Optimizing the Performance of I-mod Leach-PD Protocol in Wireless Sensor Networks

IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613
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Optimizing the Performance of I-mod Leach-PD Protocol in Wireless
Sensor Networks
Sheetal Dixit1
Dr. Naraendra Yadav2
Dr. Saroj Hiranwal3
1
M.Tech. Scholar
1,2
Department of Computer Engineering 3
Department of Information Technology
1,2,3
Shri Balaji Technical Campus
Abstract— Wireless Sensor Networks (WSNs) is a networks
of thousands of inexpensive miniature devices capable of
computation, communication and sensing. WSN is being
been attracting increasing interest for supporting a new
generation of ubiquitous computing systems with great
potential for many applications such as surveillance,
environmental monitoring, health care monitoring or home
automation. In the near future, wireless sensor network is
expected to consists of thousand of inexpensive nodes, each
having sensing capability with limited computational and
communication power which enables to deploy large scale
sensor networks. Large scale WSN is usually implemented
as a cluster network. Clustering sensors into groups, so that
sensors communicate information only to cluster-heads and
then the cluster-heads communicate the aggregated
information to the base station, saves energy and thus
prolongs network lifetime. LEACH (Low Energy Adaptive
Clustering Hierarchy) protocol is one of the clustering
routing protocols in wireless sensor networks. The
advantage of LEACH is that each node has the equal
probability to be a cluster head, which makes the energy
dissipation of each node be relatively balanced. In LEACH
protocol, time is divided into many rounds, in each round,
all the nodes contend to be cluster head according to a
predefined criterion. This paper focuses on how to set the
time length of each round, how to adjust threshold based on
the residual energy, and the measurement of energy required
for transmission, based on the distance of cluster head from
the base station, to prolong the lifetime of the network and
increase throughput, which is denoted as the amount of data
packs sent to the sink node. The functions of residual energy
and required energy, and the time length of each round are
deduced, thereby modifying the threshold value calculation.
These functions can be used to enhance the performance of
cluster-based wireless sensor networks in terms of lifetime
and throughput.
Key words: Wireless Sensor Networks, Network Lifetime,
Energy Consumption, LEACH protocol, duty cycle
I. INTRODUCTION
Clustering sensors into groups, so that sensors communicate
information only to cluster-heads and then the cluster-heads
communicate the aggregated information to the base station,
saves energy and thus prolongs network lifetime. Low
Energy Adaptive Clustering Hierarchy (LEACH) [1]
protocol is one of the clustering routing protocols in wireless
sensor networks. The advantage of LEACH is that each
node has the equal probability to be a cluster head, which
makes the energy dissipation of each node be relatively
balanced. In LEACH protocol, time is divided into many
rounds, in each round, all the nodes contend to be cluster
head according to a predefined criterion. This paper focuses
on how to set the threshold limit of sensor node, to bias the
probability of being selected as cluster head, to prolong the
lifetime of the network and increase throughput, which is
denoted as the amount of data packs sent to the sink node.
The functions of lifetime and throughput related to the time
length of each round are deduced. These functions can be
used to enhance the performance of cluster-based wireless
sensor networks in terms of lifetime and throughput.
A. Problem Statement
Leach Protocol is a dynamic clustering algorithm [2] for
clustering networks. The primary motivation behind
LEACH protocol is to distribute the power consumption in
transmission evenly in all the nodes of the network.
However, LEACH employs a purely random approach
taking into consideration, only the case that no node can be
elected as cluster head, which is elected as a cluster head in
previous round. This work proposes an augmentation of the
LEACH protocol with the residual energy of the node to
bias the original cluster head selection and to distribute the
energy consumption more evenly.
