Mobility Pattern Recognition in Mobile Ad-Hoc Networks

Transcription

1 Mobility Patten Recognition in Mobile Ad-Hoc Netwoks S. M. Mousavi Depatment of Compute Engineeing, Shaif Univesity of Technology H. R. Rabiee Depatment of Compute Engineeing, Shaif Univesity of Technology & ITRC M. Moshef Depatment of Compute Engineeing, Shaif Univesity of Technology A. Dabimoghaddam Depatment of Compute Engineeing, Shaif Univesity of Technology ABSTRACT A Mobile Ad hoc Netwok (MANET) is a collection of wieless mobile nodes foming a self-configuing netwok without using any existing infastuctue. Netwok nodes in a mobile Ad-hoc netwok move in some motion pattens called mobility models. The mobility models play a vey impotant ole in detemining the potocol pefomance in MANET. Thus, it is essential to study and analyze vaious mobility models and thei effect on MANET potocols. If we can ecognize the mobility patten of motion of mobile nodes in ou envionment we can customize ou netwok potocols to deal with that existing mobility model. In this pape we intoduce a new method fo classification and patten ecognition of mobility taces into mobility models in mobile Adhoc netwoks. This method uses a simple leaning based classification method to ecognize the existing mobility model in aw mobility taces which was collected fom eal motion of mobile Ad-hoc nodes o mobility taces geneated by mobility simulatos. Ou simulation esults pove ability of ou poposed method to accuately classify given unknown mobility taces into vaious mobility models. Keywods Mobile Ad-Hoc Netwoks, Mobility Models, Mobility Simulato 1. INTRODUCTION A mobile ad-hoc netwok (MANET) is a goup of mobile wieless nodes woking togethe to fom a netwok. Such netwoks can exist without a fixed infastuctue woking in an autonomous manne and evey mobile device has a maximum tansmission powe which detemines the maximum tansmission ange of the device. As nodes ae mobile, the link connection between two devices can beak depending on the spatial oientation of nodes. Mobile ad-hoc netwoks have numeous applications in senso netwoks, disasteelief systems and militay opeations. Some of the netwok constaints in mobile ad-hoc netwoks ae limited bandwidth, low battey powe of Pemission to make digital o had copies of all o pat of this wok fo pesonal o classoom use is ganted without fee povided that copies ae not made o distibuted fo pofit o commecial advantage and that copies bea this notice and the full citation on the fist page. To copy othewise, oepublish, to post on seves o to edistibute to lists, equies pio specific pemission and/o a fee. MC o7 (Mobility 07), Septembe 10-12, 2007, Singapoe. Copyight 2007 ACM $5.00. nodes, and fequent link uneliability due to mobility [1]. In ode to thooughly simulate a new potocol fo an Ad-Hoc netwok, it is impeative to use a mobility model that accuately epesents the mobile nodes that will eventually utilize the given potocol. Only in this type of scenaio it is possible to detemine whethe o not the poposed potocol will be useful when implemented. Cuently thee ae two types of mobility models used in the simulation of netwoks: taces and synthetic models. Taces ae those mobility pattens that ae obseved in eal life systems. Taces povide accuate infomation, especially when they involve a lage numbe of paticipants and an appopiately long obsevation peiod. Howeve, new ad-hoc netwok envionments ae not easily modeled if taces have not yet been ceated. In this type of situation it is necessay to use synthetic models. Synthetic models attempt to ealistically epesent the behavios of mobile nodes without the use of taces. The mobility model is designed to descibe the movement patten of mobile uses, and how thei location, velocity and acceleation change ove time. Since mobility pattens may play a significant ole in detemining the potocol pefomance, it is desiable fo mobility models to emulate the movement patten of tageted eal life applications in a easonable way. Vaious eseaches poposed diffeent kinds of mobility models, attempting to captue vaious chaacteistics of mobility and epesent mobility in a somewhat 'ealistic' fashion. Much of the cuent eseach has focused on the so-called synthetic mobility models that ae not tace-diven. Thee ae seveal synthetic mobility models poposed to liteatue to mimic the eal motion of mobile nodes in eal envionments. It is obvious that the pefomance of mobile Adhoc potocols is affected by the motion patten of mobile nodes. Fo example the aveage link duation in RPGM mobility model is much moe than link duation of Random Walk model because in RPGM mobility model nodes tavel nea each othe most of the time, theefoe the duation time that to neighboing nodes exist in tansmission ange of each othe is much bigge that this duation time in Random Walk mobility model [2,3]. If a netwok potocol can ecognize existing mobility model in a eal envionment using collected mobility taces, it can customize its behavio to deal with that specific motion patten of mobile nodes. Fo example in some potocols which use mobility Pediction methods [4,5] the accuacy of mobility pediction estimato diffes in each mobility models. This mobility pediction estimato can be customized in each of the mobility models to make it moe accuate and decease the mobility pediction inaccuacy.

