Abstract:-

There is

main issue in wireless sensor networks to choose possible and best path to

transfer information from network nodes to the station. In the wireless sensor

Networks the biggest problem is to increase the network lifetime. As there are

different factors on which process of choosing the best path can be based.

Factors: (Data transfer accuracy, Energy consumption, Response time, Delay).

So nowadays

there is a latest routing method. This method is to use harmony search algorithm

in small scale sensor networks. The Goal is, In the process of increasing the

lifetime of wireless sensor network we have to represent the harmony search

algorithm as successful algorithm to solve wide range or problem with

problem-independency.

So in this we are going to improve the energy

efficiency in harmony search algorithm. We will try to manage network energy

consumption and path length to make balance between them. The path energy consumption

should be low when it will choose path because we will choose starter energy of

each node randomly from range.

Introduction:-

Wireless

sensor network is the network to use to monitor the environmental conditions.

Such networks use in inaccessible conditions or in difficult and harsh

environments. And the devices for this networks use small batteries with less

energy power. So it is not easy to charge the battery or to change the nodes.

That’s why it is the one of main problem in wireless sensor networks to manage

and maintain the energy.

So our main

goal is to choose best path to transfer the data from base station to network

nodes. The best path is, that consume less energy. For this purpose we have to

choose the path which involves only few nodes. This path must be establish the

balance between network energy consumption and remaining path energy.

We are going

to define a new objective function for the harmony search algorithm in order to

increase the life time of wireless sensor network. This algorithm was developed

in 2001. This is meta-heuristic algorithm. It is use to optimize the problem.

Related-Work:-

Zong Woo

Geem is the person who developed the harmony search algorithm. He was inspired

by the Musician. The harmony search algorithms affect many problems to solve

them successfully. Following are the problems which are successfully solved by

HS algorithm.

·

Transportation pahts

·

Succession planning

·

High speed network mlticast routing

·

Water distribution design

·

Transfer pipe network design

When Zeng

was on initial state he implemented the routing process to increase wireless

sensor network life time. After implementation of routing process he tried to

make improvement of decryption in the harmony memory. On that time routing

algorithm include only one specific parameter. But after that, to improve the

local search and to resolve the smallest coming initial generations the dynamic

matching introduced for parameters. So that was the peek point of research that

effect local search to improve convergence speed of algorithm. In the plus

point the objective function was designed to change the energy consumption and

increase the network lifetime. HSBEER routing algorithm used to save energy for

wireless sensor network by harmony search. It leads them in major developments.

All this

happens step by step. Following

1. Harmony memory

decoding improved with wireless sensor network routing.

2. New harmony also

improved and dynamic compatibility was also represented to solve smallest

coming in initial generation and for searching on next generation

3. In final step

the local search algorithm was represented to improve convergence speed and

accuracy of routing algorithm. Also some main functions designed to consume

less energy and increasing network life

So after

solving many problems the objective function was developed to consume less

energy and increase network life. As it is important to optimize and extend the

network lifetime. So the method proposed that able to support optimize methods

in wireless receiver network which develop protocols that relay on centralized

clusters. By this a protocol algorithm was designed by using harmony search

algorithm for wireless device. That method based on musical meta-heuristic.

This method was purely design to minimize the distance between cluster member

and head.

Proposed-Method:-

The proposed

method is able to find best path in sending and receiving node. This process

take place again and again to find best path on starting node until it loses

its energy in network.

Transferring

the information from one node to other node is the result in loss of node

energy. So we can say network lifetime deplaned on the number of times nodes

are able to send data. Suppose if we find best path which lose less energy and

more nodes can send information then the network lifetime will increase and

death of initial node will be late.

We are

trying to search (by harmony search algorithm) on effective objectives

functions to increase the network lifetime. Let suppose the wireless sensor

network with fixed nodes. Each sensor node cand send and receive information

with nodes in its range. Every node may have different powers but they share

same range. The sink node is fixed and sizes of send packets are same so all

this operation done in receiver node.

The

consumption of energy can be find by this equation.

Er

is the energy consumption in node

Eelec

is the energy needed to transfer information

Eamp

is energy needed for strong signal

l is data

length

d is the

distance in start and sink node

ER consumption of energy

Initial-Harmony-Memory:-

·

(n+1)*HMS is the matrix for harmony memory.