II. MOTIVATION
With the recent technological advances in wireless
communications, integrated digital circuits, and micro
electro mechanical systems (MEMS) [3], development of
wireless sensor networks has been enabled and become
dramatically feasible. Sensor nodes are often left unattended
e.g., in hostile environments, which makes it difficult or
impossible to re-charge or replace their batteries. This
necessitates devising novel energy-efficient solutions [4] to
some of the conventional wireless networking problems,
such as medium access control, routing, self-organization,
so as to prolong the network lifetime. Various routing,
power management, and data dissemination protocols [5]
have been designed for wireless sensor networks (WSNs)
dependent on both the network architecture and the
applications that it is designed for. In most of the
applications sensors are required to detect events and then
communicate the collected information to a distant base
station (BS) where parameters characterizing these events
are estimated. The cost of transmitting information is higher
than computation and hence it is be advantageous to
organize the sensors into clusters, where the data gathered
by the sensors is communicated to the BS through a
hierarchy of cluster-heads [6]. In wireless sensor networks
(WSNs), due to the limitation of nodes’ energy, energy
efficiency is an important factor should be considered when
the protocols are designing. As a typical representative of
hierarchical routing protocols, LEACH Protocol plays an
important role. LEACH Protocol is the first protocol of
hierarchical routings which proposed data fusion [7], it is of
milestone significance in clustering routing protocols. Many

Optimizing the Performance of I-mod Leach-PD Protocol in Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/026)
hierarchical routing protocols are improved ones based on
LEACH protocol [8]. So, when wireless sensor networks
gradually go into our lives, it is of great significance to
research on LEACH protocol.
III. RESEARCH APPROACH
The modeling of packet transmission overheads for a
surveillance or a general purpose wireless sensor network
for energy consumption can be described more accurately
using a probabilistic model rather than using analytical
equations. In this paper, classical LEACH protocol is
realized using Poisson Probability Distribution [9], for
packet transmission probabilities and mean number of
packets transferred by any node. Thus, this analysis gives a
more accurate description of energy consumption as
compared to modeling techniques used earlier. Also, the
residual energy in a node can be computed using simple
logic that can be inculcated using existing hardware with no
extra cost. This parameter is critical for network lifetime and
has to be considered in cluster head election. Thus, the node
having very low energy remaining should possess very low
probability of being elected as Cluster Head (CH). Classical
LEACH , being based on purely random numbers, does not
have any provision for this consideration. In this paper, the
classical algorithm has been modified to take into account
the variation in the residual battery life and controlled using
a strength factor. This paper focuses on how to set the time
length of each round, how to adjust threshold based on the
residual energy, and the measurement of energy required for
transmission, based on the distance of cluster head from the
base station, to prolong the lifetime of the network and
increase throughput, which is denoted as the amount of data
packs sent to the sink node. The functions of residual energy
and required energy, and the time length of each round are
deduced, thereby modifying the threshold value calculation.
These functions can be used to enhance the performance of
cluster-based wireless sensor networks in terms of lifetime
and throughput.
IV. PROPOSED WORK
A. LEACH Protocol
LEACH is an adaptive clustering routing protocol proposed
by Wendi B. Heinzelman, et al. The implementation process
of LEACH includes many rounds. Each round consists of
the setup phase and the steady data transmission phase. In
the set-up phase, the cluster head nodes are randomly
selected from all the sensor nodes and several clusters are
constructed dynamically. In the steady data transmission
phase, member nodes in every cluster send data to their own
cluster head, the cluster head compresses the data that
received from member nodes and sends the compressed data
to the sink node. LEACH protocol periodically elects the
cluster head nodes and re-establishes the clusters according
to a round time, which ensures energy dissipation of each
node in the network is relatively evenly.
The cluster head election algorithm in LEACH is a
very simple procedure. All the sensor nodes generate a
random number between 0 and 1. Each node is assigned a
real number as a threshold value, T(n). If the generated
number is less than the threshold T(n), the sensor nodes will
broadcast an announcement message to notify others that it
is a cluster head. In each round, if a node has been elected as
a cluster head, its T(n) is set to zero, so that the node will
not be elected as a cluster head again. T(n) can be expressed
as:
( ) { ( )
where P is the percentage of the number of clusters
in the network (usually P is 0.05) [] , r is the number of the
election rounds, r mod(1/ P) is the number of nodes which
have been elected as cluster heads in the round r, and G is
the set of nodes that have not been elected as cluster heads
in round r.
