Ijecet 06 09_003

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International Journal of Electronics and Communication Engineering & Technology
(IJECET)
Volume 6, Issue 9, Sep 2015, pp. 19-29, Article ID: IJECET_06_09_003
Available online at
http://www.iaeme.com/IJECETissues.asp?JTypeIJECET&VType=6&IType=9
ISSN Print: 0976-6464 and ISSN Online: 0976-6472
© IAEME Publication
IMPROVING AVERAGE ENERGY
COVERAGE IN HETEROGENEOUS
NETWORK USING FARTHEST NODE SEP
PROTOCOL IN WSN
Rashmi Singh
M. Tech. Scholar
Department of Electronics & Communication Engineering
Institute of Engineering & Technology, Alwar-301030 (Raj.), India
Dr. Anil Kumar Sharma
Professor & Principal
Department of Electronics & Communication Engineering
Institute of Engineering & Technology, Alwar-301030 (Raj.), India
ABSTRACT
The impact of heterogeneity nodes, in terms of their energy, in wireless
sensor networks that are hierarchically clustered. We show that the behavior
of such sensor networks becomes very unstable once the first node dies,
especially in the presence of node heterogeneity. A new dynamic strategy for
selecting the optimal cluster head in Stable Election Protocol (SEP). The
proposed strategy selects a node as cluster head if it has the maximum energy
among all the available nodes in that particular cluster. The maximum energy
is that which is calculated at running time of the WSN. The proposed strategy
considers heterogeneous farthest distance based nodes and divides nodes
among normal, intermediate and advance nodes which improving the average
energy consumption with farthest distance base node using MATLAB tool.
Keywords: Energy consumption, E-SEP, Network Lifetime, SEP, WSNs.
Cite this Article: Rashmi Singh and Dr. A. K. Sharma. Performance
Enhancement of Wi-Max Mobile Handover OFDM Using M-QAM System
with Best-Relay Selection, International Journal of Electronics and
Communication Engineering & Technology, 6(9), 2015, pp. 19-29.
http://www.iaeme.com/IJECET/issues.asp?JTypeIJECET&VType=6&IType=9
1. INTRODUCTION
Clustered sensor networks can be classified into two broad types: homogeneous and
heterogeneous sensor networks. In homogeneous networks all sensor nodes are

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identical in terms of energy and hardware complexity. With purely static clustering in
a homogeneous network, it is evident that CHs will be overloaded with long range
transmissions to the remote sink, and extra processing is necessary for protocol co-
ordination and data aggregation. WSN faces a problem that CHs dies before other
nodes. However, to ensure that all nodes die at about the same time when system
expires, minor amount of residual energy is wasted. One method to ensure is rotating
the role of a cluster head periodically and randomly over all the nodes. The downside
of role rotation and using a homogeneous network is that all nodes should be capable
of act as CH, therefore should require necessary hardware capabilities. On the other
hand, in heterogeneous sensor network, two or more different types of sensor nodes in
terms of different energy are used. The problem area is that extra energy and complex
hardware can be embedded in few CH nodes, therefore reducing hardware cost of the
entire sensor network. Figure.1shows the cluster formation in WSN.
Figure 1 Cluster Formation in WSN
2. PROBLEM STATEMENT
One of the problems in the SEP protocol is that the cluster head which are far away to
the base station will consume more energy and are dying very frequently. Whereas
cluster-heads which are near to the base station takes operation until end, that will
cause network instability and the network lifetime is greatly affected. We will try to
enhance the lifetime of the network by avoiding direct transmission method, instead
we have used the multi-hop transmission method. By this method we can enhance the
lifetime of sensor network. The parameters for the evolutions of results for simulation
are described below:
Stability period: It is defined as the time interval between starting of the operation of
the network and to the death of the first node. It is also called “stable region.”
Instability period: It is defined as the time interval between the deaths of first sensor
node to the death of last node.
Cluster head per round: It is the number of nodes that sends data to the sink
directly after aggregating the data.
Network lifetime: It is defined as the staring of the network operation to the death of
the last node.

