Convergence Time Evaluation of AODV and AODV+G in MANETs

8/8/2019 Convergence Time Evaluation of AODV and AODV+G in MANETs

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(IJCSIS) International Journal of Computer Science and Information Security,Vol. 8, No. 5, August 2010

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Convergence Time Evaluation of AODV and

AODV+G in MANETs

Annapurna P Patil Harish.R Department of Computer Science and Engineering,

M.S. Ramaiah Institute of Technology,Bangalore-54,India.

[email protected] [email protected]

Abstract -Wireless mobile ad-hoc networks are characterized

as networks without any physical connections. In these networks

there is no fixed topology due to the mobility of nodes,

interference, mulitpath propagation and path loss. Hence a

dynamic routing protocol is needed for these networks to

function properly. Many routing protocols have been developed

for accomplishing this task. Selecting most appropriate routing

protocol for a particular network scenario is the critical issue.

Most attempts made at evaluating these algorithms so

far have focused on parameters such as throughput, packet

delivery ratio, overhead etc. An analysis of the convergence

times of these algorithms is still an open issue. The work carried

out fills this gap by evaluating the algorithms on the basis of

convergence time.

In this paper we present and examine the convergence

time evaluation of routing protocols AODV and AODV+G . The

algorithm performances are compared by simulating them in

ns2. Tcl is used to conduct the simulations, while perl is used to

extract data from the simulation output and calculate

convergence time. After extensive testing we observed that

AODV+G converged well in all situations than AODV. The

paper also evaluates the algorithms using the rudimentary

metrics-throughput and packet delivery ratio.

Keywords- Routing Protocols, MANETS, Convergence Time.

I. INTRODUCTION

A Mobile Ad-Hoc Network (MANET) is a self-

configuring network of mobile nodes connected by wireless

links, to form an arbitrary topology. The nodes are free to

move randomly. Thus the network's wireless topology may be

unpredictable and may change rapidly. Minimal

configuration, quick deployment and absence of a central

governing authority make ad hoc networks suitable for

emergency situations like natural disasters, military conflicts,emergency medical situations etc .

Every device in a MANET is also a router because it

is required to forward traffic unrelated to its own use. Almost

every year, the world is struck by numerous catastrophic

natural disasters, such as earthquake, hurricane, typhoon,

tsunami, etc. In such a situation communication systems,

fixed or mobile, were usually down due to various reasons.

The loss of communication systems as well as information

networks made the rescue operation extremely difficult.

WiFi-ready notebook PCs( MANET nodes) owned by rescue

volunteers themselves to construct a MANET to support such

a need.

MANET can be classified based on the

communication pattern or the devices used, the variants of

MANETs on the type of devices are sensor and ad hoc

networks. Routing is one of the critical issue in MANET.

Selecting the energy efficient routing protocols improves the performance of the communication. The routing protocols are

classified into three types. Proactive protocols maintain

routing information for all the destinations, and keep updating

this information through periodic updates, an example for this

protocol is DSDV[1],OLSR[3]. Reactive protocols don’t

maintain information for all the destination, rather they

discover the route to a destination on demand, an example for

this protocol is AODV[2]. Hybrid protocols attempt to

combine the advantage of both proactive and reactive

protocols, an example for this protocol is TORA[5], ZRP[4],

MPOLSR[6]. AODV+G[7] reduces unnecessary traffic will

effectively improve the efficiency of those mobile nodes in

network.AODV and AODV+G protocols performs differently

under different network scenarios. One protocol might

perform better than others in specific situation. These

protocols are compared in terms of convergence time to

uncover in which situations these types of algorithms have

their strengths and weaknesses.

II. RELATED WORK

There are many other works which are related to our

work in evaluating routing algorithms. [7] AODV and

AODV+G has been compared in terms of Average delay,

Packet delivery ratio, Normalized routing load and Routingload reduction, but not in terms of convergence time. [8]

AODV and DSDV has been compared in terms of

convergence time. Many papers have compared AODV with

other routing algorithms. In [9] AODV and DSDV have been

compared with average throughput, packet loss ratio, and

routing overhead as the evaluation metrics, [10] has compared

AODV and DSDV in terms of delay and drop rate, [11]

compares AODV and DSDV in terms of throughput, packets

received, delay and overload. Similarly, [12] compares

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AODV, DSDV and DSR in terms of throughput, delay, drop

rate.