2 In this pape we intoduce a new method fo classification of collected unknown (mobility model of the tace is unknown) mobility taces fom simulation oeal envionments into specific mobility models. Ou classification method is based on supevised leaning based statistical patten ecognition methods and it is simila to k-neaest neighbo classification method [6]. We use 3 featues of mobility models called mobility metics to classify mobility models. These featues ae: Aveage Degee of Spatial Dependence, Aveage Degee of Tempoal Dependence and Aveage Relative Speed [3]. We use these mobility metics to extact popeties of each of mobility taces and use them as classification paametes. Fo this pupose we designed and implemented a java based softwae called Mobility Analyze to analyze mobility taces and extact vaious mobility metics like aveage link duation, aveage path duation, aveage degee of spatial dependence, aveage degee of tempoal dependence, aveage elative speed and node spatial distibution fom them. Mobility Analyze uses mobility taces geneated by MobiSim softwae [7] in plain text and XML foma analyzes the taces and geneates its analysis esults in XML fomat. This softwae can ecognize the mobility patten of aw mobility taces which ae geneated by MobiSim and classify them in its suppoted mobility models using ou poposed mobility patten ecognition method. The est of the pape is oganized as follows: in section 2 we intoduce suppoted mobility models of Mobility Analyze and a bief explanation of thei featues. In section 3 we intoduce mobility metics we used in ou poposed patten ecognition method and explain ou poposed schemes to classify the mobility taces into suppoted mobility models. Section 4 povides pefomance evaluation of ou poposed method and finally we discuss the conclusion and futue woks. 2. Suppoted Mobility Models In this section we ty to biefly intoduce suppoted mobility models of Mobility Analyze softwae and explain thei paametes, algoithms and popeties and also node spatial distibution of each of mobility models. 2.1 Random Waypoint The Random Waypoint Model was fist poposed by Johnson and Maltz [8]. Soon, it became a 'benchmak' mobility model to evaluate the MANET outing potocols, because of its simplicity and wide availability. The implementation of this mobility model is as follows: as the simulation stats, each mobile node andomly selects one location in the simulation field as the destination. It then tavels towads this destination with constant velocity chosen unifomly and andomly fom [ 0, V Max ], whee the paamete V Max is the maximum allowable velocity fo evey mobile node [9]. The velocity and diection of a node ae chosen independently of othe nodes. Upon eaching the destination, the node stops fo a duation defined by the pause time paamete. If T Pause = 0, this leads to continuous mobility. Afte this duation, it again chooses anotheandom destination in the simulation field and moves towads it. The whole pocess is epeated again and again until the simulation ends. We used MobiSim to geneate mobility taces fo Random Waypoint Mobility Models. Node Spatial Distibution of Random Waypoint Mobility Model with 20 mobile nodes in 500m*500m simulation aea, aveage speed=20m/s, and maximum pause time=10s, fo simulation time=100000sec is shown in figue 1. As we can see in figue 1 spatial node distibution in Random Waypoint is non-unifom and the node density is maximum at the centeegion, wheeas the node density is almost zeo aound the bounday of simulation aea. This phenomenon is called non-unifom spatial distibution poblem in andom waypoint. 2.2 Random Diection The Random Diection model based on simila intuition is poposed by Roye, Mellia-Smith and Mose [10]. This model is able to ovecome the non-unifom spatial distibution poblem. Instead of selecting a andom destination within the simulation field, in the Random Diection model the node andomly and unifomly chooses a diection by which to move along until it eaches the bounday. Afte the node eaches the bounday of the simulation field it stops with a pause time T, then it andomly and unifomly chooses anothe diection to tavel. This way, the nodes ae unifomly distibuted within the simulation field. Node Spatial Distibution of Random Diection Mobility Model with 20 mobile nodes in 500m*500m simulation aea, aveage speed=20m/s, and maximum pause time=10s, fo simulation time=100000sec is shown in figue 2. As we can see spatial node distibution in the simulation field is unifom. In compaison to spatial node distibution shown in figue 1, Random Diection model does not have non-unifom spatial distibution poblem. Figue 1: Spatial Node Distibution in Random Waypoint Mobility Model Figue 2: Spatial Node Distibution in Random Diection Mobility Model

