Support for real time multimedia applications such as, video telephony, financial stock quote services, and multiplayer interactive games etc., is very essential in Mobile Ad hoc Networks (MANETs). Such applications require multiple Quality of Service (QoS) parameters to be satisfied, like bandwidth, end-to- end delay, packet loss rate, jitter, etc. This paper considers the problem of finding node disjoint and multi-constrained QoS multipaths from source to destination by using agent based fuzzy inference system. The proposed scheme, Fuzzy based Node Disjoint Multipath QoS Routing (FNDMQR) operates in the following steps by integrating static and mobile agents. (1) Determination of multiple paths and picking up of resource information (available bandwidth, link delay, and packet loss rate) of the intermediate nodes from source to destination. (2) Recognition of node disjoint, and multi-constrained QoS fit paths by using Takagi-Sugeno Fuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoS weight from available resource information of the intermediate nodes. (3) Selection of the best path depending on the fuzzy QoS weight. (4) Maintenance of QoS path when path breaks due to mobility of node or link failure. To test the performance effectiveness of the approach, we have analyzed the performance parameters like packet delivery ratio, average end-to-end delay and overall control overhead. The scheme performs better as compared to a node-disjoint multipath routing in MANETs.

1. Full Paper
Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013
Fuzzy Based Node Disjoint QoS Routing in MANETs
by Using Agents
Vijayashree Budyal1, S. S. Manvi2, S. G. Hiremath3
1
Basaveshwar Engineering College, Bagalkot, India
Reva Institute of Technology and Management, Bangalore, India
3
G. M. Institute of Technology Davangere, India
E-mail: vrbudyal@yahoo.co.in, sunil.manvi@revainstitution.org, drsgh@yahoo.co.in
2
can have nodes and links in common. When a link or node is
on several paths severe flow occurs when the incoming traffic
load is high. As a result shared link or the node becomes the
bottleneck. Node disjoint paths provide more reliability than
the link disjoint paths [3].With the increasing demand in realtime multimedia, application in video telephony, video
conferencing, and military arena requires multi-constrained
Quality of Service (QoS) to be fulfilled. The QoS requirement
of connection includes parameters like bandwidth, end-toend delay, jitter, packet loss rate etc.
Multi-constraint QoS parameters are imprecise and
uncertain due to dynamic topology of MANETs. However,
selecting a route, which satisfies all multiple constraints, is
an NP complete problem [4]. There is no accurate mathematical
model to describe it. Fuzzy logic is used to provide a feasible
tool to solve the multi-metric QoS problem. Fuzzy logic is a
theory that not only supports several inputs, but also exploits
the pervasive imprecision information [5]. So adopting fuzzy
logic to solve multi metric problems in ad hoc networks is an
appropriate choice.
Multi-constraint based routing protocols use QoS
satisfied paths other than the single shortest path to route
the packets. If multiple node disjoint paths with multiconstraint QoS paths are set up between a source and a
destination, then source node can use these routes as primary
and backup routes, i.e., a new route discovery is invoked
only when all of the routing paths fail or when there only
remains a single path available, whenever node or link fails.
This helps to reduce overhead in finding alternative routes
and extra delay in packet delivery introduced. Therefore, in
this paper we adopt both node disjoint and multi-constraint
QoS routing in MANETs.
Software agents based applications are an emerging
discipline, which can be applied to provide flexible, adaptable,
and intelligent services in MANETs. Software agents are
autonomous and intelligent programs that execute tasks on
behalf of a process or a user. They have two special properties:
mandatory and orthogonal, which make them different from
the standard programs. Mandatory properties are: autonomy,
reactive, proactive and temporally continuous. The
orthogonal properties are: communicative, mobile, learning
and believable [6].
Abstract— Support for real time multimedia applications such
as, video telephony, financial stock quote services, and
multiplayer interactive games etc., is very essential in Mobile
Ad hoc Networks (MANETs). Such applications require
multiple Quality of Service (QoS) parameters to be satisfied,
like bandwidth, end-to- end delay, packet loss rate, jitter, etc.