The above formulation for LEACH can be
understood in a simple way. The complete interval is
supposed to be of length 1 unit. The number of subintervals
in the interval is 1/P = 20 (for P =0.05). R refers to the
current subinterval and G is the number of nodes that have
not yet been elected as cluster heads in the current interval.
After cluster head election, the cluster head
broadcasts its identity message to non-cluster head nodes.
The non-cluster head nodes send a join-REQ message to the
nearest cluster head to join in the corresponding cluster.
After the cluster head receives all the join-REQ information,
it will produce a TDMA schedule, and notify all the member
nodes in the cluster. After a member node receives the
schedule, it sends data in its own time slots, and remains in
the sleep state in other slots. After a frame time of data
transmission, the cluster head runs the data compression
algorithm to process the data and sends the results directly
to the sink node. LEACH protocol lets the data transmission
phase last for a fixed period of time[5], then enter into a new
round of cluster head election. The time length of round has
obviously influence on the performance of LEACH
protocol. In order to decrease the overhead of set-up phase,
it is desirable to increase the time length of round, which
increases the time for data transmission. However,
prolonging the time length of round also increases the
energy consumption of cluster head, which will causes some
nodes die early and in turn shortens the lifetime of wireless
sensor networks. So, regarding to configuration of time
length of round, there is a trade -off between lifetime and
throughput.
Suppose that the time of set-up phase is α , and
the steady data transmission time is t , then the time length
of every round is tr = α + t . It is assumed that the time
when the first sensor node dies as the lifetime of the
network, which is denoted as tfd . It is noted that the
following analysis can be easily extended to other definition
of lifetime.
The relationship of the lifetime tfd and t is shown in
Fig. 3.1
It is clear from the figure that
tfd = n*( α + t) = n*tr
where n is the number of rounds after which the
first sensor nodes dies.

Fig. 3.1: Setup and Transmission Phase of LEACH
According to LEACH protocol, there are m frames
in the time t, so t = m*TFRAME . Here TFRAME is the time
length of each frame. Therefore
tfd = n*tr = n*( α + m*TFRAME)
Given the initial energy E0 of sensor node, the tfd
can be deduced according to the energy dissipation in cluster
set-up phase and data transmission phase.
The energy dissipation model of cluster head and
member node can be derived using average number of
clusters in the network. Let there be n nodes in the cluster
network and the number of clusters be k. Then the average
number of sensor nodes per cluster is n/k. Thus, in any
cluster, there is one head node and (n/k) - 1 member nodes.
It is assume N nodes are evenly distributed in M×M
area, the coordinate of the sink node is (Xsink, Ysink), the
initial energy of each node is E0, the length of data message
is L bits, and the length of control message is P bits.
A simple radio hardware energy dissipation model
can be used to find out the dissipation of energy in the setup
phase, by the cluster head and the member nodes. The
transmitter dissipates energy to run the radio electronics and
the power amplifier. The receiver dissipates energy to run
the radio electronics. The radio energy dissipation model is
shown in Fig.2. Then the energy expenditure for
transmitting L-bit message to d distance is:
( )
And the energy expenditure for receiving L-bit message is
In Eq. (3) and Eq. (4), the electronics energy
expenditure for one bit, Eelec , depends on factors such as the
digital coding, modulation, filtering and spreading of the
signal. whereas the amplifier energy expenditure for one bit,
ε *d γ
, depends on the distance from the sender to the
receiver and the acceptable bit error rate. In this paper, the
channel model in the cluster is free space propagation
model, and the channels between cluster head nodes and the
sink node are multi-path fading channel. For the free space
propagation, γ = 2, and ε is denoted as εfs . For multi-
path fading channel, γ = 4 , and ε is denoted as εmp.
Considering the first order radio energy dissipation model,
one can write the following equations for energy
consumption in transmitter and receiver circuitary.
