mobility models in mobile ad hoc network
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Mobility Models in Mobile Ad Hoc NetworkS Radhaa & S Shanmugavelba Department of ECE, SSN College of Engineering, SSN Nagar, Old Mahapalipuram Road,Kalavakkam 603110, India email:b Department of ECE, CEG, Gunidy Anna University, Chennai 600025, IndiaPublished online: 01 Sep 2014.
To cite this article: S Radha & S Shanmugavel (2007) Mobility Models in Mobile Ad Hoc Network, IETE Journal of Research,53:1, 3-12, DOI: 10.1080/03772063.2007.10876115
To link to this article: http://dx.doi.org/10.1080/03772063.2007.10876115
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IETE Joumal of ResearchVol 53, No. I, January-February, 2007, pp 3-12
.Mobility Models in Mobile Ad Hoc Networl(S RADHA
Department of ECE, SSN College of Engineering, SSN Nagar, Old Mahapalipuram Road,Kalavakkam 603 110, India.
AND
S SHANMUGAVEL
Department of ECE, CEG, Gunidy Anna University, Chennai 600 025, India.email: [email protected]. [email protected]
In order to evaluate the performance of a protocol for an ad hoc network, the protocolshould be tested under realistic conditions such as transmission range, buffer space, data trafficmodels, and realistic movements of the mobile users. This paper discusses the survey of mobilitymodels that are used in the simulations of ad hoc networks. We describe several mobility modelsthat represent mobile nodes whose movements are independent of each other and group mobilitymodels that represent mobile nodes whose movements are dependent on each other. The maingoal of this paper is to present a number of mobility models used to model the ad hoc network andthe performance of the routing protocol is dependent on the more realistic models. Also this paperhelps the researchers about tfTe choices of mobility model can be used for their performanceevaluations. Lastly, we present the simulation results for the routing protocol that illustrate theimportance of choosing a mobility model in the simulation of an ad hoc network protocol.
Indexing terms: Ad hoc networks, Mobility models, Group mobility models, GloMoSim,Routing protocol.
1. INTRODUCTION
W IRELESS networking has witnessed anexplosion of interest from consumers in recent
years for its application in mobile and personalcommunications. The continued miniaturization ofmobile computing devices and the extraordinary rise ofprocessing power available in mobile laptop computerscombine to put more and better computer basedapplications into the hands ofa growing segment ofthepopulation. Also, due to the fast development ofmicrocomputer chips and other telecommunicationequipment, many portable computer-based devices
: now exist in our everyday lives. For example, laptops,. Personal Digital Assistants (PDA) and cellular phones. are so popular and convenient that people, even in
developing countries, use them extensively for boththeir personal and business activities. Since portabledevices form the mobile nodes in a Mobile Ad hoc
; NETwork (MANET); these recent advances [1] makean ad hoc network possible and practical. Thus, ad hocnetworks are a key technology for future systems.
An ad hoc network is formally called a MANET
, Paper No 66-B; Copyright © 2007 by the IETE.
and it consists of mobile. nodes with no fixedinfrastructure. Thus, nodes join and leave the networkrandomly. An ad hoc network is form'ed dynamicallycompared with conventional wired networks andwireless networks, the latter of which rely on a centralbase station. A MANET is also called a decentralizednetwork, as every node performs the functions of bothhost and router.
A MANET has several salient characteristics,where conventional wired networks and commonwireless networks do not have. These features include:
• dynamic topologies,
• bandwidth-constrained and variable capacity links,
• power-constrained operation, and
• limited physical security.
The above are both constraints and challenges fordeveloping a MANET. From the viewpoint of routingprotocol design, each mobile node's mobility helpscreate an extremely dynamic topology. Improving theaccuracy of data transfer as well as keeping thenetwork overhead low are two of the biggestchallenges for MANET research groups.
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4 IETE JOURNAL OF RESEARCH. Vol 53. No.!, 2007
In section 2. we review the mobility mooels used inad hoc networks. Section 3 describes the selection ofrouting protocol and in section 4. we discuss thesimulation environment. In section 5, we present thesimulation results and in section 6 we conclude thepaper.