This paper considers the problem of finding node disjoint and
multi-constrained QoS multipaths from source to destination
by using agent based fuzzy inference system. The proposed
scheme, Fuzzy based Node Disjoint Multipath QoS Routing
(FNDMQR) operates in the following steps by integrating
static and mobile agents. (1) Determination of multiple paths
and picking up of resource information (available bandwidth,
link delay, and packet loss rate) of the intermediate nodes
from source to destination. (2) Recognition of node disjoint,
and multi-constrained QoS fit paths by using Takagi-Sugeno
Fuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoS
weight from available resource information of the
intermediate nodes. (3) Selection of the best path depending
on the fuzzy QoS weight. (4) Maintenance of QoS path when
path breaks due to mobility of node or link failure. To test the
performance effectiveness of the approach, we have analyzed
the performance parameters like packet delivery ratio, average
end-to-end delay and overall control overhead. The scheme
performs better as compared to a node-disjoint multipath
routing in MANETs.
Index Terms—MANETs, QoS, Takagi-Sugeno fuzzy Inference,
software agents.
I. INTRODUCTION
Ad hoc wireless network consists of collection of mobile
devices like, personal digital assistant (PDA), laptops, cell
phones etc. These nodes are interconnected by multi-hop
communication path, due to limited transmission range. The
route found between source and destination becomes invalid
often because of the temporary topology of the network.
Therefore routing in Mobile Ad hoc Networks (MANETs) is
a challenging task [1].Multi-path provides more than one route
to the destination node. Multi-path routing protocols are
deemed superior over conventional single path protocols for
enhanced throughput, reliability, robustness, load balancing,
fault-tolerance, offering QoS, and to avoid frequent route
discovery attempts [2]. Multi-path routing protocols can
attempt to find node- disjoint, link-disjoint, or non-disjoint
routes. Node- disjoint routes have no nodes or links in
common on the routes. Link-disjoint routes have no links in
common, but may have nodes in common. Non-disjoint routes
© 2013 ACEEE
DOI: 03.LSCS.2013.1. 544
A. Related Work
Some of the related works to build multi-constrained QoS
routing in MANETs are as follows: Fuzzy cost based multi7

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Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013
constrained quality of service routing is discussed in [7] to
select an optimal path by considering multiple independent
QoS metrics such as bandwidth, end-to-end delay, and
number of intermediate hops.The work given in [8] explores
the node disjoint path routing subject to different degrees of
path coupling, with and without packet redundancy. Multipath routing problem of MANETs with multiple QoS
constraints, which may deal with the delay, bandwidth and
reliability metrics, and researching the routing problem is
explained in [9].
Architecture for guaranteeing QoS based on nodedisjoint
multi-path routing protocol in MANETs is explained in [10].
The work given in [11] uses fuzzy set and roughs set theory
to select an effective routing path in MANETs. In the first
stage, the data set consisting of resources and paths are
fuzzified. In the second stage, information gain is calculated
by using ID3 algorithm for evaluating the importance among
attributes. In the third stage, a decision table is reduced by
removing redundant attributes without any information loss.
Finally, if-then decision rules are extracted from the
equivalence class to select the best routing path. Fuzzy based
priority scheduler for mobile ad-hoc networks, to determine
the priority of the packets using Destination Sequenced
Distance as the routing protocols is presented in [12]. The
proposed fuzzy agent based Node Disjoint Multi-path QoS
Routing in MANETs is motivated by observing inherent
drawbacks of existing QoS routing schemes like: lack of
support of multi-constraint QoS routing and maintenance of
the QoS path when link/node fails.
B. Our Contributions
In this work, we investigate on the use of Takagi-Sugeno
fuzzy inference system (TSFIS) for multi-constrained QoS
route selection in MANETs, integrating static and mobile
agents. Source knows the multiple nodes disjoint paths to
the destination, and collects the resource information
(available bandwidth, delay, and packet loss rate) of
intermediate nodes. The source uses gathered intermediate
node information to select the QoS path by using TSFIS
model. This model accepts uncertain and imprecise crisp
parameters like, available bandwidth, link delay, and packet
loss rate as input and is being processed in stages, i.e.,
fuzzification, inference, and defuzzification. After experiencing
all the stages, a single value score fuzzy QoS weight is
generated from the combination metrics for each node on the
path. This is used to measure QoS satisfaction on the path.