III. PROTOCOL SPECIFICATION

This section gives the small presentation of two

protocols we evaluate in this paper.

A. AODV The Ad-hoc On-Demand Distance Vector (AODV)

routing protocol is designed for use in ad-hoc mobile

networks. AODV is a reactive protocol: the routes are created

only when they are needed. It uses traditional routing tables,

one entry per destination, and sequence numbers to determine

whether routing information is up-to-date and to prevent

routing loops.

An important feature of AODV is the maintenance

of time-based states in each node: a routingentry not recently

used is expired. In case of a route is broken the neighbours

can be notified. Route discovery is based on query and replycycles, and route information is stored in all intermediate

nodes along the route in the form of route table entries. The

following control packets are used: routing request message

(RREQ) is broadcasted by a node requiring a route to another

node, routing reply message (RREP) is unicasted back to the

source of RREQ, and route error message (RERR) is sent to

notify other nodes of the loss of the link. HELLO messages

are used for detecting and monitoring links to neighbours.

B. Gossiping & AODV+G

The basic gossiping protocol is simple. A source

sends the routing request with probability 1. When a nodefirst receives a routing request, with probability p it

broadcasts the request to its neighbors and with probability 1

– p it discards the request; if the node receives the same rout

request again, it is discarded. Thus, a node broadcasts a given

route request at most once. [7] proposes GOSSIP( p,k,m), an

extension to the basic gossiping, and suggests that:

A node broadcasts with probability 1 for the first k hops

before continuing to gossip with probability p.If a node with n neighbors receives a message and does not

broadcast it, but then does not receive the message from at

least m neighbors within a reasonable timeout period, it

broadcasts the message to all its neighbors [7].

Hass et al. implements GOSSIP( p,k,m) in Ad HocOn-Demand Distance Vector protocol (AODV) [18], a typical

and well-studied on-demond routing algorithm suited for

mobile nodes routing in ad hoc network. We refer this gossip-

based AODV as AODV+G. The experiments in [7] shows

that gossiping can reduce control traffic up to 35% when

compared to flooding and the most significant performance of

GOSSIP is achieved by taking p=0.65 , k=1 and m=1.

In AODV+G, if the expanding-ring search with a

smaller radius fails, rather than flooding to the whole

network, here GOSSIP3(.65,1,1) is used. The timeout period

of GOSSIP3 should be big enough to allow neighboring

nodes to gossip. The NODE_TRAVERSAL_TIME parameter

of AODV is a conservative estimate of the average one hop

traversal time for packets that includes queuing delays,

interrupt processing times and transfer times. GOSSIP3 is not

used in the expanding-ring search with a smaller radius, since

flooding is more efficient than gossiping for zone with small

radius because of the back-propagation effects[17]. The

variant of AODV that uses GOSSIP3 is called AODV+G.

IV SIMULATION AND PERFORMANCE ANALYSIS

A. Environment and Assumption

Simulator chosen : The proposed algorithms are simulated on

NS2(version 2.33)[13]. NS2 is popularly used in the

simulation of routing and multicast protocols, among others,

and is heavily used in ad-hoc networking research. nssupports an array of popular network protocols, offering

simulation results for wired and wireless networks alike. It

can be also used as limited-functionality network emulator. It

was necessary to use available implementations of algorithms

rather than implement them freshly ourselves, as it is

important for the acceptance of an evaluation that the

implementation used for evaluation has been scrutinized and

accepted as correct by the community. Else the evaluation

results will not be accepted as doubt will exist about the

correctness of the implementation of the algorithms

Algorithms chosen : Here in this paper we have selected to

simulate and evaluate the performance of AODV andAODV+G protocols. AODV is a reactive routing protocol and

AODV+G is variant of AODV routing protocol with

GOSSIP3. Further experiments can be built based on the

results of this project, to compare convergence time

performance of algorithms within the same category as well.