2. MOBILITY MODELS
To address the challenges in MANET, manyresearch proposals are published. The mobility mooelsin ad hoc networks deal with individual motionbehaviors of the nodes. A mobility model shouldattempt to mimic the movements ofreal Mobile Nodes(MN). The following mobility models for ad hocnetworks [2] are reviewed in detail next:
• Random Walk Mobility Model
• Random Waypoint Mobility Model
• Random Direction Mobility Model
• Gauss-Markov Mobility Model
• Reference Point Group Mobility Model
• Obstacle Mobil ity Model
• The Virtual Track Based Group Mobility Model
There are other mobility models available for theperformance evaluation of a protocol in a cellularnetwork or personal communication system (peS).This paper focuses on application of some of thesemodels for the performance evaluation of ad hocnetworks.
2.1. Random Walk Mobility Model
The Random Walk -Mobility Model was firstdescribed mathematically by Einstein in 1926 [3].Since many entities in nature move in extremelyunpredictable ways, to mimic this erratic movement,the Random Walk Mobility Model was developed. Inthis mobility model, an MN moves from its currentlocation to a new location by randomly choosing adirection and speed in which to travel. The new speedand direction are both chosen from pre-defined ranges,[speedmin; speedmax] and [0, 21t"] respectively. If anMN which moves according to this model reaches asimulation boundary, it "bounces" off the simulationborder with an angle determined by the incomingdirection. The MN then continues along this new path.The Random Walk Mobility Model is a widely usedmObility model [4-7] and is also referred as Brownianmotion.
2.2. Random Waypoint Mobility Model
The Random Waypoint Mobility Model includespause times between changes in direction and/or speed[8]. An MN begins by staying in one location for acertain period of time (Le., a pause time) and then itchooses a random destination in the simulation areaand a speed that is uniformly distributed between[minspeed, maxspeed]. The MN then travels towardthe newly chosen destination at the selected speed.Upon arrival, the MN pauses for a specified timeperiod before starting the process again. Themovement pattern -of an MN using the RandomWaypoint Mobility Model is similar to the RandomWalk Mobility Model if pause time is z<?ro and[minspeed, maxspeed] = [speedmin, speedmax]. TheRandom Waypoint Mobility Model is also a widelyused mobility model [9-11].
2.3. Random Direction Mobility Model
The Random Direction Mobility Model [12] wasdeveloped to overcome the density waves in theaverage n~mberofneighbors produced by the RandomWaypoint Mobility Model. A density wave is theclustering of nodes in one part of the simulation area.In the Random Waypoint Mobility Model, theprobability ofan MN choosing a new destination that islocated in the center of the simulation area or adestination which requires travel through the middle ofthe simulation area is high. Thus, the MNs appear toconverge, disperse, and converge again. In this model,MNs choose a random direction to travel and once thesimulation boundary is reached, the MN p~uses for aspecified time, chooses another angular direction(between 0 and 180 degrees) and continues theprocess. Since the MNs travel to the border of thesimulation area and usually pause there, the averagehop count for data packets using the Random DirectionMobility Model will be much higher than the averagehop count ofmost other mobility models. In addition,network partitions will be more likely with theRandom Direction Mobility Model compared to othermobility models.
2.4. Gauss-Markov Mobility Model
This Mobility Model [13] was designed to adapt todifferent levels of randomness via one tuningparameter. Initially each MN is assigned a currentspeed and direction. At fixed intervals of time, n,movement occurs by updating the speed and directionof each MN. Specifically, the value of speed and
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RADHA & SHANMUGAVEL: MOBILITY MODELS IN MOBILE AD Hoc NETWORKS 5
direction at the nth instance is calculated based uponthe value ofspeed and direction at the (n-1)th instanceand a random variable using the following equations:
where Sn and dn in (1) are the new speed and directionof the MN at time interval n; a, where 0 $ a $ 1 is thetuning parameter and it is used to vary the randomness;sand aare constants representing the mean value ofspeed and direction as nand sXn _ I and dXn _ 1 arerandom variables with a Gaussian distribution.