( )
( )
Fig. 3.2: Radio energy dissipation model
B. Modified LEACH Algorithm for Cluster Head Election
The LEACH protocol assumes a region of spatially
distributed cluster nodes and a base station. For the sake of
simplicity, an M X M square region is considered having n
sensor nodes, evenly distributed in the entire region. It is
assumed that the coordinates of every nodes are known as
priori. Let the coordinates of the base station B being
(XBASE, YBASE). Let the total energy consumption in the
setup phase be ECH by the cluster heads and ENCH by non-
cluster heads. The core technique of LEACH protocol for
cluster head election is based on pure randomness,
excluding those nodes that were elected as cluster heads in
the previous cycle. However, a more uniform energy
consumption model can be obtained if the cluster head
election will also consider the following:
(1) The residual battery power of the sensor node.
(2) The spatial location of the elected sensor node with
respect to the sink or base station.
(3) The count of the number of cluster heads to be
elected (P) should be a function of the
Frequency/Traffic of sensed data.
(4) An optimal cycle time for election must be chosen
to reduce the overhead of setup phase.
Points 1 and 2 can be tackled simultaneously by
considering an n X 2 matrix for the entire network. Both
these issues are dependent on the energy dissipation model
of the sensor node in transmitting over a distance and the
residual battery power. Points 3 and 4 depends together on
the traffic of sensed data. More traffic needs more
processing for message aggregation and frequent
transmissions, and consequently, more energy consumption
by the cluster head. This energy consumption also depends
upon the spatial location of the cluster head with respect to
the base station.
The n X 2 matrix for each of the sensor node can be
constructed as shown:
[ ]
Here, ERi refers to the residual energy left in the
node i. Also, di refers to the distance of node i from the base
station B (XBASE, YBASE), which can be computed as:
√( ) ( )
From the matrix shown above, the power needed in
the transmission operation can be obtained as that is a
function of di.
For isotropic omni-directional antenna, the power
needed for transmission is proportional to the square of the
L bit
Packet Transmit
Electroni
cs
Tx
Amplifi
er
Tx
Amplifi
er
L bit
Packet
Eelec*L
L* Eelec d2
Eelec*L
d

distance from the antenna. Thus, four times more power is
needed for transmission if the distance between the two
nodes is doubled.
Let P be the fraction of nodes which are to be
elected as the cluster heads. Thus, P must be a function of
traffic intensity, which in turn depends on the transmission
time interval (after the setup interval).
The threshold value for every node contending to
be cluster head must be suitably chosen as a function of the
following parameters:
(1) The residual battery power
(2) The power needed for transmission
(3) The transmission time t
The probability of a node being chosen as the
cluster head must inversely be proportional to the difference
between the residual battery life and the power consumption
in transmission during the transmission period. This latter
term depends upon the probability distribution of the mean
number of packets transmitted during the transmission
period, the length of the active period and the distance
between the cluster head and the sink node.
1) Computation of the optimal transmission time
In this work, slotted CSMA as the Medium Access Protocol
for data link layer level transmission is considered. Let t be
the steady state data transmission time. For improving
network lifetime, it is required to construct a scheme such
that the dissipation of energy of the nodes in the network
occurs uniformly.
Let n be the mean number of packets transmitted
by each node in entire interval. Assuming a Poisson
Probability Distribution for the number of packets
transmitted with mean value λ, the probability of k packet
transmission during the interval is:
( )
Also, the mean value of packets transmitted by
each node in the subinterval is λ/(1/P) = λP.
The probability of packet transmission during the
subinterval can be obtained by the formula
( )
( ) ( )
giving;
( )
( ) ( )
which is the probability of packet transmission by a
node in a subinterval. Let the total number of nodes in the
network be N, using established results, the optimal value of
the cluster heads is the percentage P = 0.05. Thus, the
number of cluster nodes being elected is
The number of non cluster heads being N -N*P =
N*(1-P). These are the nodes which probably sends the data
in the transmission phase of the subinterval. Assuming a
uniform distribution of nodes under the territory of cluster
heads, the approximate number of transmitting nodes under
each cluster head is
( ) ( )
Thus, for a total of S transmitting nodes under each cluster
head with the probability of transmission being
( )
( ) ( )
The average number of packet transmissions is
( )
( ) ( ) ( )
The optimal transmission time must be equivalent
to this value for optimal power consumption. However,
using this result, some nodes may having data to sent may
not get a time slot in the TDMA schedule and may wait for
the subsequent subinterval, neverthless, it yields optimal
network utilization at the cost of latency.