Mobility model : The Random Waypoint model is the most

commonly used mobility model in research community. At

every instant, a node randomly chooses a destination and

moves towards it with a velocity chosen randomly from a

uniform distribution [0,V_max], where V_max is the

maximum allowable velocity for every mobile node. After

reaching the destination, the node stops for a duration defined

by the 'pause time' parameter. After this duration, it again

chooses a random destination and repeats the whole process

until the simulation ends. To create Mobile node Movement Scenario files, the

command line that needs to be run under directory : ns-

allinone-2.33/ns-2.33/indep-utils/cmu-scen-gen/setdest :

./setdest [-n num_of_nodes] [-p pausetime] [-s maxspeed] [-t

simtime] [-x maxx] [-y maxy] > [output-file][14].

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Traffic pattern : Moreover, traffic sources may generate

packets at constant bit rate (CBR), or at variable bit rate

(VBR). The CBR class is commonly used for voice and data

services. In this context, the data rate and the delay remain

constant during the packet transmission. More particularly,

CBR traffic sources provide a constant flow of data packets of

512 bytes with a transmission rate of 4 packets per second.

All CBR traffic scenarios ar e generated using cbrgen.tcl in

NS-2

To create CBR traffic scenario files, under directory

:ns-allinone-2.33/ns-2.33/indep-utils/cmu-scen-gen/cbrgen.tcl

./ns cbrgen.tcl [-type cbr|tcp] [-nn nodes] [-seed seed] [-mc

connections] [-rate packet/second for one connection] >

[output-file][14].

Network scenario : The simulations are conducted using the

network simulator ns2 [14]. Random Waypoint mobility

model is used. The physical layer simulates the behavior of

IEEE 802.11 (as included with ns2). Each node has a radio

range of 250 meter, and uses TwoRayGround as the radio propagation model.

All the scenarios are based on the following basic parameters:

cbr (constant bit rate) traffic

topology of size 500 m x 500 m

maximum speed of each node 20 m/s

simulation time 180s

transmission rate (packet rate) 10 m/s

The number of nodes is varied in the range [10,100] in steps

of 10 (to represent 10 node densities). Pause time is varied in

the range [0,180] in steps of 20 (to represent 10 pause times).

B. Performance Metric

A trace file contains a lot of information which may not be

required to analyze the performance of the protocol. We are

always interested in some amount of information that is

sufficient to predict the efficiency of the protocol. The

following performance metric is needed to be taken into

consideration in order to analyze and compare the

performance of AODV and AODV+G

Convergence Time : In [15], convergence time has been

defined as the time between detection of an interface being

down, and the time when the new routing information is

available. [16] defines a route convergence period as the

period that starts when a previously stable route to somedestination becomes invalid and ends when the network has

obtained a new stable route for. Similarly, we define

convergence time as the time between a fault detection, and

restoration of new, valid, path information. [15] calculates convergence time in the IP backbone.

The authors arrive at the value of convergence time by

deploying entities called ‘listeners’, which listen to every link

state PDU being sent by the is-is protocol. The time when the

first ‘adjacency down’ packet is observed is taken as the time

of detection of an interface being down. This failure event is

said to end when the listener receives link state PDUs from

both ends of the link.

We arrive at the convergence time by measuring the

interval between the detection of route failure and successful

arrival of a packet at the destination over the newly computed

route. This includes not only the routing convergence time,

but also the time taken for the packet to traverse the network

from the source to the destination over the newly discovered

path. Since this is a comparative analysis, and both the

routing protocols use shortest distance with number of hops as

the metric for distance calculation, both protocols will arrive

at the same new route, and the time taken to reach the

destination over this new route will be the same (since all

physical characteristics are the same). Hence this extra time

measured does not affect the comparative analysis.

In any case, the time taken for a packet to travel from

the source to the destination is negligible when compared to

the time taken for the algorithm to discover the new route,

either through route request – route reply sequences as inreactive protocols, or by waiting for an update that contains

new route information as in proactive protocols. Also, this

automatically verifies that the new path calculated is correct.

The cycle of invalidation of old path and discovery

of a new path might occur many times, and for many source-

destination pairs over the course of the simulation. Hence the

average value of these times is taken as the convergence time

of that algorithm for that scenario.

This procedure has been carried out in perl.

