that balanced LEACH-C
perform far better as compared to random LEACH and
marginally better as compared to LEACH-C for each and
every criterion. The balanced LEACH-C algorithm performs
nearly two times better than random LEACH and as
compared to max energy LEACH or LEACH-C
performance is nearly 5%.
If we consider a network, dead if 50% nodes are
dead then LEACH and LEACH-C are performing better
than random LEACH. Random LEACH has performed
worst in every situation. If we consider 75% node criterion
for network life then again the BALANCED LEACH-C
performs better than other two algorithms. If we compare
the number of dead nodes as per our simulation results
LEACH-C and the balanced LEACH-C perform better, but

there nodes once start dying accelerates network decay very
fast
If we consider no of packets sent to BS then the
balanced LEACH-C is clear winner. Later has sent highest
number of packets to BS but if we consider the ratio
between packet sent and no. of rounds performed by the
algorithm then Max Energy LEACH is also comparable.
This can be confirmed by the figures 5.4 which show the
comparative analysis of three simulated algorithms for three
different criteria.
Fig. 5.4: Comparison of three algorithms for no of packets
sent to BS, residual energy, dead nodes Vs. No. of Rounds
V. CONCLUSION
The performance of three algorithms is measured in these
experiments. Parameters for performance measurements are
Residual Energy, Dead Nodes, Packets sent to BS. These
parameters are shown in above figures and are plotted
against number of rounds. If we consider residual energy
and total number of rounds then LEACH-C and the balanced
LEACH-C perform better than random LEACH. The
residual energy at the end of total number of round shows
that LEACH-C and the balanced LEACH-C most uniformly
distributed energy dissipation among nodes. For network
integration or dead nodes criterion both algorithms perform
far better than other algorithms. For number of packets sent
to BS criterion the balanced LEACH-C performs better than
other algorithms.
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