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IDL - International Digital Library Of Technology & Research Volume 1, Issue 5, May 2017

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Vitality productivity Multipath Routing for Wireless Sensor Networks: A Genetic Algorithm Approach Annapoorna, Department of Computer Science & Engineering, Don Bosco Institute of Technology, Bangalore, Karnataka, India, Gmail:swamyswati005@gmail.com

Mr.Shivakumar Dalali, Assoc.proff. Department of Computer Science & Engineering Don Bosco Institute oTechnology, Bangalore, Karnataka, India, Gmail:shivakumar.dalali@gmail.com

Abstract-The two factors included for deployment of any Wireless Sensor Network, those factors are efficient energy and fault tolerance. An efficient solution for fault tolerance is the Multipath routing in WSNs. Genetic Algorithm is based on the meta-heuristic search technique. Base station (BS) already prepared routing schedule in its routing table, all the nodes share it with the entire network. In proposed algorithm various parameters are used for efficient fitness function such as distance between sender and receiver nodes, distance between BS to hop node and on the number of hop to send data from next hop node to the BS. Simulation and evaluation are tested with various performance metrics in the proposed algorithm.

Keywords-Wireless sensor networks, Energy efficient multipath, fault tolerance and GA.

1. INTRODUCTION The

area

of

Wireless

Sensor

Networks has been explored highly by researchers due to its varied applications in real life such as in Environment monitoring, security surveillances,

habitatmonitoring,

underground mines and so on.[1],[2] The major consideration of WSNs is the limited power of the sensor nodes as they are operated with small batteries. In many

applications

they

are

randomly

deployed in harsh environment in which human interaction is almost negligible so it

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International e-Journal For Technology And Research-2017

is very difficult for replenish their batteries.

Here we discuss about a multipath

The sensor nodes are prone to be failure as

routing for relay based two tier WSNs in

they work in harsh environment. Therefore

which sensor nodes form the first tier and

for long run operation energy efficiency and

the CHs from the second tier.Here the

the fault tolerance of the sensor nodes plays

algorithm

an important role in WSNs.[2]

Algorithm(GA) which is shown to be an

Energy efficient multipath routing is one of the efficient solutions for the same.In multipath routing ,data packets are routed through two or more paths,therefore reduces chance of data loss at the recipient end node BS[3].So multipath mechanism is highly fault

tolerable

than

the

single

path

routing[4].

energy

is

based

efficient.We

on

consider

Genetic

several

parameters to make an algorithm energy efficient such as distance between sending and receiving node,hop count and distance between next hop to BS.To derive an efficient fitness function for the proposed algorithm all these parameters are used.

An example of multipath routing for relay based WSN with path is depicted in fig1.

Clustering is one of the best method to

save

the

energy in

WSNs.During

clustering process,sensor nodes are grouped and form the cluster on the basis of some criteria.There is a cluster head(CH) in each cluster and member sensor nodes always forward the sensed data to its CHs, CHs aggregate the received data and forward to BS.CHs are usually selected from the normal sensor nodes and therefore may die quickly do to their extra work load such as data aggregation and forwarding[6].

In the above fig. there are 9 relay nodes placed with a BS.For source node 1 there are two different paths first is 1 to 4 to

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International e-Journal For Technology And Research-2017

5 to BS and second path is 1 to 2 to 6 to 9 to

disjoint

and

braided

the BS.There is no common intermediate

mechanism.Here selection criteria for path

node in both the path except source and

with minimum delay due to involvement of

destination.So,both paths are disjoint in

minimum

nature.

nodes.Here, did not consider distance and

number

multipath

of

intermediate

residual energy to make the algorithm

2.RELATED WORK

energy efficient.

C.Intanagonwiwat[8] have discussed Energy aware multipath routing and

a query driven multipath routing protocol Diffusion(DD).Here

reliable [10],The objective of this protocol is

routing mechanism is started by flooding the

to make the wireless sensor network energy

interest

in the network.Node

efficient and providing the reliable data

creates gradient to that node from which the

transmission by maintaining a backup path

current message has been received and

from each source node towards the sink

several paths can be discobered between

node.When anode receives the service path

each pair of source and sink nodes when a

request message ,it transmits a service path

node

reservation message towards the BS to

known

as

Direct

message

receives

the

interest

message.Whenever a node detects any event

confirm discover path.

then node forwards to the BS.Then node

Braided

Multipath

Routing

select the best path based on the packet

Protocol[9] is based on Direct Diffusion

reception performance over each path i.e the

protocol and design multiple paths to

path with minimum latency, for data

provide

transmission.

