A044030110

N.S.A.Krishnakanth et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 3), April 2014, pp.01-10
www.ijera.com 1 | P a g e
A Hybrid Reliable Data Transmission based on Ant-agent
Resource Allocation Technique in EEMCC Protocol for
MANETS
Dr. M. Rajanbabu1
, N. S. A. Krishnakanth2
, Sampath Kumar .B3
, P. Divya Sri4
,
Professor, ECE Department, Lendi Institute of Engineering and Technology, Jonnada, Vizianagaram, Andhra
Pradesh, India
ECE Department, Lendi Institute of Engineering and Technology, Jonnada, Vizianagaram, Andhra Pradesh,
India
India
India
Abstract
Real time multicast applications in mobile adhoc network brings forward added advantages in wireless network.
The fragile and mobile environment of adhoc network produces the need of bandwidth allocation for real time
applications. Reliability is also an important factor in multicasting in mobile adhoc networks (MANETs), as it
confirms eventual delivery of all the data to all the group members, without enforcing any particular delivery
order in EEMCCP. In the first phase of this paper, we design an „ant agent-resource allocation‟ technique for
reserving bandwidth for real-time multicast applications. In the forward phase, the source sends a forward ant
agent which collects the bandwidth information of intermediate nodes and reserves a bandwidth for real-time
flow for each multicast receiver. In the backward phase, the backward ant confirms the allocation and feeds the
bandwidth information to the source. Normal traffic flows can utilize this bandwidth temporarily until the real-
time traffic starts. When the real-time traffic flow has to be transmitted, the temporary resource which is utilized
by other traffic flows gets dropped and the real-time flow starts In the second phase of this paper, we provide a
hybrid reliable data transmission technique for multicasting in MANET. It uses the advantages of both
Automatic Retransmission Request (ARQ) and Forward Error Correction (FEC) approaches in a controlled
manner to provide a lossless non real time data to the receiver. Our technique has two stages, where in the initial
stage we differentiate the data traffic as real time traffic and non-real time traffic. For both type of traffics, data
is transmitted using the ARQ technique initially. In the second stage, if the traffic is non-real time, it estimates
the total data loss occurred at the receivers for a given time period. If the loss is greater than a threshold value, it
transmits data using FEC technique until the loss becomes less than the threshold. In addition to this, the ant
agents can be used to detect the QoS change, congestion and route breakage and also controls the reliability in a
network.
I. INTRODUCTION
The main features of the ad hoc network are
their speedy deployment and effortless
reconfiguration, which makes them ideal in situations
where installing an infrastructure is too expensive or
too vulnerable.[21] MANETs have applicability in
several areas like Soldiers transferring information
for mission critical situation on the battlefield,
information sharing by business associates at a
meeting; people using laptop computers or other
technical gadgets taking part in an interactive
conference, emergency disaster relief personnel
needs active networks during emergency situations,
personal area and home networking, location-based
services, sensor networks and so on .[20]
Multicast in MANET is a competent way for
handling one-to-many and many to many
communications. Multicasting is projected for the use
of group based computing, where the relationship of
a host group is typically dynamic and vibrant that is,
hosts may connect and disconnect groups at will.
There is no limitation on the location or number of
members in a host group. A host may be a member of
more than one group at a time. At the same time, a
host may or may not have to be a member of a group
to propel packets to the members in the group. The
use of multicasting in MANET showers in the
advantage of flexibility but also bring forth the
challenges like suitable use of nominal bandwidth
and reliability. Thus the effective use of bandwidth
allocation in MANET Multicasting is considered as
RESEARCH ARTICLE OPEN ACCESS

an important factor. The importance of bandwidth
allocation increases with the use multimedia
application. Multimedia applications like audio/video
conferences which are Real-time applications need
much more bandwidth allocation than Best- effort
application like Email and File Transfer Protocol
(FTP). As the case of all wireless environments, radio
links are not complete foolproof and they are affected
by several sources of errors and interference resulting
in a high and variable bit error rate. Consequently,
one of the critical issues of a MANET is its radio
interface. The second one is the mobility of the
nodes. Even then many existing and forthcoming
applications in MANETs require the association of
groups of mobile users. [20]
II. RELATED WORKS
Saida Ziane and Abdelhamid Mellouk [1]
have proposed swarm intelligence based routing
algorithm which are used to improve the resource
allocation of multimedia traffic in MANET. The
approach uses the multi path selection along with the
swarm intelligence techniques to enhance the quality
of service (QoS) for multimedia traffic over the
MANET. The three types of ant agents in the swarm
technique (regular forward ants, uniform forward ants
and backward ants) use the information to identify
the appropriate neighbors. Multiple paths are used,
along appropriate neighbors, to improve the QoS
instead of single path routing.