The performance of our scheme Fuzzy based Node disjoint
Multipath QoS Routing (FNDMQR) is compared to nodedisjoin multi-path routing in MANETs (NDMRP) [9].The rest
of paper is organized as follows. Section II explains proposed
work on fuzzy agent based multi-constrained QoS routing.
Section III describes an evaluation of our approach using
simulation. Finally, section IV concludes our paper.
II. PROPOSED WORK
This section describes network model, Takagi-Sugeno
Fuzzy Inference System (TSFIS), QoS routing agency, and
8
© 2013 ACEEE
DOI: 03.LSCS.2013.1.544
fuzzy and agent based multi-constraint QoS routing scheme.
A. Network Model
An ad hoc network consists of set of mobile nodes and
set of links between the mobile nodes as shown in figure 1.
Due to mobility of the nodes in the ad hoc network, link
connection varies with respect to time. Each mobile node has
certain transmission range. Each node is equipped with an
agent platform and an agency in which agents reside. We
assumed that agents have protection from hosts on which
they execute. Similarly, hosts have protection from agents
that can communicate on available platform. The secured
platform consists of protection from denial of execution,
masquerading, eavesdropping, etc. Recently developed
techniques for mobile agent security have techniques for
protecting the agent platform.
Fig. 1. A Mobile Ad hoc Network
B. Takagi-Sugeno Fuzzy Inference System
Fuzzy system is classified as Mamdani and Takagi-Sugeno
models. In this paper we propose Takagi Sugeno (first-order)
fuzzy inference system for reasoning, because as it has high
interpretability and computational efficiency, and built-in
optimal and adaptive technique. And also, it is not necessary
to define a prior linguistic terms for conclusions, since the
mapping is direct. And also, the effort of performing
defuzzification is saved, because the crisp output is directly
determined by the fuzzy mean formula.
Our Takagi-Sugeno fuzzy system consists of three crisp
inputs and one output. The system inputs are available
bandwidth ‘AB’ and link delay ‘TD’, and packet loss rate
‘PR’ of the intermediate nodes and output is QoS weight ‘γ’.
Three inputs are characterized by bell shaped membership
functions. Bell function for ‘AB’ is defined by equation 1.
i ( AB ) 
1
AB  ci 2bi
1 (
) ……….
ai
(1)
Where a, b and c are the parameters of membership function governing the centre, width and slope of the bell-shaped
membership function. ‘TD’ and ‘PR’ take similar kind of bell
function. The steps involved in FIS are fuzzification,inference

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Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013
and defuzzification. µi(AB) is Membership function value for
the available bandwidth.
Fuzzification: The first step is to consider the crisp inputs
and determine the degree to which they belong to each of the
appropriate linguistic sets via bell membership functions
which is termed as fuzzification. Fuzzification converts input
data into suitable fuzzy values (linguistic terms). The linguistic
terms, which divide the membership functions for available
bandwidth, are {ABless, ABmore} and is as shown in figure
2. Link delay linguistic terms are {TDless, TDmore}, and for
packet loss rate the linguistic terms are {PRless, PRmore}.
Vertical coordinates represent the degree of membership,
which distributes in the interval of [0- 1].
Defuzzification: The final output fuzzy QoS weight ‘γ’ of the
system is the weighted average of all rule outputs, computed
as given in equation 3.
N
w z
i i

i 1
N
w
……….
(3)
i
i 1
C. QoS Routing Agency
Each node comprises of Fuzzy based Node Disjoint Multipath QoS Routing agency (FNDMQR). Components of
agency and their interactions are depicted in figure 3. Agency
consists of Knowledge Base (KB), static agents and mobile
agents. Static agent are Administrator Agent (AA), and QoS
Decision Agent (QDA). Mobile agents are Disjoint Agent
(DA) and Recovery Agent (RA).