network.In this protocol,the development of

high

fault

tolerance

in

the

proposed

path is initiated by BS by sending a message

multipath routing algorithms for WSNs and

to its neighboring node towards source

compare the performance of

node.The process is repeated until entire

[9]D.Ganeshan

have

multipath

routing algorithm with single path under

source

various parameter i.e network lifetime in

message.During exchange of the message

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nodes

are

not

receive

thw

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source nodes also construct alternative

In [17], Heinzelman et al. have

around their next hop.DD is based on this

discussed an energy model and we use this

protocol,all thr drawbacks of DD is also

energy model for the simulation work. In

exist here.Energy-Efficient and QoS-based

this energy model, distance plays a very

Multipath Routing Protocol(EQSR)[11] has

vital role and it acts as an important

been

and

parameter for the calculation of energy

B.Yahya.The protocol provides reliability

consumption in both type of channels i.e.

and

real-time

multipath fading (mp) and free space (fs)

provides

channels. The free space model is used,

reliability by using the lightweight XOR-

when distance between transmitter and

based

receiver is less than a a threshold d0 value

proposed

delay

by

J.Othman

requirements

applications.The

Forward

of

protocol

Error

mechanism[12],which

Correction(FEC) introduces

data

and in other case we use multipath (mp)

redundancy in the data transmission process

model. To forward the l-bit data with

and fulfill the delay constraints.Applying a

considering the distance d is represented as

queuing model to manage real time and non

follows (refer equation 1):

real time traffic for differentiation technique by using EQSR. It uses flooding strategy

ET(l,d)= lEelec + l fsd2 d<d0 (1)

during the neighbor discovery phase and

lEelec + l mpd4 d â&#x2030;Ľ d0

may exaggerate the exact value of mutual interference between different paths.In[13] authors discussed a fault tolerant routing scheme.This

scheme

consist

two

sub

processes 1)fault recovery process and 2)fault detection scheme.

3.SYSTEM MODEL A. Energy model

The

energy

consumption

by

electronic circuit is represented with Eelec.

The energy consumption by ampliďŹ er in free space as well as in multipath model is denoted

by

fs

and

mp

respectively.

Moreover, When a node receives the data, there are some energy consumption which is expressed with the following equation.

ER(l)=lEelec

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Where ri and rj ∈ R.

B. Network lifetime Previously

various

definition

of

network lifetime has been discussed in [19],

• K: It shows the number of various paths in the network.

[20]. The general method to define the

• Hop(ri) represents the number of next hops

lifetime of WSN is number of rounds until

required to reach to the base station from

the first node die. Beside it there are some

relay node ri. Ifri can directly communicate

other methods i.e. certain number of nodes

with BS, then Hop(ri) is 1. The recursive

die or certain % of nodes die or until any

definition of Hop(ri) is defined as follows:

node alive. Moreover there are some

Hop(ri)= 1

scenario, where lifetime is considered until

1+Hop(rj) Hop(rj)=Min{Hop(rk) | Dist(ri,rk) ≤ Range,∀rk} Where ri, rj and rk (-R.

the whole region is covered. In proposed work, we consider the network lifetime until the 50% of relay nodes alive.

4. TERMINOLOGIES We have used following terminologies to discuss the proposed algorithm. „

If Dist(ri,BS) ≤ Range

A. Boot strapping In proposed work, boot strapping is one of the important operation. In this operation entire relay nodes participate. It is started by BS with forwarding the HELLO

• R = {r1,r2,...rm}: The set of relay nodes

message. The HELLO message consists of

and rm+1 represents the BS. • Dist(ri,r j): It

ID of CH, residual energy, distance from BS

defines the aerial distnace between ri and rj.

and hop count. The relay nodes broadcast

• Range: It defines the communication range of a relay node.

•ComCH(ri):it represents the set of relay nodes and relay node belongs to this systetm may exchange the information with node ri.

same message and those relay node(s) which are in communication range update the information and again broadcast it till every relay node will not receive the same message.

In other words, we can say

B. Cost function

ComCH(ri)={rj | Dist(ri,r j) ≤ Range} IDL - International Digital Library

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The proposed algorithm generates energy efficient multipath from all relay

directly communicate with the BS then we use following cost function.

node to destination, so for the same we design the cost function based on distance between sender and receiver, hop count of next hop and distance between next hop to BS which as follows:

5.GENETIC ALGORITHM BASED MULTIPATH ROUTING Many researchers applied GA for clustering,routing and node deployment in WSNs[21],[22].Here we discuss about the

Cost(ri,r j) represents the incurred

multipath

routing

algorithm

based

on

cost to send data from ri to rj. The selection

GA.The discussion about various steps of

of next hop by relay node ri is based on the

GA as follows with suitable example.