3 2.3 Random Walk The Random Walk model was oiginally poposed to emulate the unpedictable movement of paticles in physics. It is also efeed to as the Bownian Motion. Because some mobile nodes ae believed to move in an unexpected way, Random Walk mobility model is poposed to mimic thei movement behavio [3]. The Random Walk model has similaities with the Random Waypoint model because the node movement has stong andomness in both models. We can think the Random Walk model as the specific Random Waypoint model with zeo pause time. Howeve, in the Random Walk model, the nodes change thei speed and diection at each time inteval. Fo evey new inteval t, each node andomly and unifomly chooses its new diection θ ( fom ( 0,2π ]. In simila way, the new speed follows a unifom distibution fom [ 0, V Max ]. Theefoe, duing time inteval t, the node moves with the velocity vecto ( v( cos( θ (, v( sin( θ ( ). If the node moves accoding to the above ules and eaches the bounday of simulation field, the leaving node is bounced back to the simulation field with the angle of θ ( o π θ (, espectively. This effect is called bode effect [11] oeflection ule. Node Spatial Distibution of Random Walk Mobility Model with 20 mobile nodes in 500m*500m simulation aea, aveage speed=20m/s, fo simulation time=100000sec is shown in figue 3. As we can see spatial node distibution in the simulation field is unifom. is dependent on its velocity at the pevious time slot. Also, a node s velocity is esticted by the velocity of the node peceding it on the same lane of the steet. If two mobile nodes on the same Manhattan lane ae within the safety distance (SD), the velocity of the following node cannot exceed the velocity of peceding node. The inte-node and inta-node elationships involved ae: If node j is ahead of node i in its lane then: Vi ( t + 1) = Vi ( + andom()* ai ( (1) i, D ( SD V ( V ( ) i, j i j t The map used in ou simulations fo Manhattan mobility model is shown in figue 4. Node Spatial Distibution of Manhattan Model with 20 mobile nodes in 500m*500m simulation aea and aveage speed=20m/s, fo simulation time=100000sec is shown in figue 5. As we can see spatial node distibution in the simulation field is not unifom and we have much moe node density in intesections in compaison with lanes. In poposed scheme fo simulation of this model in MobiSim, nodes stop fo a andom pause time between zeo and a specified maximum pause time in intesections and choose anothe destination so the node density in intesections is much moe. We added this pause time to Manhattan Model to simulate behavio of cas in intesections due to the taffic lights and pedestian cossings. Figue 4: Manhattan Mobility Model Map Figue 3: Spatial Node Distibution in Random Walk Mobility Model 2.4 Manhattan The Manhattan mobility model is usually used to emulate the movement patten of mobile nodes on steets defined by maps. It can be useful in modeling movement in an uban aea whee a pevasive computing sevice between potable devices is povided [3]. Maps ae used in this model. The map is composed of a numbe of hoizontal and vetical steets. Each steet has two lanes fo each diection (noth and south diection fo vetical steets, east and west fo hoizontal steets). The mobile node is allowed to move along the gid of hoizontal and vetical steets on the map. At an intesection of a hoizontal and a vetical stee the mobile node can tun lef ight o go staight. This choice is pobabilistic: the pobability of moving on the same steet is 0.5, the pobability of tuning left is 0.25 and the pobability of tuning ight is The velocity of a mobile node at a time slot Figue 5: Spatial Node Distibution in Manhattan Mobility Model 2.5 Gauss-Makov The Gauss-Makov Mobility Model was designed to adapt to diffeent levels of andomness via one tuning paamete [12].