Throughput : If y number of packets delivered within t time

at a node then the throughput at the node could be defined as

y/t. By definition, the throughput needs to be calculated at the bottleneck node, not sender. For the throughput calculation, in

general divide the successfully received packets by the

simulation time will give the answer. In the trace file there are

different levels of received packets such as the RTR or AGT

level. The packets received by the node in its AGT level will

be the real received packets. Here these packets are filtered

from the trace file using perl script.

Packet Delivery Ratio : The ratio between the number of

packets successfully received by the application layer of a

destination node and the number of packets originated at the

application layer of each node for that destination.

V. EXPERIMENTAL RESULTS

Graphs are one of the ways to analyze and compare

the results of the trace file. Other methods can also be used

for comparison like tabular form showing required output

data of the trace file. Simple MS Excel or MATLAB also

work for plotting graphs. In this paper the graphs are plotted

using xgraph in NS2.


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In order to be able to cover most if not all the types

of scenarios the algorithms might face, we varied both the

node density (number of nodes) and the node mobility (pause

time). The node density (number of nodes) was varied in the

range [10,100] in steps of 10 (10 different node densities).

The upper limit of this range was chosen to be 180

because the simulation time is 180s in all the cases. Thus a

pause time of 180 implies that the nodes pause in their initial

positions for 180 seconds – the entire duration of the

simulation. Hence this represents the case where nodes are

completely static. Similarly, pause time 0 represents very high

mobility where the nodes are in constant motion. Thus we

tested each algorithm over 10 node densities x 10 pause times

= 100 scenarios.

A. Convergence Time

Convergence time of AODV and AODV+G is calculated

using perl script. This script parses trace file created by

simulating AODV and AODV+G algorithms to calculateconvergence time. In each graph, the node density is fixed

and the pause time is varied.

Figure 1: 10 nodes, varying paused time

Figure 2: 20 nodes, varying pause time





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Based on the above figures it is found AODV+G

convergence time is less than AODV in all assumed network scenarios. It is also found that as the pause time increases the

convergence time of AODV+G decreases. Convergence time

of both AODV and AODV+G increases as the node density

increases.

B.. Throughput

Here node density is varied from 10 to 100 in steps

of 10 nodes. For each node density both the algorithms are

simulated with varied paused time from 0s ts 180s in steps of

20s. Average throughput in each node density is taken and the

graph is plotted as show in figure 12.


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Figure 12: Throughput AODV Vs AODV+G

From the above figure we observed that AODV with

the low density performance well than AODV+G. As the

node density increases AODV+G throughput increases. With

the node density more than 30 nodes the throughput of AODV+G is almost double than AODV.

C. Packet Delivery Ratio

Here node density is varied from 10 to 100 in steps

of 10 nodes. For each node density both the algorithms are

simulated with varied paused time from 0s ts 180s in steps of

20s. Average of packet delivery ratio in each node density is

taken and the graph is plotted as show in figure 11.

Figure 11: Packet Delivery Ratio AODV Vs AODV+G

From the above figure we observed that AODV with

the low density performs well than AODV+G. With the node

density more than 30 nodes packet delivery ratio of AODV+G

is more than AODV.

VI. CONCLUSION

AODV and AODV+G mobile Ad-hoc routing

protocols have been presented and evaluated using well know

network simulator NS2( version 2.33 ). AODV+G is gossip

based AODV, here GOSSIP3(.65,1,1) is used. These two

protocols are evaluated using the network performance metric

convergence time. Here we observed that AODV+G

converged well than compared to AODV in all assumed

network scenarios. We also noticed that with the very low

node density throughput and packet delivery ratio of AODV

is more than AODV+G. With node density more than 30

nodes AODV+G performs better than AODV. We can extend

our work to compare the performance of Adaptive Gossip-

based Ad Hoc Routing (AGAR) with Gossip-based Ad Hoc

Routing (AODV+G) using convergence time, throughput and

packet delivery ratio.

ACKNOWLEDGMENT

We wish to acknowledge our Principal Dr K Rajanikanth,

M. S .Ramaiah Institute of Technology,Bangalore-54 and

Professor and Head of the Department at CSE Prof .V.

Muralidharan for their encouragement which helped us

produce this work..