Ya-Ju Yu et.al [2] has proposed a utility-
oriented (UO) resource allocation algorithm to
exploit the system usage in which the usage is
considered as the user‟s satisfaction. Here,
optimization of radio resource allocation for Layer-
encoded multimedia multicasting (LMM) over the
wireless relay networks (WRN) are discussed and
obtained an algorithm which tries to derive sets of
LMM trees. The algorithm is a dual phase system, in
which, one used to calculate the path using modified
Dijkstra‟s algorithm. The other is a dynamic resource
allocation phase using an incentive based technique
to allocate maximized system utility.
De-Nian Yang and Ming-Syan Chen [3]
have proposed a mechanism for the reduction of total
bandwidth cost of the IP multicast tree by suitably
selecting the cell and the wireless technology for
every mobile host. The scheme is based on an
algorithm of Lagrangean relaxation and introduces a
distributed protocol which is based on the modified
algorithm the modified algorithm produces the
advantage of using dynamic group membership and
mobility of members, no modification on the current
IP multicast routing protocols. The algorithm
provides reduction in total bandwidth cost of the
shortest path tree as it searches the somewhat inferior
solutions to evade trapping in locally optimal
solutions.
A. Sabari and K. Duraiswamy [4] address
the problem of traffic engineering multicast which
optimizes many objectives like distance, delay and
bandwidth concurrently. Here an Ant Based
Multicast Routing (AMR) algorithm for multicast
routing in mobile ad hoc networks has been
introduced to resolve the Traffic Engineering
Multicast problem. In addition to the existing factors,
the algorithm estimates an additional factor in the
costs metric that is calculated as the product of
average-delay and the maximum depth of the
multicast tree. Thus tries to minimize this combined
cost metric.
Juan Liu et.al [5] have address the User
Grouping and Bandwidth Allocation (UGBA)
strategy in wireless multicast systems. The UGBA is
used when there is a need of fixed bandwidth
becomes at most important and at the same time the
users are distributed uniformly. Here the two factors,
fixed bandwidth allocation and the data transmission
rates, are considered to provide a guideline to
scheduling transmissions in wireless multicast
systems. The problem of UGBA is handled by using
a Signomial Programming (SGP) method which finds
the suboptimal solution to any non-convex problem.
Loc Bui et.al. [6] have addressed the
concept of shadow queues and proposed a shadow
algorithm which tries to achieve the optimal solution
for multi-rate multicast. Here the author introduces
the techniques of „shadow traffic‟ which are
generated by the receivers and „moving back‟ to the
sources, and the corresponding „shadow‟ (token)
queues. The transmission of shadow traffic in the
reverse direction then estimates the real traffic
generation (at the sources) and its transmission
through the network. In the network, setting up of the
shadow traffic is initiated by the back-pressure-type
algorithm nevertheless; the algorithm used is a non-
standard back-pressure algorithm.
Kumar Manoj et.al [7] have proposed an
algorithm, bandwidth control management (BWCM)
model, to estimate bandwidth calculation and slot
reservation for multimedia ad hoc wireless network
The algorithm consists a group of mechanisms which
are; control management used to calculates the
bandwidth, co-ordination that arranges the allocation
of the bandwidth, temporary resource reservation
used to release the connection link or bandwidth after
the completion of the communication. The model
tries to improve the QoS performance in multicast
communication and tackle its challenges like,unstable
node topology and frequent failures, by minimized
end-to-end delay.
