Vitality productivity Multipath Routing for Wireless Sensor Networks: A Genetic Algorithm Approach

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Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
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Vitality productivity Multipath Routing for
Wireless Sensor Networks:
A Genetic Algorithm Approach
Annapoorna, Mr.Shivakumar Dalali,
Department of Computer Assoc.proff. Department of Computer
Science & Engineering, Science & Engineering
Don Bosco Institute of Technology, Don Bosco Institute oTechnology,
Bangalore, Karnataka, India, Bangalore, Karnataka, India,
Gmail:swamyswati005@gmail.com 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

is very difficult for replenish their batteries.
The sensor nodes are prone to be failure as
they work in harsh environment. Therefore
for long run operation energy efficiency and
the fault tolerance of the sensor nodes plays
an important role in WSNs.[2]
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].
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].
Here we discuss about a multipath
routing for relay based two tier WSNs in
which sensor nodes form the first tier and
the CHs from the second tier.Here the
algorithm is based on Genetic
Algorithm(GA) which is shown to be an
energy efficient.We consider 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.
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

5 to BS and second path is 1 to 2 to 6 to 9 to
the BS.There is no common intermediate
node in both the path except source and
destination.So,both paths are disjoint in
nature.
2.RELATED WORK
C.Intanagonwiwat[8] have discussed
a query driven multipath routing protocol
known as Direct Diffusion(DD).Here
routing mechanism is started by flooding the
interest message in the network.Node
creates gradient to that node from which the
current message has been received and
several paths can be discobered between
each pair of source and sink nodes when a
node receives the interest
message.Whenever a node detects any event
then node forwards to the BS.Then node
select the best path based on the packet
reception performance over each path i.e the
path with minimum latency, for data
transmission.
[9]D.Ganeshan have proposed
multipath routing algorithms for WSNs and
compare the performance of multipath
routing algorithm with single path under
various parameter i.e network lifetime in
disjoint and braided multipath
mechanism.Here selection criteria for path
with minimum delay due to involvement of
minimum number of intermediate
nodes.Here, did not consider distance and
residual energy to make the algorithm
energy efficient.
Energy aware multipath routing and
reliable [10],The objective of this protocol is
to make the wireless sensor network energy
efficient and providing the reliable data
transmission by maintaining a backup path
from each source node towards the sink
node.When anode receives the service path
request message ,it transmits a service path
reservation message towards the BS to
confirm discover path.
Braided Multipath Routing
Protocol[9] is based on Direct Diffusion
protocol and design multiple paths to
provide high fault tolerance in the
network.In this protocol,the development of
path is initiated by BS by sending a message
to its neighboring node towards source
node.The process is repeated until entire
source nodes are not receive thw
message.During exchange of the message

source nodes also construct alternative
around their next hop.DD is based on this
protocol,all thr drawbacks of DD is also
exist here.Energy-Efficient and QoS-based
Multipath Routing Protocol(EQSR)[11] has
been proposed by J.Othman and
B.Yahya.The protocol provides reliability
and delay requirements of real-time
applications.The protocol provides
reliability by using the lightweight XOR-
based Forward Error Correction(FEC)
mechanism[12],which introduces data
redundancy in the data transmission process
and fulfill the delay constraints.Applying a
queuing model to manage real time and non
real time traffic for differentiation technique
by using EQSR. It uses flooding strategy
during the neighbor discovery phase and
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
In [17], Heinzelman et al. have
discussed an energy model and we use this
energy model for the simulation work. In
this energy model, distance plays a very
vital role and it acts as an important
parameter for the calculation of energy
consumption in both type of channels i.e.
multipath fading (mp) and free space (fs)
channels. The free space model is used,
when distance between transmitter and
receiver is less than a a threshold d0 value
and in other case we use multipath (mp)
model. To forward the l-bit data with
considering the distance d is represented as
follows (refer equation 1):
ET(l,d)= lEelec + l fsd2 d<d0
(1)
lEelec + l mpd4 d ≥ d0
The energy consumption by
electronic circuit is represented with Eelec.
The energy consumption by ampliﬁer 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