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Throughput: It is defined as the rate of data sent over network, it includes both the
data transfer i.e. from node to cluster head and cluster head to sink.
3. SYSTEM MODEL
In this section we describe our model of a wireless sensor network with nodes
heterogeneous in their initial amount of energy. We particularly present the setting,
the energy model, and how the optimal number of clusters can be computed. In all the
previous protocols, nodes are assumed uniformly distributed and the optimal
probability of the node to become cluster head is the function of spatial density. The
optimal clustering mainly depends on the energy model used. Same energy model is
used as in SEP and E-SEP protocol. Figure. 2 shows radio energy dissipation model,
in transmitting an L bit message over a distance d.
Figure 2 Wireless Energy Model Used
To achieve an acceptable signal-to noise ratio, the energy expended by radio is
given by:
Where d is the distance between sender and receiver node, L is the size of the
packet, Eelec is the energy dissipated per bit to run the receiver circuit or transmitter,
Efs and Emp depend on the transmitter amplifier model used. When the equation
given above is equated at d = d0 the value comes d0 .
We mark the following basic norms for WSNs in this paper:
 All sensor nodes are fixed after deployment.
 Every sensor node has a unique ID.
 Links are symmetric.
 There are no problem objects between communication pair.
 Sensor nodes are location-aware and can adjust their transmission power based on
distance.
As can be seen from the conventions above, the network is not expected to be
homogenous. It can be heterogeneous with various types of sensors and sink nodes
(static or mobile ones). The Heterogeneous Network Model defines the heterogeneous
wireless sensor network model used in the paper. Network model consists of N
sensors which are randomly deployed in a 100 X 100 square meters region. Some
expectations made for the network model and sensors are as follows:

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 Base station is located in the middle of the sensor field.
 Base station and nodes are stationary after deployment.
 Nodes endlessly sense the region and they continuously have data to send to base
station.
 Nodes do not have any information about their location i.e. they are location
uninformed.
 Some percentage of the nodes has high energy then the other nodes.
 Due to the exacting environment condition it is not possible to recharge the batteries
of the nodes.
4. PROPOSED IMPLEMENTATION
Label SEP, which recovers the stable region of the clustering hierarchy process using
the characteristic parameters of heterogeneity, specifically the segment of advanced
nodes (m) and the added energy factor between advanced and normal nodes (α). In
instruction to extend the stable region, SEP efforts to maintain the constraint of well-
balanced energy consumption. Automatically, advanced nodes have to become cluster
heads more often than the normal nodes, which is corresponding to a fairness
constraint on energy consumption. If the new heterogeneous setting (with advanced
and normal nodes) has result of the network so the apriori setting of popt, from
Equation (3), does not change. On the other hand, the total energy of the system
changes. Suppose that Eo is the initial energy of each normal sensor.
The energy of every advanced node will be Eo·(1 + α). The total energy of the
new heterogeneous setting is equal to: n (1−m)·Eo+n·m·Eo·(1+α) = n·Eo·(1+α·m).
So, the total energy of the system is amplified by 1+α ·m times. The main steps
are:
 The first improvement to the existing SEP.
 To increase the epoch of the sensor network in proportion to the energy increment.
 In order to optimize the stable region of the system, the new epoch must become
equal to 1/popt· (1 + α m) because the system has α m times more energy and virtually
α m more nodes (with the same energy as the normal nodes).
 We can now increase the stable region of the sensor network by 1+α·m times, if
(i) All normal node develops a cluster head once every (1+α ·m) rounds per epoch;
(ii) Each advanced node develops a cluster head exactly 1+α times every (1+α·m)
rounds per epoch; and Constraint (ii) is very severe—If at the end of each epoch the
number of times that an advanced sensor has become a cluster head is not equal to 1 +
α then the energy is not well distributed and the average number of cluster heads per
round per epoch will be less than n×popt. This problem can be condensed to a
problem of optimal threshold T(s) setting (cf. Equation 1), with the constraint that
each node has to become a cluster head as many times as its initial energy divided by
the energy of a normal node. (iii) The average number of cluster heads per round per
epoch is equal to n × popt (the spatial density does not change). Figure.3 shows the
proposed flowchart.