2) Computation of the optimal threshold value based on the
optimal transmission time
As stated in the previous section, the optimal transmission
time is given by
( )
( ) ( ) ( )
Let ERI be the residual battery life of node id I. Let
its distance from the base station be dI . Assuming data
aggregation without any compression, the energy required
for transmission of number of packets over dI is
( )
( )
The above equation reflects the expenditure of
energy from the sensor node. In any case, if the residual
energy is less than this, the node cannot be chosen as the
cluster head. However, if the residual energy is greater than
this, it is the difference between the two that matters.
Moreover, the higher the threshold value for a
node, the greater the chances of it being the cluster head.
Therefore, the threshold value must directly be proportional
to
( )
However, a mathematical analysis of the standard
LEACH protocol shows that the threshold values increases
after each round of election.
Let denotes the quantity of energy when the
node is have full energy level. The quantity is a
quantity below 1 which is a measure of likelihood of node
being cluster head. The inculcation of this parameter in the
standard LEACH can be done in the following way
( )
{ ( )
( )
The parameter α is chosen suitably to manage the
process of election for cluster heads to evenly distribute the
energy consumption over the entire sensor network.
Chapter 4 discusses the analytical plots for
equations described above. It also discusses the results of
the simulation model against energy consumption and
network lifetime as facilitated by standard LEACH.

V. ANALYSIS OF PROPOSED WORK
A. Calculation of the optimal transmission time
For optimal transmission time, the expression derived is
stated in equation 3.11 repeated here for ready reference
( )
( ) ( ) ( )
The setup time can be assumed to be a constant as
it virtually takes the same time to broadcast a joining
message and to get the acknowledgements.
Consider the following values of parameters used
in the expression:
Parameter
(Symbol)
Specification Value
P
Fraction of Nodes to be electred as
cluster heads (CH) or Ratio of CH
from among the total population of
sensor nodes
[0.05,
1]
λ
Mean number of Packets
transmitted by nodes in a time
interval
[1,50]
Table 4.1: Parameter Specification for Wsn Model
Figure shows the plot of the optimal transmission
time against mean packet transmission rate and Cluster
Head percentage.
Fig. 4.1: Horizontal Plane axis show mean packet arrival
rate and proportion of cluster head at any time from total
population of clusters and the vertical axis shows optimal
transmission time for generation of TDMA schedule.
Fig. 4.2: Horizontal Plane axis show transmission range and
packet length and the vertical axis shows energy
consumption.
Fig. 4.3: Illustration of Threshold values of nodes (values
that determine the probability of cluster heads)
.
Fig. 4.4: Illustration of 5 rounds and the Cluster Heads
For a total of N = 100 nodes with the proportion of
cluster heads being P = 0.05, figure 4.5, 4.6 and 4.7 gives
the simulation results of the LEACH protocol
implementation in MATALB.
Fig. 4.5: Plot of Number of Packets sent to the base station
versus number of rounds. Poisson Probability Distribution is
assumed with mean value λ = 10.

Fig. 4.6: Plot of Number of Dead Nodes versus number of
rounds. First order Radio Energy dissipation model is
assumed as specified in [22].
Fig. 4.7: Plot of the total energy remained in the network
versus number of rounds.
Consider the proposed solution of the identified
problem to ensure longest possible network lifetime where
the lifetime of the network is defined as the shortest time
when the any node belongs to the network runs out of
battery life. Equation 3.14 is repeated here for ready
reference:
( )
{ ( )
( )
As illustrated in figure 4.6, the first dead node
occurs at round r = 260-265, where N = 100 is the number
of nodes in the network and P = 0.05 is the percentage of
nodes to be choosen as Cluster Heads (CH) in each round.
