Congestion Control Strategy In Efficient Transit Routing Protocol With Multiple
Traffic
Jay Prakash1, Rakesh Kumar2, J.P.Saini2, Amrendra Singh Yadav2
Department of Computer Science & Engg.
Madan Mohan Malaviya University of Technology
Gorakhpur (U.P.), India
jpr_1998@yahoo.com1,rkiitr@gmail.com2,jps_uptu@rediffmail.com2
Yadavamrendrasingh@gmail.com2
Abstract- Mobile Ad Hoc Networks (MANETs) to a
wired backbone Internet access network. This paper
demonstrates that a wired backbone network can be
utilized for more than just providing access to the
global Internet. Network coding, the notion of
performing coding operations on the contents of
packets while in transit through the network, was
originally developed for wired networks; recently,
however, it has been also applied with success also to
mobile ad hoc networks (MANET). Traffic between
mobile nodes in the ad hoc network may also be routed
via this backbone network to achieve higher
throughput, and to reduce the load in the ad hoc
network. This is referred to as transit routing. This
paper proposes a cost metric algorithm that facilitates
transit routing for some of the traffic flows between
nodes in the MANET. The algorithm aims at carrying
out transit routing for a flow only when it leads to
improvements of the performance.
wired backbone subnet [4],[5], consisting of (i) one or
multiple gateways (GWs), and (ii) a number of attached
Access points (APs) (also called base stations). These
nodes are interconnected by high capacity wired links,
forming a backbone subnet.
Thus there are 3 types of nodes in the network:

Gateway nodes are routers that have one or
multiple links directly connected to the global
Internet or external networks. These nodes are the
main entrances into the global Internet or external
networks. In frameworks where there is a
backbone subnet, these GW nodes are usually
equipped with wired interfaces only.

Access Points nodes are bridges or routers that
have both wired and wireless interfaces. They are
thus located on the boundary between the wired
backbone and the wireless ad hoc subnet. They
do not have links directly to the global Internet,
but they can reach the Internet through the GWs.
Mobile nodes that require Internet access will
have their traffic forwarded via one of these APs.

Mobile nodes are hosts or routers that usually are
equipped with one single wireless interface. They
are located inside the ad hoc network. While
GWs and APs are static nodes, MNs are mobile,
and can freely move from one location to another
within the MANET.
Keywords- MANET, Gateway, OLSR, Cost metric
routing.
1. Introduction
Connectivity between Mobile Ad Hoc Networks
(MANETs) and the global Internet or other external
Networks. Military communications are often groupcentric, which means that the exchange of information,
such as voice and data, is typically among a subset (e.g. a
squadron) of the total deployed set of forces. Group
communications, often called multicast, require enabling a
set of resources that provide edges and paths among the
nodes in the multi cast group. One example is found in the
currently on-going research project, ITEA Easy Wireless
[6]. Such connectivity increases the usefulness of
MANETs in many user scenarios. The project proposes to
connect a MANET to external networks and the global
Internet. This network architecture forms a base
communication system for emergency service personnel,
governing the fire department, the police department, and
emergency medical services. It is also expected that this
architecture is applicable to military operation scenarios.
A number of proposals related to Internet
connectivity for MANETs have been published lately.
Common to many of these proposals is the existence of a
Only the traffic between the MANET and the global
Internet is routed via the backbone subnet. Traffic between
two MNs in the MANET, on the other hand, is routed
solely over the wireless links with in the MANET. A
wired backbone subnet is to provide Internet access to
mobile nodes (MNs) belonging to an associated mobile ad
hoc subnet. The wired backbone subnet can also be used
for transit routing, meaning that it is used for the
communication between two MNs in the MANET. This
feature has the potential of improving the overall
performance, since the wired backbone subnet is much
more reliable and possesses higher bandwidth compared to
the wireless ad hoc subnet. Other advantages of transit
routing are:






Wireless communication over many hops is often
difficult in ad hoc networks, since the throughput
is rapidly decreasing with the number of
successive hops.
In addition, the probability of unsuccessful
transmission is an increasing function with the
number of hops.
By routing via the wired backbone subnet, the
traffic load on the wireless medium may be
reduced.
Possibility to achieve higher end to end
throughput since the wired backbone subnet has
usually much higher bandwidth than wireless
links.
By the aid of the wired backbone subnet, it may
be easier to maintain a more stable traffic stream
between MNs separated by many hops.