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Figure.3 Proposed Flow Chart
5. RESULT
The simulation is done in MATLAB. Let us undertake the heterogeneous sensor
network with 100 sensor nodes are randomly distributed in the 100m*100m zone. The
base station is situated at the center (50, 50). We have set the minimum probability for
becoming a cluster head (pmin) to 0.0005 and primarily the cluster head probability
for all the nodes is 0.05. The parameters used in our simulation are listed in the Table
1.
Table1 Simulation Parameters Description
Sl. No. Parameter Values
1 Simulation Round 100
2 Sink Location 0.000005
3 Network Size 100*100
4 Initial Energy Eo (0.5)
5 Initial energy of advance nodes 3 j
6 Distance threshold 2 mm
7 Multi root dist from higher e.ad 10 mm
8 Energy for data aggregation EDA 5 nJ/bit/signal
9 Transmitting and receiving energy Eelec 5 nJ/bit
10 Amplification energy for short distance Efs 111 Pj/bit/m2
11 Amplification energy for long distance Eamp 0.014pJ/bit/m4
12 Probability Popt 0.3
Figure 4 shows the results for the cases Average energy of each node with 25
numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.567.
Figure 5 shows the results for the cases Average energy of each node with 50
numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.531

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Figure 4 Average Energy of each node with 25 Numbers of rounds
Figure 5 Average energy of each node with 50 numbers of rounds
Figure 6 shows the results for the cases Average energy of each node with 80
numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.0.52.
Figure 7 shows the results for the cases Average energy of each node with 100
numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.456.
Figure 6 Average energy of each node with 80 numbers of rounds

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Figure 7 Average energy of each node with 100 numbers of rounds (----) Proposed
Figure 8 shows the results for the cases dead nodes with 25 numbers of rounds. It is
obvious that the rounds of the dead node 0. Figure 9 shows the results for the cases
dead nodes with 50 numbers of rounds. It is obvious that the rounds of the dead node
0.
Figure 8 Number of dead nodes of initial 25 Rounds
Figure 9 Number of dead nodes of initial 50 Rounds

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Figure 10 shows the results for the cases dead nodes with 80 numbers of rounds. It
is obvious that the rounds of the dead node 4. Figure 11 shows the results for the cases
dead nodes with 100 umbers of rounds. It is obvious that the rounds of the dead node
10.5.
Figure 10 Number of dead nodes of initial 80 Rounds
Figure 11 Number of dead nodes of initial 100 Rounds
Figure 12 Dead Node Occurrence with respect to Number of rounds

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Figure 13 Percent of Alive nodes with respect to Number of rounds
Figure 14 Percentage of Energy Consumption with respect to Number of Rounds
Figure 15 Throughput with respect to Number of Rounds
Finally, we observe the Improved Network Life time and average energy
consumption in Heterogeneous Network with our proposed result analysis.

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6. CONCLUSION
We proposed SEP (Stable Election Protocol) so every sensor node in a heterogeneous
two-level hierarchical network independently elects itself as a cluster head based on
its initial energy relative to that of other nodes. Unlike [5], we do not require any
global knowledge of energy at every election round. Unlike [4], SEP is dynamic in
that we do not assume any prior distribution of the different levels of energy in the
sensor nodes. We are currently extending SEP to deal with clustered sensor networks
with more than two levels of hierarchy and more than two types of nodes. This paper
also calculates the Dead node, Energy consumption, Percent of Alive nodes &
throughput of Proposed SEP in WSN. Furthermore, our analysis of SEP is not only
asymptotic, i.e. the analysis applies equally well to small-sized networks.
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