Cost function and ri always select that rj for which the value of Cost is maximum. The Cost function is energy efficient because its

A.Chromosome representation and Initial population

value is maximum only when both type of

Chromosomes are represented as a

distance is minimum. As we know that the

collection of relay nodes and length of the

energy consumption in sensor network is

chromosome

directly proportional to square of the

paths(K).In fig1. There are 9 relay nodes

distance. So, we can say that the next hop

with BS and if we want to generate two

selection based on this Cost is energy

multipath from individual source to BS then

efficient. Moreover, other parameter hop

the length of chromosome is 18(9*2).In

count tries to select the next hop towards the

chromosome,gene

base station. Above mentioned Cost function

represents the next hop of relay node 2.In

works only when j = BS. If a node able to

path 1,the gene value at position 1 is 2 and

is

based

value

on number

at

position

of

2

in second path the gene value at position 1 is IDL - International Digital Library

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3.As an example for relay node 1 and 2 the

represents the ith position from left and j

paths are

represents the m + jthposition from left.

1 → 2 → 5 → BS and 2 → 5 → BS. Similarly we can get second path from relay node 1 and 2 to BS is represented by 1 → 3 → 7 →

Where 1 ≤ i ≤ m, m+1 ≤ j ≤ 2m and m represents the number of relay nodes. The whole

process

of

2-point

crossover

operation is shown in following figure (refer

BS and 2 → 6 → 9 → BS.Shown in fig 2.

Figure 3).

Fig. 2. Chromosome representation for subgraph (refer Figure (1)) with two path.

B.Fitness Function Fitness

value

represents

the

qualifying criteria of a chromosome.In proposed work we try to maximize the Cost.

Fig. 3. Crossover operation.

In fig.3,2-point crossover has been I represents ith relay node ans j represents the next hop of ith relay node.

C.Selection and Crossover

used because there are two path.Suppose there are K multiple path in the network then the length of chromosome is m*K.In this

In this step of GA with higher fitness

situation K-point crossover operation is used

value has been selected for the next

and the crossover point is represented by a

operation known as crossover.We use

vector as <i1,i2,....ik>.The range of i1 is

Roulette-Wheel model for the selection of

between 1 to m and range of i2 is between

chromosomes.

m+1 to 2m similarly the range of ik is

Suppose, for the crossover operation two

between(K-1)m+1 to Km.

random values (i, j) are generated then i IDL - International Digital Library

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optimal results after 37 epochs. After

D.Mutation For the mutation we select that relay

completion of crossover operation, we have

node which contributes minimum cost value

applied uniform mutation operation to

in

strengthen the chromosome.

fitness.After

selection

of

this

node,algorithm tries to replace it with a valid node.

6.SIMULATION RESULTS In

proposed

algorithm.

For

performing the simlation, we have used MATLAB and C on Intel Dual core processor processor with T9400 chipset, 3 GHz CPU and 6 GB RAM running on the platform Windows 8. We have designed a network scenario named as WSN#1. The

Fig 4.Energy consumption in WSN#1

size of WSN#1 is 150Ă&#x2014;150 square meter area and position of the base station is taken as (75, 75). The desired parameters for experiment

has

been

taken

as

same

discussed in [17]. we have considered an initial population of 100 chromosomes for the execution of proposed algorithm . With the help of Roulette Wheel selection model 8% chromosomes has been selected for the crossover operation,

. The algorithm

executed up to 65 epochs, but it returns the IDL - International Digital Library

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The Figure 5 represents the energy consumption per round. The results in Figure 5 has been calculated by varying the number of relay nodes from 10 to 50.

7.CONCLUSION Multipath routing is one of the prominent method to totolerate the fault in sensor network, .Therefore,in this research article we have presented multipath routing algorithm for WSNs using GA. Here, we No.of relay nodes Fig 5.Network lifetime in WSN#1 with two paths

The network lifetime of scenario WSN#1 has been depicted in Figure 4 and from the ďŹ gure we can see the network lifetime in both paths. We have calculated the network lifetime by varying the number of gateways from 10-50 and it is assumed that network alive until 50% nodes will not die. Selection of next hop is based on cost function and cost function considers the distance and hop count of possible next hop.

Therefore, there is a higher probability that next hop always towards the base station with minimum distance.

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have discussed all the basic steps of GA with suitable example. For the experimental purpose, we have executed the algorithm by varying the number of relay nodes from 10 to 40 and pictorially represented the two different path for 40 relay nodes. Moreover, for two different paths we have also represented the network lifetime in terms of round. However, in proposed work we did not consider the balancing of energy amongst the path as well as relay nodes. In future, we will consider the Quality of Service (QoS) parameters with balancing the energy amongst paths and relay nodes in both static and mobile scenario.

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“Directed diffusion for wireless sensor

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