Fig. 2. Membership functions for available bandwidth
Inference: Fuzzified data trigger one or several rules in
the fuzzy model to calculate the result. The fuzzy rules are
realized in the form of IF-THEN. The input parameters are
combined using T-norm operator ‘AND’. The total number
of rules formed is as follows:
Rule 1: If AB is ‘ABless’ and TD is ‘TDless’ and PR
is‘PRless’ Then z1 = Ψ1AB + ζ 1T D + φ1PR + σ1
Rule 2: If AB is ‘ABless’ and TD is ‘TDless’ and PR
is‘PRmore’ Then z2 = Ψ2AB + ζ 2TD + φ2PR + σ2
Rule 3: If AB is ‘ABless’ and TD is ‘TDmore’ and PR
is‘PRless’ Then z3 = Ψ3AB + ζ 3TD + φ3PR + σ3
Rule 5: If AB is ‘ABmore’ and TD is ‘TDless’ and PR
is‘PRless’ Then z5 = Ψ5AB + ζ5TD + φ5PR + σ5
Rule 6: If AB is ‘ABmore’ and TD is ‘TDless’ and PR
is‘PRmore’ Then z6 = Ψ6AB + ζ6TD + φ6PR + σ6
Rule 7: If AB is ‘ABmore’ and TD is ‘TDmore’ and PR
is‘PRless’ Then z7 = Ψ7 AB + ζ7TD + φ7 PR + σ7
Rule 8: If AB is ‘ABmore’ and TD is ‘TDmore’ and PR
is‘PRmore’ Then z8 = Ψ8AB + ζ8TD + φ8 PR + σ8
The output level zi of each rule is weighted by the firing
strength wi of the rule given by 2. Ψi , ζi , φi , and σi are
constants chosen between 0-1. Where i = 1 to N. N is the
number of rules. For example, for and ‘AND’ rule with inputs
AB and TD, and PR have a firing strength as given by equation
2.
wi = µ(AB) . µ (TD). µ (PR) ……..
(2)
Where µ (AB), µ (TD), and µ (PR) are the membership values
for inputs available bandwidth and link delay and packet
loss rate.
© 2013 ACEEE
DOI: 03.LSCS.2013.1. 544
Fig. 3. Fuzzy based node disjoint multipath QoS routing agency
KB: KB of source comprises of information of node ID,
destination, resource information {AB, TD, PR} of the
intermediate nodes on the paths, multiple path IDs from source
to destination and their fuzzy QoS weight γ obtained by using
TSFIS and running application(s) details.. Intermediate node
KB consists of node status (connected/disconnected to
network), Node disjoint Forward QoS Routing Table
(NDFQRT), {AB, TD, PR} of its own. KB is read, updated
and is used by agencies (AA, QDA, DA and RA) to establish
QoS route and to maintain the path between source and
destination.
Administrator Agent: It is a static agent and performs the
following functions at source, (1) creates and dispatches DA
to find multiple paths to destination, (2) collects multiple
node disjoint paths and resource information of intermediate
nodes from DA, (3) computes γ for each node by using TSFIS
and ‘Γ’ for each node disjoint path (4) selects a QoS node
disjoint path from multiple node disjoint paths, and (5) initiates
reconstruction of QoS path upon request from RA during
link/node failure.
Disjoint Agent: It is a mobile agent triggered by AA of
source whenever it wishes to send data to the destination.
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DA’s are dispatched by AA to reach all its neighbors. Every
DA performs the following functions. (1) Traces all the feasible
paths to the destination by cloning. Gathers intermediate
node resource information {AB, TD, PR}. (2) Handover the
multiple path information to AA of destination. (3) AA of
destination separates out the node disjoint path from a
number of multiple paths identified by DA, and (4) DA
traverses back through the node disjoint paths to reach
source, gathering resource information of the intermediate
nodes.
QoS Decision Agent: This agent is a static agent triggered
by AA only at the source node. It is responsible for computing
the γ for each of the node on the disjoint paths by using
TSFIS. Updates computed γ of each node on the node disjoint
paths in AA of source. Later it is disposed off.