At each time interval the next location is calculatedbased on the current location, speed, and direction ofmovement. Specifically, at time interval n, an MN'sposition is given by the equations:
the transmissions range in addition to the Manhattanmobility mociel, which is proposed to model themovement in an urban area [16]. In the Manhattanmodel, the mobile node is allowed to move along thehorizontal or vertical streets on the urban map. At anintersection of a horizontal and a vertical street, themobile node can tum left, right or go straight. Theprobability of moving on the same street is 0.5, theprobability ofturning left is 0.25 and the probability ofturning right is 0.25. Manhattan mobility modelfocuses on nodes moving along horizontal or verticalstreets, which is not enough to model nodes movingalong non-horizontal and non-vertical streets.Moreover, Manhattan model is not suitable to modelthe movement happening in the intersections ofhighway systems, which is much more complex thanthe intersection oflocal streets.
2.7. The Virtual Track Based Group MobilityModel
2.6. Obstacle Mobility model
The Obstacle Mobility model [15] extends theRWP model through the incorporation ofobstacles andit is proposed recently for the ad hoc network. Theseobstacles are utilized to restrict the node movement and
where (xn, Yn) and (xn-l' Yn-l) in (2) are the x and Ycoordinates of the MN's position at the nth and (n -l)thtime intervals, respectively, and sn_l and dn- l are thespeed and direction of the MN, respectively, at the(n -1)th time interval. To ensure that an MN does notremain near an edge of the grid for a long period oftime, the MNs are forced away from an edge when theymove within a certain distance of the edge. This is doneby modifying the mean direction variable d in theabove direction equation.
2.5. Reference Point Group Mobility
The Reference Point Group Mobility (RPGM)Model [14] is a group mobility model. In this model,each node in a group has two components in itsmovement vector: the individual component and thegroup component. The individual component isbased on the Random Waypoint model. The groupcomponent is shared by all nodes in the same group andis also based on the random waypoint model. Here, thedestination is an arbitrary place in the entire system.
Most of the protocols are assumed as randommobility models for speed and direction of the mobilenodes. Realistic models for the motion patterns areneeded during the simulation in order to evaluate the
The key idea of this model [17] is to use some"virtual tracks" to model the dynamics of groupmobility. Some "switch stations" are first randomlydeployed in the field. These stations are then connectedvia virtual tracks with given track width. The groupednodes must move following the constraint of the tracks.At the switch stations, a group can then be split intomultiple smaller groups; some groups may be evenmerged into a bigger group. Such group dynamicshappen randomly under the control ofconfigured splitand merge probabilities. Nodes in the same groupmove along the same track. They also share the samegroup movement towards the next switch station. Inaddition, each group member will also have an internalrandom mobility within the scope of a group. Themobility speeds of these groups are randomly selectedbetween the configured minimum and maximummobility speeds. One can also define multiple classesof mobile nodes, such as pedestrians, cars, UnmannedGear Vehicles (UGV), and Unm:.mned Aerial Vehicles(UAV), etc. Each class of nodes has differentrequirements: such as moving speed etc. In such cases,only nodes belonging to the same class can merge intoa group. This model is suitable to simulate theheterogeneous mobility scenario, including groupmovement, group dynamic split and merge, andindividual movement.
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system and protocol performance.
3. ROUTING PROTOCOLS
To evaluate the performance of these mobilitymodels, we used the new routing algorithm based onNode Transition probability (NTP) [18], which iscomputed using the received power at a particular nodefrom all other nodes. Node Transition Probabilityalgorithm (NTP) provides an efficient solution forwireless, mobile ad-hoc networks. Therein, we havecompared the performance of NTP based routingprotocol with existing on-demand routing protocolssuch as Ad hoc On-demand Distance Vector CAODV)[19] and Dynamic Source Routing (DSR) [8]. Whenthe numbers of communication pairs are increased, aconsiderable amount of routing overhead will begenerated. Simulation results show that the NTPalgorithm is more preferable for mobile networks thanother algorithms, when the node density, mobility andthe traffic levels are high.