In our previous paper [8], we have proposed
an energy efficient and reliable congestion control

(EERCCP) protocol for multicasting in mobile adhoc
networks. Our algorithm tries to overcome the
disadvantages of existing multicast congestion
control protocols which depend on individual
receivers to detect congestion and tries to adjust their
receiving rates. Our protocol consists of three phases;
Emy E. Egbogah et.al. [9] have proposed a
reliable routing protocol named Scalable Team
Oriented Reliable Multicast (STORM). STORM
combines individual nodes with comparable mobility
patterns and speeds into teams, and builds hierarchy-
based multicasts mesh structure among elected team
nodes. A Unicast Acknowledgement Scheme (UAS)
is developed to construct the routing structure in an
efficient manner. To improve the reliability of
STORM, a modified version of Reliable Adaptive
Congestion controlled multicasT (ReACT) is used as
a reliable transport protocol. It offers scalability as
the network size, multicast groups and total number
of multicast group member‟s increase as well as
creating and propagating control packets with reliable
delivery and low memory consumption.
Bo Rong et.al. [10] have proposed a new
hybrid error control scheme that combines
interleaving, forward error correction (FEC), and
threshold based ARQ to mitigate the error and loss
effects encountered in MANETs. In particular, the
threshold based ARQ is studied to shorten the
transmission delay in reliable multicast. In order to
work compatibly with a verity of MANET multicast
routing protocols, this new scheme is based on
Client/Server architecture which resides on the top of
UDP layer. Moreover, they used specification and
description language (SDL) to formally portray the
hybrid error control scheme from a broad overview
down to detailed design levels.
Mehdi EffatParvar et.al.[11] have proposed
a reliable multicast algorithm with local recovery
approach. By using the proposed algorithm, nodes
can join to multicast group in minimum time and data
delivery can be increased. The algorithm tries to
accomplish fast recovery during any route breakage,
so that the destination can connect to source in new
route or in the same route.
Dimitrios Koutsonikolas and Y. Charlie Hu
[12] have examined FEC‟s efficiency in wireless
network by implementing four reliable schemes
initially proposed for wired networks on top of On
Demand Multicast Routing Protocol (ODMRP). They
proved that pure FEC can offer significant
improvements in terms of reliability, increasing
Packet Delivery Ratio up to 100% in many cases, but
it can be very inefficient regarding the number of
redundant packets it transmits. Moreover, a carefully
designed hybrid protocol, such as RMDP, can
maintain higher reliability while improving the
efficiency compared to a pure FEC scheme.
Erik M. Ferragut [13] has proposed a new
erasure code as a solution to the dynamic erasure
code problem. The dynamic erasure code problem is
to extend the digital fountains concept to a message
generator, simultaneously with the transmission (i.e.,
live data). Solution of this problem provides a means
for robust multicasting or one-way transmission of
live data on a computer network. It also gives a
method for robust distributed storage of log data, or
other serially generated data.
Ali Alsaih and Tariq Alahdal [14] have
proposed a reliable multicast transport protocol over
combined networks using sub sub-casting called
RMSS. It is based on a hierarchal structure where
receivers are grouped into local regions. In each local
region there are special receivers, which are called
designated receivers and mobile agents. Each of the
receivers is responsible for retransmission of
requested packets to the receivers which are in their
local region. Here a sub sub-casting is used to
retransmit the data only to the requested receivers.
In our previous paper [14], we have
proposed an energy efficient and reliable congestion
control (EERCCP) protocol for multicasting in
mobile adhoc networks. Our algorithm tries to
overcome the disadvantages of existing multicast
congestion control protocols which depend on
individual receivers to detect congestion and tries to
adjust their receiving rates. Our protocol consists of
three phases;
First phase - Builds a multicast tree routed at the
source, by including the nodes with higher residual
energy towards the receivers.
Second phase- An admission control scheme,
depending on the output queue size, to analyze flow
is admission or rejection
Third phase- Adjusts the multicast traffic rate at each
bottleneck of a multicast tree.