VII. REFERENCES

[1] Charles E.Perkins a nd Pravin Bhagwat, “Highly DynamicDestination-Sequenced Distance-Vector Routing (DSDV)for Mobille Computers ” In Proceeding of SIGCOM’ 94 Conference on Communications Architecture,

protocols and Applications August 1994.

[2]

“Ad hoc On Demand Distance vector (AODV)Routing protocol”, RFC 3561, july 2003.

[3] Jing Xie, Luis Girons Quesada and Yuming Jiang“A Threshold-based Hybrid Routing Protocol for MANET” Department of Telematics, Norwegian University of Science and Technology. 2008

[4] Nicklas Beijar, “Zone Routing Protocol (ZRP Networking Laboratory, Helsinki University of Technology. 1998

[5] [ns] MANET : TORA Routing Protocol at http://www.mail-archive.com/[email protected]/msg05173.html.

[6] Jiazi YI, Sylvain David, Asmaa Adnane, Benoit Parrein, “ Multipath-LSR. Simulation and Testpath”, 5th OLSR Interop/Workshop, Vienna,Austria (2009)

[7] Z. J. Haas, J. Halpern, and L. Li. Gossip-based ad hoc routing. IEEE Proceedings of INFOCOM , 2002.

[8]

Annapurna P Patil, Narmada Sambaturu, Krittaya Chunhaviriyakul,“Convergence Time Evaluation of Algorithms inMANETs”, (IJCSIS) International Journal of Computer Science andInformation security, julyb2009.

[9] R. Khalaf, A. El-Haj-Mahmoud, and A. Kayssi(Lebanon) “Performance Comparison of the AODV andDSDV Routing Protocols in Mobile Ad Hoc Networks”,

From Proceeding (371) Communication Systems and Networks – 2002.


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[10] Ebrahim Mahdipour , Ehsan Aminian, Mohammad Torabi,Mehdi Zare, "CBR Performance Evaluation over AODV and DSDV in RW Mobility Model, "Computer and Automation Engineering, International Conference on, pp. 238-242, InternationalConference on Computer and Automation

Engineering (iccae 2009), 2009.[11] Muazzam Ali Khan Khattak, Khalid Iqbal, Prof Dr.

Sikandar Hayat Khiyal, “ Challenging Ad-Hoc Networksunder Reliable & Unreliable Transport with Variable NodeDensity”, Journal of Theoretical and Applied InformationTechnology, 2005-2008.

[12] REN Wei, YEUNG D.Y, JIN Hai1” TCP performanceevaluation over AODV and DSDV in RW and SN mobilitymodels” (School of Computer Science and Technology,Huazhong University of Science and Technology, Wuhan430074, China) (Department of Computer Science, HongKong University of Science and Technology, Hong Kong,China) 2005.

[13] The Network Simulator - ns-2 at http://www.isi.edu/nsnam/ns/

[14] Jaspreet kaur and Cheng Li, “ Simulation and analysis of Multicast protocols in Mobile Ad Hoc Networks using NS-2”,

Memorial University of Newfoundland St.John’s, Newfoundland,A1B 3X5, Canada, 2007

[15] G. Iannaccone, C.-N. Chuah, R. Mortier, S. Bhattacharyya, andC. Diot, “Analysis of link failures over an IP backbone,” in ACM SIGCOMM Internet Measurement Workshop (IMW), Nov. 2002.

[16] D. Pei, X. Zhao, L. Wang, D. Massey, A. Mankin, S. F. Wu, andL. Zhang, “Improving bgp convergence through consistencyassertions, ” in Proc. of IEEE INFOCOM , 2002.

[17] Zygmunt J. Haas , Senior Member, IEEE , Joseph Y. Halpern , Senior Member, IEEE , and Li (Erran) Li , Member, IEEE, “Gossip-Based AdHoc Routing”, IEEE/ACM TRANSACTIONS ON NETWORKING,VOL. 14, NO. 3, JUNE 2006

AUTHORS PROFILE

The authors are Faculty and Post graduate Student at M S

Ramaiah Institute of Technology, Bangalore working in the

area of performance evaluation of routing algorithms at the

R&D labs, Department of Computer Science and

Engineering.


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Convergence Time Evaluation of AODV and AODV+G in MANETs