4. SIMULATION ENVIRONMENTS ANDMETHODOLOGY
The routing protocols are simulated within theGloMoSim library [20]. The GloMoSim library is ascalable simulation environment for wireless networksystems using the parallel discrete-event simulationcapability provided by PARSEC [21]. We simulated anetwork of mobile nodes using config.in file in thesimulator. Nodes are placed randomly within a 500 x500 meter area. Radio propagation range of250 metersand channel capacity of 2 Mb/s were chosen for eachnode. Each simulation is executed for 600 seconds.Multiple runs with different seed values wereconducted for each scenario and the collected data wasaveraged over those runs. All these parameters wereset using config.in file in the simulator.
The following parameters are used in simulator torepresent the mobility. If MOBILITY is set to NO inthe config.in file, then there is no movement of nodesin the model. Under main directory in GloMoSimsimulator we have to write the program for the requiremobility models and pass the parameters usingconfig.in file. For example, in Random Waypoint(WP) model, a node randomly selects a destinationfrom the physical terrain. It moves in the direction ofthe destination in a speed uniformly chosen betweenMODILITY-WP-MIN-SPEED and MOBILITY-WPMAX-SPEED (meter/sec). After it reaches itsdestination, the node stays there for MOBILITY-WP-
PAUSE time period. The MOBILITY-INTERVAL isused in some models that a node updates its positionevery MOBILITY-INTERVAL time period. TheMOBILITY-D-UPDATE is used that a node updatesits position based on the distance (in meters). The valuefor the SPEED, PAUSE, INTERVAL and UPDATEhas to be defined by the user. Similarly for other type ofmobility models can be generated with requiredparameters.
A traffic generator which is available in app.conffile in the simulator is used to simulate the CBRsources. The size of data payload is 512 bytes. Datasessions with randomly selected sources anddestinations were simulated. Each source transmitsdata at a rate of4-12 pkts/sec. We vary the traffic loadby changing the number of data sessions in app.conf·file in the simulator and examine its effect on routingprotocols.
5. PERFORMANCE EVALUATION
In this section, we analyze the simulation results ofNTP algorithm with existing algorithms for differentmobility models. The following metrics were used incomputing the protocol performance. The metricswere derived· from one suggested by the MANETworking group for routing protocol evaluation [22].
. Packet Delivery ratio
Measured as the ratio of the no. pf data packetsdelivered to the destination and the no. ofdata packetssent by the sender.
End-to-end delay
Measured in ms as the time between the receptionofthe last and first packet I total no. ofpackets reachingthe application layer. This delay includes processingand queuing delays in each intermediate node.
Control overhead
Measured as ratio of no. of control packetstransmitted during entire simulation period by datapacket transmitted.
The performance results ofvarious algorithms withrespect to mobile speed obtained using GloMoSim fordifferent mobility models are presented. The NTPalgorithm requires lesser control overhead comparedto AODV as shown in Fig 1. This is due to the fact thatonly the stable routes are used by the algorithm forrouting the packets. Flooding ofRREQ and the search
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RADHA & SHANMUGAVEL: MOI3ILlTY MODELS IN MOI3ILE AD Hoc NETWORKS 7
for new route contributes to the increased overhead inAODV. On the other hand, the control overhead is lesswith an increase in packet size in DSR. Both NTP andAODV use equal sized packets.
A slight decrease in throughput is observed in thecase ofNTP for random walk model when compared toAODV and DSR as shown in Fig 2. The RREP packetused by AODV helps to maintain the performance.This is an area where there is scope for improvement inNTP algorithm. During high mobility of theparticipating nodes, all the algorithms show a smalldegradation in throughput.
Longer is the duration of pause time, lower is themobility. In Fig 3, the control overhead is found tostabilize for longer pause times for all the threealgorithms. NTP algorithm is found to perform inbetween AODV and DSR in terms ofcontrol overhead.
The throughput of all the three algorithms forRandom waypoint mobility with (miformly distributedspeed is shown in Fig 4. The performance is found to bebetter when compared to Random Walk mobilitymodel, which is characterized by abrupt transitions indirection and speed of the mobile nodes. Increasingpause times results in smoother transitions andtherefore, the throughput and control overhead remainsconstant.