III. PROPOSED SCHEME
A. Ant Agent-Reservation
In this paper, we use an „ant agent-
reservation‟ technique for reserving bandwidth for
real-time application when a new multicast receiver
is joined. Our technique has two phases; forward
phase and backward phase. In the forward phase, the
source sends a Forward Ant Agent (FAA) which
collects the bandwidth information of intermediate
nodes. In the backward phase, the Backward Ant
Agent (BAA) confirms the allocation and feeds the
bandwidth information to the source along with
reserving a bandwidth for real-time flow for each
backward nodes. Before we discuss about the two
phases, we analyze how the ant agents work.

B. Ant agents
Ant agents are generated at the source and
are probabilistically being sent to the destination
passing through the intermediate nodes. Each node in
this network possesses two tables; a private table
(PRT) and a public table (PUT) [22]. The PUT
provides each FAA to choose its next hop. In PUT
pheromone values of neighboring nodes are stored.
These values decide the ant agent to select the next
node. The probability value Pij to choose the best
neighboring node j from a source node i is given by;



Nl
l
y
l
x
j
y
j
x
RT
RT
Pi
p
p
j
][][
][[ ]
(1)
The probability value to choose the best
neighbors among N nodes is done by using
pheromone trail value Tp and a Trail-and-error
method Rj. X and Y are particular weight value for
Tp ( optimal value for X and Y are 1 and 2
respectively). The Rj values are calculated by taking
the network interface Queue length (Qj) along the
outgoing link. The value of Qj is carried on using
traffic state and a quantitative measure associated
with the queue waiting time. Thus the value of Rj is
obtained as;



Nl
l
j
Q
Q
1jR (2)
The private table (PRT) contains the average
time and variance values along with the bandwidth
information of the node for each destination to which
the forward ant has been previously sent. The average
time and variance values from PRT are used to
calculate the pheromone value. The pheromone value
is updated regularly using an incentive based scheme
in which every node which successfully transmits the
ant agent is awarded an incentive, whereas the other
nodes are not updated. This helps in finding the best
node, as higher the value, provides higher strengthen
link. The pheromone values are calculated as stated
below;
PH=PH+ Inc (3)
Where, PH is the weight value of the pheromone,
Inc is the increment value. The increment value is
calculated by the trip time Tt and average trip time 
. The value of s, which is the scaling factor, is usually
kept as 2. The value is given by;
s
T
Inc
t
 if
s
Tt
<1 , where s > 1
= 1 , otherwise (4)
The value of PUT determines the neighbor
nodes and contains the public values to determine the
next hope. The values of PRT are the private values
between a particular source and receivers.
C. Forwarding Phase
In the previous section 3.1.1, we discuss the
ant agent moving from source to destination. Here we
determine the bandwidth estimation using FAA to
calculate the available bandwidth of each node along
the path. The FAA contains a BW_INFO packet
which carries the bandwidth information. It has
source IP address (SA), destination IP address (DA),
message type (MT), flow ID and requested data rate
field (RDR).
Fig. 1 BW_INFO packet
The SA and DA consist of the source and
destination ID‟s respectively, MT denotes the
message type which has to be sent in the flow and
flow ID denotes the flow type to be transmitted. RDR
is the field where the requested bandwidth
information is stored. During the forwarding phase in
each hop, the FAA checks the available bandwidth
(given by eq.5) of the node with the value stored in
the RDR field. If the values are equal or more than
the RDR value, the next hop is attained without any
modification in the field. In the same case, if the
available bandwidth is less than the threshold value,
the node modifies the RDR value with the available
bandwidth in the node.
3.1.3 Backward Phase
After reaching the destination, the FAA is
modified as Backward Ant Agent (BAA) by
transferring all its fields and is sent back to the
destination along the same trailing path. During the
backward phase, the BAA revisits the nodes and
checks for the available bandwidth again. If the node
has available bandwidth (eq 5) less than the RDR
values, the RDR values are modified to lesser values.
If the value is greater than the RDR field value,
bandwidth reservation is carried on. To determine the
congestion and link activity as well as to avoid
improper admission, we use the PRT values. The
BAA provides this information to source node by the
BW_INFO packet. The values obtained in the RDR
field determine the threshold value for the bandwidth
to transmit data (Data Rate). Thus for all real time
traffic, a threshold bandwidth value is determined for
transmission.
