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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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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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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