International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 1- Dec 2013
An Empirical Model of cooperative communication
Model in Mobile Adhoc Networks
M.V.Rajesh1,LovaGangadhar Kandula2
Sr. Assistantprofessor ,2M.Tech Scholar
1,2
Dept of CSE, Aditya Engineering College, Aditya Nagar, Surampalem, Andhra Pradesh
1
Abstract:Cooperative communication is the one of the current
interesting research issue in the field of wireless sensors,
various topology architectures defined for data transmission
and cooperative communication in MANET, In this paper we
are introducing an empirical model for cooperative
communication with one of the evolutionary algorithm(i.e
genetic algorithm),In this approach we consider the factors of
signal strength and channel capacity for calculating the
communication cost, then we generates the chromosomes for
data transmission between source and destination through
relay nodes.
I.INTRODUCTION
Cooperative communication in mobile ad hoc networks is
basic characteristic while communication between the
nodes and apart from the peer to peer connections, these
nodes indirectly does not connect to the destination nodes
in these architectures of cooperative communications,
various topology structures defined for the cooperative
communication between the nodes in mobile ad hoc
networks, every topology architectures has their individual
advantages and vulnerabilities with their architectures or
structures .
Wireless sensor networks (WSNs) area
special class of ad hoc networks. Ina WSN, the
interconnected units arebattery-operated microsensors,
each ofwhich is integrated in a single package with lowpower signal processing, computation, and a wireless
transceiver. Sensor nodes collect the data of interest (e.g.,
temperature, pressure, soil makeup, etc.),and transmit
them, possiblycompressed and/or aggregated with those of
neighboring nodes, to the other nodes. In this way, every
node in the network acquires global view of the monitored
area that can be accessed by the external user connected to
the WSN through one or more gateway nodes (see Figure
1). Potential applications of sensor networks abound; they
can be used to monitor remote and/or hostile geographical
regions, to trace animals movement, to improve weather
forecast, and so on. Examples of scenarios where WSN can
be used are described in Estrin et al. [1999], Heinzelman et
al.[1999], Khan et al. [2000], Mainwaring al. [2002], Pottie
and Kaiser [2000],Sadler et al. [2004], Schwiebert et
al.[2001], Srivastava et al. [2001], Steereet al. [2000], and
Szewczyk et al. [2004].The following aspects that have to
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be carefully taken into account in the design stage are
peculiar to wireless ad hoc networks.
II.RELATED WORK
In mobile ad hoc networks Cooperative
communication usually refers to a system where
users share and coordinate their Resources to improve
the information quality of transmission and it is a
generalization of the depended Communication,
where multiple sources also serve as relays for each
other and previously study of relaying problems
appears in the informationtheory community to
enhance communication Between the source and
destination
[7].Recent
tremendous
interests
incooperative communications are due to the increased
understanding of the benefits of multiple antenna systems
[1].Although
multiple-input
multiple-output(MIMO)
systems have been widely acknowledged, it is difficult for
some wireless mobile devices to support multiple antennas
due to the size and cost constraints. Recent studies show
that
cooperative
communications
allow
singleantennadevices to work together to exploit thespatial
diversity and reap the benefits of MIMOsystems such as
resistance to fading, highthroughput, low transmitted
power, and resilientnetworks [1].In a simple cooperative
wireless networkmodel with two hops, there are a source, a
destination, and several relay nodes. The basic idea of
cooperative relaying is that some nodes, whichoverheard
the information transmitted from thesource node, relay it to
the destination node instead of treating it as interference.
Since thedestination node receives multiple independently
faded copies of the transmitted information from the source
node and relay nodes, cooperative diversity is achieved.
Relaying could beimplemented using two common
strategies,
• Amplify-and-forward
• Decode-and-forward
In amplify-and-forward, the relay nodes simply
boost the energy of the signal received from the sender and
retransmit it to the receiver. In decode-and-forward, the
relay nodes will perform physical-layer decoding and then
forward the decoding result to the destinations. If multiple
nodes are available for cooperation, their antennas can
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 1- Dec 2013
employ a space-time code in transmitting the relay signals.
It is shown that cooperation at the physical layer can
achieve full levelsof diversity similar to a MIMO system,
and the connectivity of wireless networks. Most existing
works about cooperative communications are focused on
physical layer issues, such as decreasing outage probability
and increasing outage capacity, which are only
linkwidemetrics. However, from the network’s point of
view, it may not be sufficient for the overall network
performance, such as the whole network capacity.
Therefore, many upper layer network-wide metrics should
be carefully studied, e.g., the impacts on network structure
and topology control. Cooperation offers a number
ofadvantages in flexibility over traditional wireless
networks that go beyond simply providing amore reliable
physical layer link. Since cooperation is essentially a
network solution, the traditionallink abstraction used for
networkingdesign may not be valid or appropriate. Fromthe
perspective of a network, cooperation canbenefit not only
the physical layer, but the wholenetwork in many different
aspects.
Genetic Algorithm is an evolutionary algorithmuses
genetic operators to generate the offspring of the existing
population. This section describes three operators of
Genetic Algorithms that were used in GA algorithm:
selection, crossover and mutation.
Selection: The selection operator chooses a chromosome in
the current population according to the fitness function and
copies it without changes into the new population.GA
algorithm used route wheel selection where the fittest
members of each generation are more chance to select.
Crossover: The crossover operator, according to a certain
probability, produces two new chromosomes from two
selected chromosomes by swapping segments of genes GA.
Genetic algorithm is one of the most efficient evolutionary
algorithms for solving the problems like NP hard problem
(i.e if a problem can have more than one solution) by
applying the process of crossover and mutation
Crossover and mutation are two basic operators
of GA. Performance of GA very depends on them. Type
and implementation of operators depends on encoding and
also on a problem.