3.1.4 Transmitting Real-Time Traffic
In MANET, the link capacity is fragile due
to node mobility and other time varying situations.
The available bandwidth is determined by the total
rate value assigned to the link (TR) and the link
capacity (LC) value. We can determine TR values by
measuring the traffic values taken from PRT and the
SA DA MT FLOW ID RDR

LC value is determined by calculating throughput
value TH of a single packet transmission [17].
TH =
HT
S
(5)
Where, S is the packet size and HT is the halt time of
each packet which is the difference between time at
which ACK is received (Tack) and time for a packet
transmitted (Ttran). Thus is HT is given by,
HT=Tack-Ttran (6).
The available bandwidth is compared with RDR
value to determine the threshold bandwidth to be
allocated. With respect to TR and LC, we can
determine the available bandwidth (ABW) by;
ABW=LC+TR (7)
The BAA value holds the threshold
bandwidth value which is reserved for the Real-time
flows. Normal traffic flows can utilize this bandwidth
temporarily until the real-time traffic starts. When the
real-time traffic flow has to be transmitted, the
temporary resource which is utilized by other traffic
flows gets dropped and the real-time flow starts.
Our scheme also checks for QoS changes,
congestion as well as route breakages. We take into
account the three scenarios;
Scenario 1: QoS changes
Our scheme calculates the available
bandwidth (ABW) and feeds the information through
BW_INFO packet. On Periodical updating of ABW,
if it decreases below to a minimum threshold level, it
indicates insufficient capacity of that node. The ant
agents send notification to the source to either change
the traffic through another path or waits for the path
to be restored. This helps in maintaining a better
Quality of Service (QoS) through out the network.
Scenario 2: Congestion Control
The ant agents determine the queue length in
each node during the periodical visits. If the queue
length exceeds its maximum value, then it indicates
congestion. This information is passed on to the
source by the agents and apparently the source
reduces the rate to avoid congestion.
Scenario 3: Route Breakage
The ant agents are periodically sent to
analyze the ABW value throughout the network. If
the ant agents do not reach the source in the allotted
time period the route is considered disconnected.
During this time, the source discards the route and
tries to establish another failure free path using the
ant agents.
3.2 PROPOSED SCHEME
In section 1.4, we have discussed Reliable
multicast in MANET and the various protocols used.
We have discussed above (section 2) various recent
works related to the different protocols used in
reliable multicast like the ARQ, Gossip or FEC
based. These protocols have there own merits and
demerits when used. [15] have proposed a hybrid
method called Reliable Multicast data Distribution
Protocols (RMDP) which uses the FEC encoding to
improve the behaviour of the protocol in presence of
large groups of receivers, and to reduce the amount
of feedback from receivers. ARQ is used to deal with
those cases where the default amount of redundancy
does not suffice to complete reception. The RMDP
method identifies the drawbacks of both FEC and
ARQ method and uses the advantage of the two
protocols in order to overcome the drawbacks.
The major drawback of using ARQ single
handedly is that it scales very badly to large sets of
receivers as well as scalability problems also exist in
handling feedback from the receivers. In the same
way, FEC is computationally expensive, since the
entire data stream must be processed to produce the
encoded packets, each one conveying information on
a number (possibly as large as k) of source data
packets. As in of [15] hybrid method maintains a
balance between both the ARQ as well as FEC. The
use of FEC techniques to drastically reduce the
impact of independent losses for different receivers,
which make ARQ-based protocols perform very
poorly as the number of receivers grows. The
protocol is well-suited to the use with mobile
equipment because of its simplicity, robustness to
losses, moderate demand for feedback, and
scalability.
In our work, as like [15], we introduce a
hybrid method of ARQ and FEC. Our method is a
two phase technique, where in the initial step we
differentiate the data services among the real time
data services and non-real time data services. If the
data service is a non-real time data services, then the
next phase is executed. We use our concept of hybrid
method in accordance to the data loss. In general, the
service in default uses ARQ method to send data but
if there exists an excessive data loss then the system
changes over to FEC to send data.
