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

Rashmi Singh and Dr. Anil Kumar Sharma


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.

Improving Average Energy Coverage In Heterogeneous Network Using Farthest Node Sep

Protocol In WSN


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:



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.


Protocol In WSN


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.


numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.531



Figure 4 Average Energy of each node with 25 Numbers of rounds

Figure 5 Average energy of each node with 50 numbers of rounds


numbers of rounds. It is obvious that the rounds of the energy decrease 0.6 to 0.0.52.


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


Protocol In WSN


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 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 12 Dead Node Occurrence with respect to Number of rounds


Protocol In WSN


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.



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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IJECET_06_09_003