Recovery Agent: It is a mobile agent and performs the
operation of route maintenance whenever link/node fails.
route to reach the source. When destination AA receives
duplicate DA, it compares the whole node IDs of the entire
route with existing node disjoint paths in its reverse routing
table. If there is no common node (except source and
destination) between the node IDs from the the duplicate DA
and node IDs of existing node disjoint path in the destination
reverse routing table then, the path in current DA is node
disjoint path and is recorded in the reverse routing table of
the destination and DA traces the reverse route to reach the
source. Otherwise, current DA is disposed.
DA collects the intermediate node’s resource information
{AB, TD, PR} while tracing back the reverse path from
destination to source. The multiple node isjoint paths and
resource information of the intermediate node is made
available to the AA of source for further QoS verification.
2) Fuzzy Agent based QoS Path Selection: Multiconstrained QoS path is selected from numerous known node
disjoint multi-paths placed in AA of source by using TSFIS
(refer section II B). TSFIS computes the γ for every node on
each of the path by considering { AB, TD, PR } as input
metrics to TSFIS. AA of source computes Γ by considering γ
of all the nodes on the path and is given by equation 4.
D. Fuzzy and Agent based Multi-constraint QoS Routing
Scheme
This section describes the functioning of the proposed
multi-constraint QoS routing scheme. The scheme operates
in the following steps.
1) Recognition of node disjoint multiple paths to the
destination:
When a source node needs multi-constraint QoS path to
the destination. Source AA dispatches DA to reach its
neighbors. DA carries source ID, sequence number, maximum
number of hops, and traveled node list. Upon reaching the
intermediate node, AA of intermediate node checks for the
duplication of the DA by looking at the sequence number.
When receiving a duplicate DA, the possibility of finding
node disjoint multiple paths is zero if it is dropped, for it may
come from another path. But if all of the duplicate DA are
broadcast, this will generate broadcast storm and decrease
performance. In order to avoid this problem DA records the
shortest routing hops to keep loop-free paths and decrease
routing broadcast overhead.
When intermediate node receives DA for the first time, it
checks the node list of path traversed and calculates the
number of hops from the source node to itself and records
the number as the shortest number of hops in its reverse
routing table. If the node receives the duplicate DA, it
computes the number of hops and compares with shortest
number of hops in its reverse routing table. If the number of
hops is more than shortest number of hops in the reverse
routing table, then the DA is dropped. Only when it is less
than or equal to the shortest number of hops, the node
appends its own address to the node list of path in DA and is
cloned to reach the neighbors or the destination. Agent
cloning is a technique of creating an agent similar to that of
parent, where cloned agent contains the information of parent
agent that it has traversed. A child agent can communicate
either to any one of its parents who are within the range or to
any of its parents at a given level.When first DA is received
by the destination, it records the list of node IDs of entire
route in its reverse route table and DA traces the reverse
© 2013 ACEEE
DOI: 03.LSCS.2013.1.544
P

j 
i 1
…………….
(4)
P
Where, P is the number of nodes on the path ‘j’. If à is
greater than QoS required by the user, it implies the path
satisfies the requirement and QoS packets are transmitted
through that path.As an example consider figure 4, which is
consisting of number of mobile nodes. There exist multiple
paths between source and destination shown with dotted
lines. Destination decides a node disjoint paths from
numerous multiple paths and these multiple node disjoint
paths are shown with solid lines. Upon receiving node disjoint
paths, source AA uses TSFIS to identify a paths which
satisfies multi-constraint QoS shown with solid bidirectional
arrow. One among them with least number of hops is selected
as QoS path to route the packets. The other QoS satisfied
node disjoint paths act as back up paths.
Fig. 4. Fuzzy based node disjoint multi-path QoS routing agency
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Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013
3) QoS Route Maintenance: The proposed scheme uses RA
to maintain QoS path. Whenever node moves or fails, then
RA sends error to the source. AA of the source checks to
find a path from the existing QoS satisfied node disjoint multipaths to reach destination. If not found it initiates new route
discovery.
III. SIMULATION
The proposed FNDMQR scheme is simulated along with
NDMRP in the network scenario using C programming
language to verify the performance and operation
effectiveness. Membership functions and rule bases of the
fuzzy are carefully designed and the output is verified using
Matlab 7.0 fuzzy logic toolbox with FIS editor. Then the inputs
are identified in the library of C code programming. In this
section, we describe the simulation model.