3.1 Phase – 1
In the initial phase, we determine the data
services available. We classify the data services into
two major groups; the real time services and the non-
real time services. The real time data services are
basically those information/data which are delivered
immediately after collection. There is no delay in the
timeliness of the information provided. These are
often used for navigation or tracking. [16] These data
needs to be sent to the receiver without any time
delay even there exist a minimal loss. Therefore we
can compromise the losses but the time lagging can
not be compromised in the case of real time data

services. Similarly the other data services are termed
as non real-time services. In these services of non
real-time data, the time lagging factors provide less
importance but the losses in these service plays a
major role.
We consider the two factors of data loss and
time lagging of both the services and detect between
the two services. As real time data services are less
prone to data losses, the information is sent in ARQ
process. But in the case of non-real time services, the
data loss plays a major role. So we cannot take the
ARQ services in the non real-time services, if the
losses are high. Thus we detect the losses and if the
losses are higher than a threshold level, we shift the
services from ARQ to FEC. We discuss this issue in
the next section.
3.2 Phase - 2
As discussed above, the default services for
sending the data, we consider the ARQ services. But
when a non real time data is sent, we periodically
determine the losses caused by the ARQ services. If
the services cause a higher data loss (above a certain
threshold level) in a particular time period, the
default ARQ services is changed into an FEC
services.
Consider a period  in which the losses are
determined for a non real time data. We analyze a
data drop rate (DL) in each period  . The probability
of data loss of DL along with the number of multicast
receiver (r) is given as;
P(DL,r)=1-(1-DL)r
(8)
Where,
DL =
periodTime
droppedpacketofNumber
(9)
The above equation state two factors;
 Increase in data drop increases the
probability of data loss.
 Increase in receivers along with data drop
evolves a higher data loss.
Thus when a probability of losses increases due to
either data loss or due to increase in receivers and
cross a particular threshold level (P(DL, r)th), the
source get informed. The source then changes the
ARQ service and adopts FEC services (We evaluate
the use of FEC in the next section.). The FEC service
is sent throughout the section (till the next sets of
data are sent). After the complete of section, the
default ARQ services are resumed again. If the
probability of the threshold level does not reduce, the
FEC service is again resumed or else the ARQ
service gets maintained.
3.3 FEC Service
FEC or Forward error correction is a system
of error control for data transmission, whereby the
sender adds carefully selected redundant data to its
messages, also known as an error-correction code.
Here we use Luby transform (LT) coding for the FEC
service. LT codes are the first class of practical
fountain codes that are near optimal erasure
correcting codes which employing a particularly
simple algorithm based on the exclusive or operation
( ) to encode and decode the message. [17]
The LT Coding algorithm [18] produces a
virtually unlimited number of encoded blocks from
some k original data blocks via logical XOR
operations. The k original data blocks are obtained by
partitioning the original data into k uniform segments
and the creation of each encoded block, or “symbol”,
will require O(ln(

k
)) logical operations on the
original blocks. To decode the original data with a 1-
 chance of success, any k+O( k ln
2
(

k
))
encoded blocks should be sufficient.
The encoding process is relatively straight forward.
1. Choose some degree d for the next encoded
block according to the Robust Soliton
Distribution
2. Randomly choose d different original data blocks
and XOR them together to produce the encoded
block.
3. Repeat steps 1 and 2 until the desired number of
encoded blocks have been produced.
It should be noted that as each encoded
symbol is produced, the identities of its sources must
be stored as meta-data for the decoding process.
The process of decoding the data is as follows:
1. When an encoded block is received, XOR it
with all of its neighbors in the bipartite graph
which have been recovered, and remove the
edges that join the XORed nodes.
2. If the encoded block has only one remaining
neighbor, then part of the original data has been
recovered. Copy its data to its sole neighbor and
place that data node in a queue of original
nodes to process.
3. While the queue is not empty, choose a data
node from the queue. XOR each received
neighbor‟s data with the data in the original
node and disconnect the nodes. For each
neighbor that is XORed, perform step 2.
4. Continue receiving and processing encoded
blocks until the original data has been
completely recovered.
Thus our technique of hybrid usage of ARQ
and FEC cumulatively produces a reduces loss based
scheme which helps the non real time data to
maintain loss free even if the number of receivers are
increased. This increases the scalability of the
network and avoids time-waste for redundancy.