In reality, one observes that lower mobile speeds'occur with higher probability and vice versa.Therefore, we have studied the effect of these mobilitymodels with exponential distributed speed. Thecontrol overhead and throughput performance isshown for all the three algorithms in fig 5 and Fig 6.The Control Overhead for NTP lies between that ofAODV and DSR. Random Waypoint with Exponentialspeed is the most realistic mobility model. The control
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8 IETE JOURNAL OF RESEARCH, Vo153. No.1. 2007
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overhead is found to stabilize for longer duration ofpause times for all the three algorithms.
The throughput performance for AODV and DSRis found to improve with exponentially distributed
speed is shown in Fig 7 and Fig 8 shows the amount ofcontrol overhead required to obtain the abovethroughput. It is almost 100% due to the lower mobilityof the nodes and therefore, fewer link breakages. Asthe traffic in the network increases, where the number
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1:r-,. _ - .. - -~~- . . - - - - - - -; ......~ '-~~---~-------:-Q-0.95 --.-.-~--.-.-;--- • ..... • • •
0.9 - •• - - - ~ - - _. - - ~ - - - - - - -j- ---_. -~ -- -- -- ~ ----- -I~ ~~V 1-: : : : : J -... DSR L
0.85 • - • - •• f-Expon~niialsp~d (RP:NDO-M WALK)---- : :0.8 ------t~-----~-------:-------~------t------i-------:---. . , , .
• I • ,. •
I • • , • I •
------~------,-------.-------~------~------~----------I I • I , •• I • • , ,
f • • I • I •
0.7 ------.------~-------:-------~------;------~-------:---I • , I I •
• fl. • I •
0,G5 - - - ... - - ~ -- - - - - - '1- ...... - ...... -:- - ......... - ... ~ ... - ......... - ~ ......... - - - ~ - ... - ... - - -: ... - ...I I I I I , •I I • , , , •
0.6 ------f------~------~-------~------~------~------~---J • I • I •• • , I I t
I • I ., • f ,o 55 - - - ... - - y - - - - - - ~ - ... - - - - -:- - _ ...... - ... r -- - ---: ... --- ...... ~ - -- --- -;- --I I I I I •I I I • I •
0.5OL-----:-l'=0---=2'=O---:::3~O----:4D~---;:50~---;:6~O---:;7~0--J
Speed ( km I hr )
Fig 6 Throughput vs mobility
l' 075
~
0.1<
0.05
0.15..
;: ;: ~NTP_____ .{ - : - _ ~ - __ - - -: - - - - - - ~ - - - - - ~ - - - - -A. AODV
: : : : : : - ..... DSR, , , , . .-----~------.------~-----~------~-----.------.------~--• I I I •
• • It.I It' • I I I
.----~E~-pon-e6-tiai spe~d -(RANDOM·WAY pOI~ii) ---T-----f_.-----~------:------t-----~------~-----i------:------~--
• , • I , •• , I , I •
I I • I • • I •
- - - - - ... - - - - - -,- - - - - - 'r - - - - - ., - - - - - - r- - - - - - .... - - - - - .,- - - - - - r- - -I • • • I, t I I I
• I • , • , I ,_____ ~ • ~ ~ 4 1 ~ __
• , • , I ,t I I • • •
I , • , I • , I
~~- =-::..:::.: ~-:.k-:-:-- --:--- -- -~ -- - -- -:- --- -- ~--~: : , ~---:--~---?
- - - - -; - - - - - ~ T- - - - - -~.- - - - - -:- - - - - ·-7 - - - - - -:- - - - - -;. --:..+- ........ : if . : : (t>
+--~~---_:~:::::~:::::~::::::~:::::~~~~-~~-----~--....- r • , , , , , ,
f I I , • • • I
• • • t t , I •
o01----,O,i-O:----:-2~OO-::---=3~OO=----:4-:'::O:::O--=!3-:'::O:::0:---::6-:'::O::::O:----:;7:-:.0::::0:----;:8::;!0':::0:---'
Pause Time ( Seconds)
Fig 7 Control OIH vs pause Time
0.5
0.45
0.4
0.35
~ 03-E'"<3 0.25e15 0.2<...>
, - & ;;;?,~. - - "!" f .............",..,..,••,.......~~....F~-~.......-""""".-~-.,..--- ..