Greater probability for successful transmissions,
since wired links are much more reliable
compared to wireless links.
higher degree of robustness and reliability because of the
redundancy. If one GW encounters failure, there is another
GW that can still provide Internet connectivity for MNs in
the network. In order to obtain an understanding of
situations in which it is beneficial or not to favor the
alternative transit routing, we have constructed a reference
topology as a case study. We argue that a reference
topology should allow for multiple GWs in the backbone
access network, since with more GWs, there will be more
bandwidth for Internet traffic. Furthermore, with multiple
GWs, traffic may be load balanced between these GWs. If
one GW encounters failure, there is another GW that can
still provide Internet connectivity for MNs in the network.
However, these issues are not the main focus of this paper,
instead we focus on the derivation of the cost metric
routing algorithm that can make transit routing for intranet
traffic possible in order to optimize throughput.
Hence, transit routing for intranet traffic in our
scenario, which in many cases are not the shortest path, is
not possible without some modifications to the cost metric
used by these protocols. The proposed optimized cost
metric algorithm is designed for this purpose, namely to
make transit routing through the backbone subnet possible
when appropriate. Most MANET routing protocols today,
such as OLSR [7], AODV [8], DSR [9] and TBRPF [10]
etc., utilize a hop count metric in the calculation of the
routing table. These protocols are commonly referred to as
shortest path protocols, and imply that the shortest path (in
terms of hops) to a desired destination node is always
preferred, no matter what the characteristic of that path is.
This means that when there is a performance gain in terms
of throughput by using the alternative path through the
backbone subnet, the cost metric algorithm will favor this
path
2. COST METRIC ROUTING FOR INTRANET
TRAFFIC
With the feature of transit routing for intranet
traffic higher performance may be achieved in terms of
throughput and reduced load in the ad ho subnet. This is
however not always true. The challenge here is to identify
those cases when there is a gain to route traffic through the
wired backbone subnet, and cases when it is better to just
let the traffic be routed along the original path within the
ad hoc subnet. There are certainly situations when routing
over the wired subnet may result in worse performance.
2.1. Reference Topology
This topology is shown in Fig. 1, and is mainly
based on the works in [4], [5]. The difference in our
topology and the topologies proposed in these works is the
use of multiple gateways. Moreover, the network has a
Fig 1. Reference topology
The reference topology shown in Fig. 1 consists
of two subnets, i.e. (i) the wired backbone subnet and (ii)
the wireless ad hoc subnet. GWs and APs are located in
the wired subnet, while MNs are located in the ad hoc
subnet. As before, the primary task of nodes in the wired
backbone subnet is to provide Internet connectivity to
MNs in the ad hoc subnet, while the secondary task is to
enhance throughput performance for intranet traffic
between mobile nodes. Furthermore, the APs, in addition
of being access points into the wired backbone subnet,
have the important role to extend the access coverage area.
In our study, we assume that all nodes in the network, i.e.
GWs, APs and MNs, are running the same MANET
routing protocol OLSR. The benefit of this choice is that
micro mobility within the network is naturally handled by
the OSLR routing protocol.
2.2 Reference Throughput One key characteristic
of multi hop wireless networks is the decreased throughput
as the number of hops between source and destination is
increased. To verify this, we used the string topology as
shown in Fig. 2 to determine the relationship between max
throughput and the hop count.
Fig. 2. String topology
2.3 Enhancement in Throughput
The throughput was simulated for both the wired path and
the ad hoc path, and then compared. In Table 1 we list the
source and destination pairs in which the simulation result
shows a higher throughput for the wired path compared to
the ad hoc path. The table also shows the number of
wireless hops and the corresponding throughput (kbps) for
both the wired path and the ad hoc path. The first wireless
section has m successive wireless hops, while the second
wireless section has n successive wireless hops. Here we
have for convenience introduced the notation “m+n”,
with the understanding that this is a wired path, and it
consists of two distinct wireless sections.
The last column of the table shows the
improvements (in percent) in throughput of the wired path
compared to the ad hoc path. However, because of the
symmetry in the topology, the remaining combinations of
source and destination pairs may be derived from the listed
subset.
Table 1. Source-destination pairs with improved
throughput using the wired path.
2.4. The Cost Metric Routing Algorithm
The cost metric algorithm is a supplement to
existing MANET routing protocols such as OLSR, to
make transit routing for intranet traffic possible. The aim
of this scheme is to optimize the throughput of intranet
traffic. The cost metric algorithm is a supplement to
existing MANET routing protocols such as OLSR, to
make transit routing for intranet traffic possible. The aim
of this scheme is to optimize the throughput of intranet
traffic.
The general form of the cost metric algorithm
may be written as shown below:
if ((m+n)-k > g) #g= 0, 1,2
ad_hoc_path
else
if (Cii < Ci)
wired_path
else
ad_hoc_path
Here, Ci and Cii are the total cost for the ad hoc path and
the wired path, respectively. The parameter g is a pre
configurable parameter that determines the “greediness”
of the algorithm. For example, if we want to limit transit
routing only to cases where “m+n” ≤ k+1, then g is set
to 1.