Algorithm
Consider an incoming traffic flow at  =1, where 
is a given period
1. If the flow is real-time, then
1.1 flows are transmitted using ARQ
end if
2. if flow is non-real time, then
2.1 Flow are transmitted using ARQ
2.2 determine probability of data loss, P(DL, r)
2.3 If P(DL, r) > P(DL, r)th , then
2.3.1 Flow are transmitted using FEC
2.3.2 After FEC session complete, repeat
from 1.
Else
2.3.3 Continue the transmission using ARQ
End if
End if
3.  =  + 1 ,
4. Repeat from 1
IV. SIMULATION RESULTS
4.1 Simulation Model and Parameters
We use NS2 [19] to simulate our proposed
technique. The proposed hybrid reliable data
transmission (HRDT) technique is applied in our
previous multicast routing protocol EERCCP [15]. In
our simulation, the channel capacity of mobile hosts
is set to the same value: 2 Mbps. We use the
distributed coordination function (DCF) of IEEE
802.11 for wireless LANs as the MAC layer protocol.
It has the functionality to notify the network layer
about link breakage.
In our simulation, 50 mobile nodes move in
a 1000 meter x 1000 meter region for 50 seconds
simulation time. We assume each node moves
independently with the same average speed. All
nodes have the same transmission range of 250
meters. In our simulation, the speed of the mobile is 5
m/s. The simulated traffic is Constant Bit Rate
(CBR).
Our simulation settings and parameters are
summarized in table 1
No. of Nodes 50
Area Size 1000 X 1000
Mac 802.11
Radio Range 250m
Simulation Time 50 sec
Traffic Source CBR and VBR
Rate 0.5,1,1.5 and 2Mb
Mobility Model Random Way Point
Speed 5m/s
Receivers 5,10,…25
Pause time 5 s
Transmit Power 0.660 w
Receiving Power 0.395 w
Idle Power 0.335 w
Initial Energy 3.1 J
TABLE1: SIMULATION PARAMETERS
4.2. Performance Metrics
We compare our proposed ARAT protocol with
the EERCCP [18] protocol. We evaluate mainly the
performance according to the following metrics.
Average end-to-end delay: The end-to-end-delay
is averaged over all surviving data packets from the
sources to the destinations.
Average Packet Delivery Ratio: It is the ratio of
the No. of packets received successfully and the total
no. of packets sent.
Throughput: It is the number of packets received
by all the nodes in the network.
4.3. Results
In this experiment, we vary the rate as 100,
200…..500Kb.
Fig 1: Rate Vs Delay
Fig 2: Rate Vs Bandwidth Received
Fig 3: Rate Vs DelRatio
0
0.005
0.01
0.015
0.02
0.025
100 200 300 400 500
Delay
Rate
Rate Vs Delay
ARA
0
0.2
0.4
0.6
0.8
1
100 200 300 400 500
Bandwidth(Mb/s)
Rate
Rate Vs BandwidthReceived
ARA
EEMC
CP
0
0.2
0.4
0.6
0.8
1
1.2
100 200 300 400 500
DelRatio
Rate
Rate Vs DelRatio
ARA
EERC
CP

Fig 4: Rate Vs Throughput
Figure 1 show the end-to-end delay occurred
for both ARAT and EERCCP. As we can see from
the figure, the delay is less for ARAT, when
compared to EERCCP.
Figure 2 shows the total bandwidth received
for both ARAT and EERCCP. As we can see from
the figure, the received bandwidth is high for ARAT,
when compared to EERCCP.
Figure 3 shows the delivery ratio for both
ARAT and EERCCP. As we can see from the figure,
the delivery ratio is high for ARAT, when compared
to EERCCP.
Figure 4 shows the throughput occurred for
both the cases. As we can see from the figure, the
throughput is high for ARAT, when compared to
EERCCP.
4.2 Performance Metrics
We compare our (HRDT) technique with
existing multicast AODV [18] and RMDP [16]. We
evaluate mainly the performance according to the
following metrics.
Average end-to-end Delay: The end-to-end-delay is
averaged over all surviving data packets from the
sources to the destination.