o 95 . ~~ - .1; -.- ... ~..-;..----- .¥- - -.- - -:- - _.j) - _... ..;. ...:)- 'C". f • I •
: : : :: -e- NTP0.9 -- ... t .. ~------~-----:--- ~. AODV
: : : • ..... ·DSR
0.85 _. - - - g-x-p·One-rrtial spe&d-(RANDOM-WAY pOIUi)-: :0.8 - - - - - ~ - - - - - -:- - - - - - t - - - - - -:- - - - - -:- - - - - - t - - - - - -:- - - - - -}- --
I • I , •I • • • •
I • , , • • • •
-----~------l------~-----~------~-----T------.------~--. . .. . .• • • f • • • •
0.7 - - - - - ... - - - - - -- - - - - - • - - - - - -: ... - - - - -:- - ... - - - ~ - - - - - -:- - - - - - :- - -I • I I I
. I I • , , I • •0.65 -----~------:- ... ----t-----~------~-----1-----~------~--• I • I • I • •I I • I· • • I I
0.6 - - - --;- - --- -:--- ... --f ...... - - - -:-- - - --:- - --- ... i-- ...... - -:- --- _ ... ;..--• • • I • , • I• • • • • I • •, I I I • f • I
0.55 - - - - - '\ - - - - - -.- - - - - - r ... - - - -..." - ... - - - ... ,. - ...... - - ,. - - - - ... -,- - ...... - - ...... -• • I I • • •. . . , . . .I • I , f • I
:::"Q.
g, 0.75C>
¢=.
Fig 8 Throughput vs pause Time
of transmitting nodes increases, NTP uses lessercontrol overhead to deliver the packets because of theuse of the more stable route. AODV uses more numberofcontrol packets since it floods the RREQ packet for
every Source-Destination (S-D) pair which is shown inFig 9. Throughput performance of all the threealgorithms is shown in Fig 10 for different trafficconditions. As the traffic increases, the throughput of
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10 IETE JOURNAL OF RESEARCH, Vol 53, No. 1,2007
04
035
0.05
O.C ,.---.....---.,----.----,---r---:-r==:==';=::;::;:::==il: : ~ NTP
____ __ : -: - - - - - - ~ - - - - - - ~ - - - - - -:- - - - - - -:- - ~ AODV~ ::: - .... DSR· . . . , ,
------~------------.------p------~------.------~------I • • • • • I
• • I I • I •• • , I • • I
- - - - - -rEx-p-o-n-e~n-tial ;p~1d-{RANb-oriWAy·poINt) ----1- -----------~-----~------1------t------~------:------i------
• • I • , • ,
• , • I • I •• , I • I • I
------.------~------~------r------r-----~------,------, I • •
fl' I
• I , • • I •
-----_I_-----~------.------~------~ __ ----I- --~------• I I I • , •
• I I • , I I
°o1:~ ~~~J~~=~t~~~~~~~~j~~~~~~• I • • t I I
~::::::~:::::t::=:~~+-----~~~~---~-----~-----~------• • • • I I I
• • • , I I I
°1'::-O---:1:-'::5:----...,~~O:::----::2:';:5---:3~O~--;::;3~5---4;::O;----;45~-~50
No. of Source-Des-lmaloon Pair
045
"2g 0.2'->
Fig 9 Control Overhead vs no of SoD pair
1 _
::i. 0.8
"t~ 0.752
¢= 07
Fig 10 Throughput vs no of SoD pair
AODV decreases due to heavy congestion created bythe excessive control and data packet~.
6. CONCLUSION
In this paper, we have studied the effect of variousmobil ity models in ad hoc networks with DSR, AODVand NTP based routing algorithms. The performanceresults of this algorithm are-presented in detail. Fromthe results of the throughput, end-to-end delay and thecontrol overhead parameters, it is found that theexponential model outperforms the uniform models fora wide range of speeds and traffic loads. The newRouting algorithm based on Node TransitionProbability is found to perform acceptably withconsiderable reduction in control overhead. TheControl Overhead is found to decrease by as much as30% with only 3% reduction in throughput whencompared to AODV. The performance of NTP isstudied using different mobility models proposed forAd-Hoc networks as shown in Table 1.