Fig. 5. Packet delivery Ratio vs. Node Speed
the multi-constrained QoS on the path by using TSFIS and
stable path is identified by considering minimum number of
hops.
Average end-to-end delay generated for varying number
of nodes and speed is reported in the figure 6. As the node
speed increases average end to end delay also increases.
The decrease of end-to-end delay in FNDMQR is mainly
presented by selecting a suitable QoS route that results in
reduction of path breakage. Where as NDMRP suffers
frequent link breaks and needs route reconstruction frequently
which results in increase in end-to-end delay.
A. Simulation Model
A mobile ad hoc simulation model consists of N = 80
number of mobile nodes placed randomly within the area of A
X B = 1000 X 1000 m2. A random way point mobility model is
used. Each node randomly selected a position with a speed
ranging from Smin to Smax = 0-10 m/s. A pause time Pautime =
0-10 sec, is assigned for each node. If a node tries to go out
of the boundary, its direction is reversed (Bouncing ball
model). The radio propagation range for each node is selected
as R ran = 250 m and channel capacity is Ch cap = 10 Mbps. Link
delays may vary between Ldmin to Ldmax = 20-50 ms. The
sources and destinations are randomly selected with uniform
probabilities. Residual power of each node varied between
pwrmin to pwrmax = 20- 200 mW. Traffic sources are with
constant bit rate (CBR) with data payload size as Dtpld = 512
bytes. Each simulation is executed for Simtime = 600 seconds.
Simulation was carried out with different QoS requirements.
The following performance metrics are used for
evaluating the proposed scheme.
Packet delivery ratio (PDR): It is the ratio of the number
of data packets delivered to the destination node to the
number of data packets transmitted by the source node. It is
expressed in percentage.
Overall control Overhead: It is defined as the ratio of the
total number of control messages or agents to the total
number of packets generated to perform communication.
Average end-to-end delay: It is defined as the average
time taken to transmit predefined number of packets from
source to destination. It is expressed in seconds.
Fig. 6. Average end-to-end delay vs. Node Speed
Figure 7 shows that the average end to end delay
raisesgradually as the number of source increases. The
reason is that with increasing number of sources, the total
traffic load increases and the network becomes congested.
So, more packets are kept waiting in the queues for long time
which causes the delay to increase. However FNDMQR
outperforms NDMRP in reducing the end-to-end delay.
B. Results
In this section, we discuss various results obtained
through simulation. The results include packet delivery ratio,
overall control overhead, average end-to-end delay. Our
scheme FNDMQR is compared with existing NDMRP.Figure
5 depicts PDR with variation in node speed and number of
nodes. PDR decreases, as node speed, increases in both
FNDMQR and NDMRP because when node speed increases
packets are lost while reconstructing the QoS path. PDR of
FNDMQR is more compared to NDMRP since it accounts
© 2013 ACEEE
DOI: 03.LSCS.2013.1. 544
Fig. 7. Average end-to-end delay vs. No. of Sources
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Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013
Overall control overhead with respect to node speed and
number of nodes are shown in figure 8. As the speed of the
nodes increases control overhead increases. Because of
network connectivity, as the node mobility increases mobile
agents are generated for repairing the path for the QoS
communication.
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Fig. 8. Overall Control Overhead vs. Node Speed
CONCLUSIONS
This paper presented fuzzy based multi-constrained QoS
node disjoint multi-path routing in MANETs by using agents.
Fuzzy rule base is developed to unite the various uncertain
QoS metrics such as available bandwidth, link delay, and
packet loss rate to generate single QoS weight for the node
disjoint paths, which is used for path selection. The results
for our proposed FNDMQR show good packet delivery ratio
and reduction in end-to-end delay and control overhead. The
agent-based architectures provide flexible, adaptable and
asynchronous mechanisms for distributed network
management, and facilitate software reuse and maintenance.
Future work includes optimization of membership function
of fuzzy system according to the user requirement, to support
QoS routing in MANETs
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© 2013 ACEEE
DOI: 03.LSCS.2013.1.544
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