Average Packet Delivery Ratio: It is the ratio of the
No. of packets received successfully and the total no.
of packets sent.
Average Energy Consumption: The average energy
consumed by the nodes in receiving and sending the
packets are measured.
Control Overhead: The control overhead is defined
as the total number of routing control packets
normalized by the total number of received data
packets
4.3 Results
A. Varying the Receivers
In this experiment, we vary the group size or
the number of receivers per group as 5,10…..25.
Figure 1: Receivers Vs Delay
Figure 2: Receivers Vs Delivery Ratio
Figure 3: Receivers Vs Energy
Figure 4: Receivers Vs Overhead
When the number of receivers is increased,
we observe the following results.
Figure1 shows the end-to-end delay
occurred for both HRDT and RMDP. As we can see
from the figure, the delay is significantly less for
HRDT, when compared to RMDP. This is because,
0
2000
4000
6000
8000
100 200 300 400 500
Throughput
Rate
Rate Vs Throughput
ARA
Receivers Vs Delay
0
1
2
3
4
5
5 10 15 20 25
Receivers
Delay
RMDP
HRDT
Receivers Vs DelRatio
0
0.2
0.4
0.6
0.8
1
5 10 15 20 25
Receivers
DelRatio
RMDP
HRDT
MAODV
Receivers Vs Energy
0
0.5
1
1.5
2
2.5
3
5 10 15 20 25
Receivers
Energy
RMDP
HRDT
MAODV
Receivers Vs Overhead
0
500
1000
1500
2000
5 10 15 20 25
Receivers
Overhead(pkts.)
RMDP
HRDT

RMDP is completely FEC based resulting in high
delay for encoding and decoding.
Figure2 shows the packet delivery ratio for
HRDT, RMDP and MAODV. As we can see from
the figure, the delivery ratio is initially less for
HRDT than RMDP for the receivers 5,10 and 15,
since ARQ suffer from poor performance, when the
receivers are increased. But when the receivers are
more than 15, it changes to FEC mode, resulting in
more delivery ratio than RMDP. Since MAODV does
not involve any error recovery features, it has the
least delivery ratio
Figure3 shows the energy consumption for
HRDT, RMDP and MAODV. The energy
consumption is more for RMDP compared to HRDT
and MAODV, since FEC requires more energy for
encoding and decoding.
Figure4 gives the overhead occurred for
both HRDT and RMDP. Cleary the overhead is less
in HRDT than RMDP. This is due to the fact that
HRDT adaptively changes to FEC, when the
receivers are more.
V. CONCLUSIONS
The ARA technique is developed for
reserving the bandwidth for real-time applications in
MANETs, which contains two ant agents: FAA and
BAA. It probes the bandwidth and reserves the
necessary bandwidth needed for the real time
applications. When the real-time traffic flow has to
be transmitted, the temporary resource which is
utilized by other traffic flows gets dropped and the
real-time flow starts. The scheme also detects the
QoS changes and route breakage and performs
congestion control by periodical monitoring of ant
agents. To provide lossless real time data to the
receiver, a Hybrid Reliable Data Transmission
(HRDT) technique is developed with Automatic
Retransmission reQuest (ARQ) and Forward Error
Correction (FEC) features. The HRDT is used in
conjunction with the ARA based EEMCC protocol.
Among the two traffic services, the non real time data
traffic need to be have a lower loss even if there exist
a delay and the real-time traffic need minimum delay
irrespective of the losses. Since ARQ involves less
delay and overhead, the real-time data is transmitted
completely using the ARQ technique. But for the non
real-time data, the total data loss occurred at the
receivers is estimated for a give time period. If the
loss is greater than required threshold value, it
transmits data using FEC technique since FEC
achieves more reliability than ARQ. Once the loss
becomes less than the required threshold, again the
data is transmitted using ARQ.Thus the proposed
scheme not only controls the reliability in a network
but also the overhead and scalability issues of the
existing ARQ and FEC techniques. Therefore, the
proposed Hybrid Reliable Data Transmission based
on Ant-agent Resource Allocation Technique in
EEMCC Protocol for MANETS gives reliable data
transformation.
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