3. Proposed work
In a more realistic situation, multiple flow of
traffic are likely to exist simultaneously in the network,
due to which a considerable congestion on a particular
route (in internet backbone) would be occur if all nodes
are likely to route the data through the same path Since
this paper [1] did not consider any scenario that describes
this set of problem so, by considering a suitable load
balancing strategy we can get rid of that much congestion
occur it the internet backbone due to the simultaneously
multiple traffic flow. One possible way to solve this is to
incorporate some mechanisms for bandwidth monitoring
or estimations, such as the ETX [2] or ETT [3]. With the
additional information on the traffic load distribution in
the network that these extensions can provide, we will be
able to derive an improved cost metric algorithm that also
accounts for multiple traffic flows. Transit routing was
enabled only in suitable situations that in most cases
resulted in an improved performance in terms of
throughput. We have proposed a simple and effective cost
metric algorithm that can be added to existing routing
protocols and thereby making transit routing and higher
throughput for intranet traffic possible.
Now, our focus is to control the congestion
occurred in the
network. Since the gateways get
overwhelmed whenever multiple flow exists. Here we use
a close loop technique (back pressure) to control the
congestion. We will use the back pressure at the gateway
which is congested. Back-pressure is used to slow down
the source. In our scenario the node very nearer to the
gateway is act as a source. Once the source node get slowdown then its receiving queue get flooded and further if no
any traffic re-assignment process is used at the source
node then it will generate another problem.
one source may comprise node i at the same time, the
frequency isn’t equal to the number of sources used.
A path in WSN comprises nodes 1,2,...,i,...,m, denoted as
P={1,2... i,...m} . Parameters related it comprises the
utilized ratio of buffers, load factor and utilized frequency.
By considering all above parameters we will select another
path within MANET to reassign the traffic
4. SIMULATION & ANALYSIS
This proposed protocol is simulated using NS2. In this the
mobile host’s channel capacity is set as 2 Mbps. The used
MAC layer protocol is distributed coordination function
(DCF) of IEEE 802.11 in this simulation, 100 mobile
nodes are made to wander in a 1500 X 1000 meter region
for 100 seconds. Transmission range of all nodes is 250
meters. This protocol is compared with the Uniform traffic
allocation. The experimental results prove that the
proposed method exceeds the performance against
congestion. Our proposed protocol reduces the packet drop
ratio and increases the reliability of system.
Fig 3. Proposed Topology
Algorithm For Congestion Control1. Back-pressure solution is used at flooded
gateway GW.
2. Once the Src1 node received the back-pressure
it gets slow-down.
3. If ( Incomming-Buffer = Full)
4. Use Traffic Re-assignment mechanism to
control the congestion at source node.
Traffic Re-Assignment Mechanism-
The parameters related to nodes comprise the utilized ratio
of buffers, load factor and utilized frequency of node.
Define 1- The buffer utilized ratio in node i is denoted as
BOi,
BOi=(data_buffered/buffer_size).
(1)
Define 2-The load factor of node i is denoted as Lfi.
LFi = (Ri / Ci).
(2)
Define 3- Forwarding capability function of node i,
denoted as F( LFi )
F(LFi) =
0, (1-LFi) <=0
1-LFi , others
(3)
Define 4-. Utilized frequency of node, denoted as Ui,
means the number of paths using node i. For some paths of
Fig 4.1. Packet/sec Vs Speed-time
Here, the above graph represents the number of packet
send per unit time. There is a variation in packet sending
time. Since there is un-uniform traffic pattern so we can
perform exact analysis.
Fig 4.4. Throughput Vs Speed-time
Fig 4.2.Pdr Vs Speed-time
The above graph represents the comparison between %
packet drop ratio v/s packet/sec. The simulation result
represents that the proposed solution has smaller value for
packet drop ratio that conventional solution.
In the above graph a comparison b/w throughput and
packet/sec is performed. Here our proposed work achieves
higher throughput value.
5. CONCLUSION
This paper is based on transit routing approach and it
consider a multi-traffic scenario. In multi-traffic based
scenario there is a high chance for congestion in the
network. So we have focus on a traffic reassignment
policy that is able to redeem the problem due to heavy
traffic in the network. The reassignment policy works here
to find the alternate path passes though the MANET
topology. To choose the best path among several were
discovered, we use the various parameters. At last we
perform the simulation and analysis phase where we
recognize that our proposed solution out performed than
the conventional solution.
6. REFRENCES
Fig 4.3. Reliability Vs Speed-time
The above graph is a comparison of proposed and
conventional scheme b/w reliability and packet/sec. Our
proposed solution achieve a higher value for reliability
factor.
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