Further research can be carried out in examining
the movements ofentities in the real world to produceaccurate mobility models and to develop a new modelthat combines the best attributes ofsome ofthe models.For example, a model could be developed bye,ombingGauss-Markov Mobility Model for the edges and themovement patterns of the mobile node with theRandom Waypoint (or Random Walk) MobilityModel. Also, the similarities and differences betweenmobility models that randomly select directions andmobility models that randomly select specificlocations should be analyzed.
REFERENCES
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2. Tracy Camp, Vanessa Davies, Jeff Boleng, MobilityModels for Ad Hoc Network Simulations, Journal oflVCMC:Special issues on Mobile Ad hoc Networks:Research, Trends andApplications, 2002.
3. ~f Sanchez and P. Manzoni. Anejos, A java basedsimulator for ad-hoc networks, Future Generation
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RADHA & SHANMUGAVEL: MOIJILlTY MODELS IN MOIJILE AD Hoc NETWORKS
TABLE 1: Comparison of different mobility models
11
MOBILITY MODEL
NTP (Node transition probabilitybased routing)
Throughput Control overhead
AODV (Ad-hoc on demanddistance vector routing)
Throughput Control overhead
Random Walk
(i) Uniformly distributed speed
(ii) Exponentially distributed speed
Random Waypoint
(I) Uniformly distributed speed
(Ii) Exponentially distributed speed
Gauss Markov
0.9608
0.9664
0.9619
0.9541
0.9149
0.1149
0.0932
0.1226
0.1046
0.0849
0.9913 0.1592
0.9934 0.1398
0.9954 0.1470
0.9934 0.1422
0.9942 0.1374
Computer Systems, vol 17, no 5, pp573-583, 2001.
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12 JETE JOURNAL OF RESEARCH. Vol 53. No.1, 2007
AUTHORS
..!'~.~~*'
She has 30 publications in International and nationaljournals and conferences in the area of Mobile Ad hocnetwork. Presently she is guiding 6 research scholars in the.area of security in mobile ad hoc networks, sensor networksand Wireless MEMS. Her current areas of research aresecurity and architecture issues of mobile ad hoc networksand sensor networks. She received IETE-SK Mitra MemorialAward in October 2006 for the Best Research Paperpublished in the IETE Journal of Research.
***•
(j•
S Radha graduated from MaduraiKamraj University, in Electronics andCommunication Engineering duringthe year 1989. She obtained herMaster degree in Applied Electronicsfrom Government of Technology,Coimbatore and PhD degree in thearea of Mobile Ad Hoc Networks fromCollege of Engineering, Gunidy, Anna
University, Chennai. At present she is working as Professorin the Department of ECE, Sri Sivasubramaniya NadarCollege of Engineering, Klavakkam, India.
S Shanmugavel graduated fromMadras Institute of Technology inElectronics and CommunicationEngineering in 1978. He obtained hisPhD degree in the area of CodedCommunication and Spread Spectrum
. \ ... .....".....) Techniques from Indian Institute ofVhf, \. ~ '. " t 11 Technology, Kharagpur. During theLl;.... ~ .............u,!,.... period November 1978 to March 1989he was working as Senior Research Assistant at Radar andCommunication centre, liT, Kharagpur, where he worked invarious sponsored research projects on DigitalCommunication and Spread Spectrum Techniques.
He joined the faculty of the Dept of E & ECE at liTKharagpur as Lecturer in 1987 and became AssistantProfessor in 1991. Presently he is a Professor at School ofElectronics and Communication Engineering at AnnaUniversity, Chennai. He has published more than 100research papers in the national and internationalconferences and journals in the area of Mobile Ad hocNetworks,ATM Networks, Spread Spectrum Communicationand Error Control Coding. His current areas of interest areMobile Ad hoc Networks, Broadband ATM networks andCOMA Engineering.
He received rETE-CDIL award in September 2000 forthe Best Paper published in the JETE Journal of Research.
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