·

HMS is number of found paths in between receiver and

sender.

·

n is number of nodes

In the found

paths the longest path is the path on which all nodes found. Every node is neighbor for other nodes that found in

range. Recursive method will be used to find nearest paths to the receiver

node. Few paths will be known as harmony matrix element if we use this method.

So in this

way the longest path will not be selected and runtime will be improved.

A View of Path Selection in the

Wireless Sensor Network.

After

creating harmony matrix may be it will not have best paths. So we have to

create new harmonies that will replace the worst harmonies.

Below is the

explanation to access new harmonies.

A musician

has three choices to play the notes in orchestra

·

First:- Play the well known notes.

·

Second:- Play the notes that is mention.

·

Third:- Play the random nodes.

We can

choose the new harmony vector such as A=(a1,a2,….ai,…,an) in the harmony

search algorithm by three ways.

·

In the present harmony memory use the variables

·

Make some modification in variabless.

·

Make a variable randomly

Fitness-Function:-

It is very

important to choose a best fitness function. We have to present a fitness

function that will increase lifetime. The research shows that the below

function can be the best result on wireless sensor network.

F(X)=1/EminxE(X)

This fitness function will reduce the

energy consumption and will find the best solution.

The following is the proposed

algorithm.

for (i = 0; i < tmax; i++) for (j = 0; j < HMS; j++) for each index in row if (Rnd < HMCR( Then using HMC if (Rnd < PAR) Then using PA Else New-Fitness-Function:- Following are the 3 factors that affect the path choosing. · Consumption of energy will be average in path · In the nodes on path the energy the remaining energy will be average · The remaining energy nodes on path will be minimum In this equation, Er is the energy consumed by the node which sends information, and ER is the energy consumed by the node which receives the information. F(x)=1/EminxE(X) So this function is compared with fitness function proposed. Experiment:- In figures from (a) to (d). It shows the average remaining energy of nodes, Minimum residual energy, from average remaining energy of all node's standard deviation and life time of network in some networks. From these figures. As compare to proposed method to EEHSBR algorithm the average remaining energy of network is lower in some cases. But in two cases where 20 and 50 nodes the average residual energy of network is not lower. The proposed method's average standard deviation is lower and in EEHSBR algorithm average standard deviation is high. Where 50 and 70 nodes in figure the EEHSBR has lower standard deviation. Where the minimum remaining energy the proposed method result in best levels. But it is not for the case where 100 nodes So that the EEHSBR algorithm was better. So the average energy consumption was more in the proposed method, Due to choosing the long path more energy was consumed compared with EEHSBR algorithm. So in the experiment the efficiency of the new fitness function is totally tangible for consumption of energy. Also to find the optimal path in harmony search algorithm it can be use this fitness function. The EEHSBR improved new fitness function of proposed method according to figure. The values 128.02%, 147.83%, 57.02%, 118.70%, 73.89%, 186.17%, 127.79%, 63.13%, 26.12%, and 132.91% have been observed according to the number of nodes in the network. Conclusion:- So in this study we was focusing on the new fitness function for wireless sensor network routing to increase life time for networks in the HSA called successful metaheuristic algorithm. The new objective function is are on following factors. · Emin that is minimum remaining energy of nodes on path · Eavg that is average energy of node in path · E(X) that is total energy consumed in path. The propose algorithm was compared with EEHSBR algorithm for efficiency evaluation. As there are four important criteria for comparing algorithm; average remaining energy, that standard deviation from remaining energy of nodes, minimum remaining energy and network life time. The experiment was meant to research the effect of each algorithm on the network life time. All the nodes are the same in experiment. So the network lifetime is increase by 128.02%, 147.83%, 57.02%, 118.70%, 73.89%, 186.17%, 127.79%, 63.13%, 26.12%, and 132.91% for some numbers of nodes. It is also compared with EEHSBR algorithm. Reference:- Images à http://www.scirp.org Research Paper à Rahimkhani, K. and Forouzesh, F. (2017) Improved Routing in Wireless Sensor Networks Using Harmony Search Algorithm. Wireless Sensor Network