4 Initially each Mobile Node is assigned a cuent speed and diection. At fixed intevals of time n; node s movement occus by updating the speed and diection of each mobile node. Specifically, the value of speed and diection at the nth instance is calculated based upon the value of speed and diection at the st ( n 1) instance and a andom vaiable using the following equation: Node Spatial Distibution of Gauss-Makov Model with 20 mobile nodes in 500m*500m simulation aea and aveage speed=20m/s, fo simulation time=100000sec is shown in figue 7. As we can see the node spatial distibution in this mobility model is simila to Random Walk node spatial distibution. 2 sn = as + (1 α) s + (1 α) sxn 1 2 dn = ad + (1 α) d + (1 α) d x (2) Whee s n and dn ae the new speed and diection of the mobile node at time inteval n; α, whee 0 α 1, is the tuning paamete used to vay the andomness; s and d ae constants epesenting the mean value of speed and diection as n ; s and d ae andom vaiables fom a Gaussian distibution. Totally andom values o Bownian motion ae obtained by setting α = 0 and linea motion is obtained by setting α = 1 [11]. Intemediate levels of andomness ae obtained by vaying the value of α between 0 and 1. At each time inteval the next location is calculated based on the cuent location, speed, and diection of movement. Specifically, at time inteval n, an Mobile node s position is given by the equations: x y n n = x = y + s + s cosd sin d Whee ( xn, yn ) and ( xn 1, yn 1 ) ae the x and y coodinates of the mobile node s position at the n th st and ( n 1) time intevals, espectively, and s and d ae the speed and st diection of the mobile node, espectively, at the ( n 1) time inteval [2,12]. The model paametes ae memoy facto (α), and andom amplitude which is epesented with σ. If we set the memoy facto to 1 the model behavio becomes like Random Walk and if we set it to zeo model behavio becomes like Bownian Motion. Figue 6 shows taveling patten of 5 nodes with a = 0.1, σ = 5and aveage speed=20m/s. Figue 6: Taveling patten of Mobile Nodes in Gauss- Makov Mobility Model (3) Figue 7: Spatial Node Distibution in Gauss-Makov Mobility 2.6 Refeence Point Goup Model In line with the obsevation that the mobile nodes in MANET tend to coodinate thei movemen the Refeence Point Goup Mobility (RPGM) Model is poposed in [13]. One example of such mobility is that a numbe of soldies may move togethe in a goup o platoon. Anothe example is duing disasteelief whee vaious escue cews (e.g., fiemen, policemen and medical assistants) fom diffeent goups and wok coopeatively. In the RPGM model, each goup has a cente, which is eithe a logical cente o a goup leade node. Fo the sake of simplicity, we assume that the cente is the goup leade. Thus, each goup is composed of one leade and a numbe of membes. The movement of the goup leade detemines the mobility behavio of the entie goup. Initially, each membe of the goup is unifomly distibuted in the neighbohood of the goup leade. Subsequently, at each instan each node has a speed and diection that is deived by andomly deviating fom that of the goup leade. The movement in goup mobility can be chaacteized as follows: V θ membe membe ( = V ( = θ leade leade ( + andom()* SDR*max speed ( + andom()* ADR*maxangle SDR is the Speed Deviation Ratio and ADR is the Angle Deviation Ratio. SDR and ADR ae used to contol the deviation of the velocity (magnitude and diection) of goup membes fom that of the leade. So model paametes will be ADR, SDR, initial distance (membes initial distance fom the leade node), and goup size which detemines numbe of goup nodes. We used Random Walk Mobility Model fo motion behavio of leade node in each goup theefoe node spatial distibution in RPGM model is simila to Random Walk Mobility Model because the leade node in each node tavels with Random Walk Mobility Model. (4)