The fluctuation in the value occurs due to consideration of
probabilistic value of the mean number of packets
transmitted in accordance with Poisson Probability
Distribution.
The basic LEACH protocol is purely random, and
all those nodes that have not yet been elected as cluster
heads have equal probability of becoming cluster heads.
However, even the nodes that have not yet been elected as
cluster heads may have varying amount of remaining battery
life due to the following reasons:
(1) Placement of sensor node might be at a place which
might attract other sensor nodes to use it at a router
and therefore results in high energy dissipation.
(2) Node Malfunctioning, thereby resulting in high
energy dissipation.
(3) The node might have been chosen as CH in some
previous round at which time it had served a large
number of nodes in its cluster, therby had already
dissipated significant amount of energy in data
aggregation and transmission.
(4) Leakage in Battery or battery malfunction.
The formulation proposed in this work is identical
to the pure LEACH for its 1 complete round, consisting of a
total of 1/P = 20 sub-rounds of Cluster Head election. This
is because every node which is elected as cluster head
during any one sub-round of a round has its threshold value
set to zero and so cannot be elected as CH again in the same
round. However, after the completion of the round, each
node is left with varying amount of battery life probably due
to its operation as cluster head which needs more energy
consumption, as compared to non cluster nodes.
With the inclusion of the energy parameter in the
Pure Leach formulation for setting the threshold values in
the CH election, the threshold values of the nodes with low
power becomes smaller, thereby reducing the probability of
them to be elected as cluster heads, thereby increasing the
battery life.
The simulation with proposed modification is described as
follows:
Fig. 4.8: Plot of Number of Dead Nodes versus number of
rounds as per the proposed modification. First order Radio
Energy dissipation model is assumed as specified in Ch 3

The given simulation shows improvement over
traditional Leach as the round at which the first node dies is
260-265 in pure LEACH whereas it is 273-280 in case of
implementation with proposed modification with α = 0.1.
Thus, the relative improvement in performance is 15/260 =
5.7% over [22]. Chapter 5 discusses the result of the paper
and outline the conclusion and future scope.
VI. CONCLUSION AND FUTURE SCOPE
The LEACH is a well-known routing protocol for cluster
based wireless sensor networks. This paper analyses the
performance of LEACH-based wireless sensor networks in
terms of lifetime and throughput. Electing cluster head
randomly in LEACH protocol causes that the current energy
of some cluster heads are less or their distances to base
station are far, because of the heavy energy burden, these
cluster heads will soon die. For this issue, this paper
proposed a new improved algorithm of LEACH protocol
which is aim at balancing energy consumption of the whole
network and extending the network lifetime by balancing
the energy consumption of these cluster heads. The
reasonable number of frames in a LEACH round is deduced
to prolong the lifetime and increase the throughput. In work
proposes a modification of the LEACH is described based
on stochastic cluster-head selection algorithm by
considering two additional parameters, viz, the residual
energy of a node relative to the residual energy of the
network and the energy needed to work as cluster head in
the sub-round based on the geographical placement of the
node. The new improved algorithm is simulated by Matlab
platform, the simulation results indicate that the energy
efficiency and the lifetime of network are both better than
that of LEACH Protocol.
The analytical results obtained are in agreement
with the simulation results thereby proving the validity of
the equations. However, the proposed algorithm increases
the lifetime of the network but it does so at the cost of
complexity of the logic circuitry which had to include the
logic for computation of remaining battery life and an
additional logic to compute the projected estimate of energy
consumption if it is being elected as cluster head. This also
results in additional computation as overhead. Also it results
in increased cost because of this additional logic realization
in hardware. It also includes, in some way, the Latency in
the network for the obvious reason of the computational
complexity of the proposed equations.
For the development of proposed model, this has
been assumed that that distribution of load in the network is
approximately identical over all parts of the network.
However, under heavy loads with large fluctuations,
additional parameters need to be considered based on certain
heuristics and past trends in the network flow. Thus, a
feedback network can be used to provide inputs based on the
values of past outputs of the network so as to provide a
guiding light in the process of obtaining most optimal
results. These are to be considered in the future scope of the
work.
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