Method of merging the genetic information of
two individuals; if the coding is chosen properly, two good
parents produces good children. This called Cross over
III.PROPOSED WORK
In this proposed architecture we introduced an
evolutionary algorithm for
optimal
cooperative
communication between the nodes with the parameters
channel capacity and signal strength, it leads to the
communication cost between the nodes, here genetic
algorithm finds the optimal communication cost by
applying the process of optimal chromosome or path
selection and mutation operators between the nodes, after
the mutation again calculate the communication cost
between the source and destination nodes followed by relay
nodes
During the cooperative communication between the nodes,
nodes communicate with each other with optimal path,
which is generated by evolutionary approach, When a node
transmits the data to the receiver, initially request made to
evolutionary processing module, it computes all the paths
between the source to destination and selects the optimal
path and transmits the data.
N2
N3
N6
N1
N4
N5
Figure1
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 1- Dec 2013
In figure1,it shows the cooperative communication
between the nodes ,even though various traditional
approaches available , every architecture of topology has
their own advantages and drawbacks. In our approach
when a node transmits the data from one node to another
,processing module process the request and evaluate the
path as follows.
IV.EVOLUTIONARY APPROACH
Genetic algorithm is an evolutionary algorithm, which
gives the optimal solution to the problems like NP hard
problem(i.e
problem can have more than one
solution).Genetic
algorithm
generates
the
new
chromosomes from the existing chromosomes, by applying
the process of crossover and mutation between the parent
chromosomes to generate the child chromosome and then
calculate the fitness of the chromosome with fitness
function.
In our approach we are considering our individual nodes as
genes and chromosomes as combination of the nodes from
source to destination and compute the fitness in terms of
signal strength and channel capacity. Consider an example
of nodes A,B,C,D,E,F ,if a node(gene) ’A’ wants to
transfer the data to the receiver ’F’ , processing module
computes the all the available paths from source to
destination and applies the fitness function and obtains the
optimal path and transmits the data over that path.
Evolutionary approach as follows
ABCDEF
ABEDCF
AEDCBF
ACDBEF
Communicationcost=Signalstrength+channellcapacity
Obtains the optimal path which has the best
communication cost and transmits the data over the path.
Step1: Sourcenodeselectsthedestination to transmit thedata
Step2:
Request
received
by
processingmoduleandgeneratesthepathsintopology
the
Step3: Processing module computes the path with their
signal strength and channel capacity
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Step4:
Compute
communicationcostwith
signalstrengthandchannelcapacityforfitness
Step5:selectoptimalpath(optimalcommunicationcost)andtra
nsmitsthedata.
V.CONCLUSION
We are concluding our approach with optimal approach
with evolutionary algorithm which generates the optimal
path based communication cost (signal strength + channel
capacity) over the mobile ad hoc networks. Our
cooperative communication approach evolves the
transmission of data between sender and receiver in an
efficient mechanism.
REFERENCES
1. J. Laneman, D. Tse, and G. Wornell, “Cooperative
Diversity in Wireless Networks: Efficient protocols and
Outage Behavior,” IEEE Trans. Info. Theory, vol. 50, no.
12, 2004, pp. 3062–80.
2. P. H. J. Chong et al., “Technologies in Multihop Cellular
Network,” IEEE Commun.Mag., vol. 45, Sept.
2007, pp. 64–65.
3. K. Woraditet al., “Outage Behavior of Selective relaying
Schemes,” IEEE Trans. Wireless Commun., vol.
8, no. 8, 2009, pp. 3890–95.
4. Y. Wei, F. R. Yu, and M. Song, “Distributed Optimal
Relay Selection in Wireless Cooperative Networks
with Finite-State Markov Channels,” IEEE Trans. Vehic.
Tech., vol. 59, June 2010, pp. 2149–58.
5. Q. Guan et al., “Capacity-Optimized Topology Control
for MANETs with Cooperative Communications,”
IEEETrans. Wireless Commun., vol. 10, July 2011, pp.
2162–70.
6. P. Santi, “Topology Control in Wireless Ad Hoc and
sensor Networks,” ACM Computing Surveys, vol. 37,no.
2, 2005, pp. 164–94.
[7] T. Cover and A. E. Gamal, “Capacity Theorems for the
Relay Channel,” IEEE Trans. Info.Theory, vol. 25, Sept.
1979, pp. 572–84.
[8] Q. Guan et al., “Impact of Topology Control on
Capacity
of Wireless Ad Hoc Networks,” Proc. IEEE ICCS,
Guangzhou, P. R. China, Nov. 2008.
[9] P. Gupta and P. Kumar, “The Capacity of Wireless
Networks,”
IEEE Trans. Info. Theory, vol. 46, no. 2, 2000,
pp. 388–404.
[10] M. Burkhart et al., “Does Topology Control Reduce
Interference?,” Proc. 5th ACM Int’l. Symp. Mobile
AdHoc Networking and Computing
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 1- Dec 2013
BIOGRAPHIES
LovaGangadharKandulais a student of Aditya
Engineering College, Surampalem. Presently
he is pursuing his M.Tech [Computer Science]
from this college and he received his M.C.A
from SriSaiMadhavi Institute of Engineering
Technology, affiliated to Andhra University,
Mallampudi in the year 2011. His area of
interest includes Computer Networks,Soft
wareEngeneeringand all current trends techniques in Computer
Science
MrM.V.Rajesh, well known and excellent
teacher received M.Tech (CSE) from JNTU,
Kakinada is working Sr.Associateprofessor
,Dept of CSE at Aditya Engineering College.
He has 8.5 years of industrial and teaching
experience. To his credit couple of
publications both national and international
conferences/journals. His areaof interest
includes Computer Networks and other Software Engineering.
And guided many projects.
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