B. Network lifetime
Previously various definition of
network lifetime has been discussed in [19],
[20]. The general method to define the
lifetime of WSN is number of rounds until
the first node die. Beside it there are some
other methods i.e. certain number of nodes
die or certain % of nodes die or until any
node alive. Moreover there are some
scenario, where lifetime is considered until
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. „
• R = {r1,r2,...rm}: The set of relay nodes
and rm+1 represents the BS. • Dist(ri,r j): It
defines the aerial distnace between ri and rj.
• 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.
In other words, we can say
ComCH(ri)={rj | Dist(ri,r j) ≤ Range}
Where ri and rj ∈ R.
• K: It shows the number of various paths in
the network.
• Hop(ri) represents the number of next hops
required to reach to the base station from
relay node ri. Ifri can directly communicate
with BS, then Hop(ri) is 1. The recursive
definition of Hop(ri) is defined as follows:
Hop(ri)= 1 If Dist(ri,BS) ≤ Range
1+Hop(rj) Hop(rj)=Min{Hop(rk) |
Dist(ri,rk) ≤ Range,∀rk} Where
ri, rj and rk (-R.
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
message. The HELLO message consists of
ID of CH, residual energy, distance from BS
and hop count. The relay nodes broadcast
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.
B. Cost function

The proposed algorithm generates
energy efficient multipath from all relay
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:
Cost(ri,r j) represents the incurred
cost to send data from ri to rj. The selection
of next hop by relay node ri is based on the
Cost function and ri always select that rj for
which the value of Cost is maximum. The
Cost function is energy efficient because its
value is maximum only when both type of
distance is minimum. As we know that the
energy consumption in sensor network is
directly proportional to square of the
distance. So, we can say that the next hop
selection based on this Cost is energy
efficient. Moreover, other parameter hop
count tries to select the next hop towards the
base station. Above mentioned Cost function
works only when j = BS. If a node able to
directly communicate with the BS then we
use following cost function.
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
multipath routing algorithm based on
GA.The discussion about various steps of
GA as follows with suitable example.
A.Chromosome representation and
Initial population
Chromosomes are represented as a
collection of relay nodes and length of the
chromosome is based on number of
paths(K).In fig1. There are 9 relay nodes
with BS and if we want to generate two
multipath from individual source to BS then
the length of chromosome is 18(9*2).In
chromosome,gene value at position 2
represents the next hop of relay node 2.In
path 1,the gene value at position 1 is 2 and
in second path the gene value at position 1 is

3.As an example for relay node 1 and 2 the
paths are
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 →
BS and 2 → 6 → 9 → BS.Shown in fig 2.
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.
I represents ith relay node ans j represents
the next hop of ith relay node.
C.Selection and Crossover
In this step of GA with higher fitness
value has been selected for the next
operation known as crossover.We use
Roulette-Wheel model for the selection of
chromosomes.
Suppose, for the crossover operation two
random values (i, j) are generated then i
represents the ith position from left and j
represents the m + jthposition from left.
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
Figure 3).
Fig. 3. Crossover operation.
In fig.3,2-point crossover has been
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
situation K-point crossover operation is used
and the crossover point is represented by a
vector as <i1,i2,....ik>.The range of i1 is
between 1 to m and range of i2 is between
m+1 to 2m similarly the range of ik is
between(K-1)m+1 to Km.

D.Mutation
For the mutation we select that relay
node which contributes minimum cost value
in 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
size of WSN#1 is 150×150 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
optimal results after 37 epochs. After
completion of crossover operation, we have
applied uniform mutation operation to
strengthen the chromosome.
Fig 4.Energy consumption in WSN#1

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 ﬁgure 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.
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
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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