5 Figue 8 shows taveling patten of 2 goups with 5 nodes with aveage speed=20m/s, SDR=0.05, ADR=0.05, using RPGM Mobility Model. D spatial ( i, = RD( v (, v ( ) SR( v (, v ( ). (5) i j The value of D spatial ( i, is high when the nodes i and j tavel in moe o less the same diection and at almost simila speeds. Howeve, D spatial ( i, deceases if the elative diection o the speed atio deceases. As it is ae fo a node s motion to be spatially dependent on a fa off node, we add the condition that i j D (6) i, j ( > c1 R Dspatial( i, = 0, Figue 8: Taveling patten of Mobile Nodes in RPGM Mobility Model 3. Mobility Patten Recognition In this section we intoduce used mobility metics in ou leaning based classification method and ou poposed scheme fo patten ecognition of mobility taces in mobile Ah-hoc netwoks. Befoe fomally defining the metics, we intoduce some basic teminology that will be used late in the pape: 1. V i ( : velocity vecto of node i at time t. 2. V i ( : speed of node i at time t. 3. θ i ( : angle made by V i ( at time t with the Xaxis. 4. ( : acceleation vecto of node i at time t. a i 5. x i ( : X co-odinate of node i at time t. 6. y i ( : Y co-odinate of node i at time t. 7. D i, j ( : Euclidean distance between nodes i and j at t. 8. RD ( a(, b( )) : elative diection (RD) (o cosine of the angle) between the two vectos a (, b () is given by: a(. b( ). a( b( ) 9. SR ( a(, b( )) : speed atio (SR) between the two vectos a min( a(, b( ) (, b () is given by. max( a(, b( ) 3.1 Mobility metics We popose these metics to diffeentiate the vaious mobility pattens used in ou study [3]. The basis of diffeentiation is the extent to which a given mobility patten captues the chaacteistics of spatial dependence, tempoal dependence and geogaphic estictions. In addition to these metics, we also use the elative speed metic that diffeentiates mobility pattens based on elative motion. This metic was fist poposed in [14]. Degee of Spatial Dependence: It is a measue of the extent of similaity of the velocities of two nodes that ae not too fa apat. Fomally, whee c1 > 0 is a constant which will be detemined duing ou expeiments. Aveage Degee of Spatial Dependence: It is the value of D spatial ( i, aveaged ove node pais and time instants satisfying cetain condition. Fomally, D spatial = 1 1 j = 1 D spatial ( i,, (7) P T N N t = i = i + whee P is the numbe of tuples ( i, such that D spatial ( i, 0. Thus, if mobile nodes move independently of one anothe, then the mobility patten is expected to have a smalle value fo D spatial. On the othe hand, if the node movement is co-odinated by a cental entity, o influenced by nodes in its neighbohood, such that they move in simila diections and at simila speeds, then the mobility patten is expected to have a highe value fo D spatial. Degee of tempoal dependence: It is a measue of the extent of similaity of the velocities of a node at two time slots that ae not too fa apat. It is a function of the acceleation of the mobile node and the geogaphic estictions. Fomally, D ( i, ) = RD( v (, v ( )) SR( v (, v ( t )). tempoal i i i i The value of D tempoal ( i, ) is high when the node tavels in moe o less the same diection and almost at the same speed ove a cetain time inteval that can be defined. Howeve, D tempoal ( i, ) deceases if the elative diection o the speed atio deceases. Aguing in a way simila to that fo D spatial ( i,, we have the following condition: t > c Dtempoal ( i, ) 0, (9) 2 = whee c2 > 0 is a constant which will be detemined duing ou expeiments. Aveage degee of tempoal dependence: It is the value of D tempoal ( i, ) aveaged ove nodes and time instants satisfying cetain condition. Fomally, (8)

6 Dtempoal = N T T i= t = t = Dtempoal i t (,, ). (10) P whee P is the numbe of tuples ( i, ) such that D tempoal ( i, ) 0 Thus, if the cuent velocity of a node is completely independent of its velocity at some pevious time step, then the mobility patten is expected to have a smalle value fo D tempoal. Howeve, if the cuent velocity is stongly dependent on the velocity at some pevious time step, then the mobility patten is expected to have a highe value fo D tempoal Relative Speed (RS): We use the standad definition fom physics, i.e. RS( i, = Vi ( V j (. (11) As in the case of D spatial ( i,, we add the following condition: D, j ( 3 = i > c R RS( i, 0, (12) whee c3 > 0 is a constant which will be detemined duing ou expeiments Aveage Relative Speed: It is the value of RS ( i, aveaged ove node pais and time instants satisfying cetain condition. Fomally, N N T i= 1 j = 1 t = 1 RS( i, RS =. (13) P whee P is the numbe of tuples ( i, such that RS ( i, Poposed Classification Method Ou poposed method fo classification of mobility models is divided into 2 main phases: Taining phase and Classification phase. This method is a supevised leaning based classification method Taining Phase: In taining phase we analyze 20 instances of known mobility taces geneated by MobiSim fo each of mobility models and extact values of 3 mobility metics fo each mobility tace. These mobility metics ae: Aveage Degee of Spatial Dependence, Aveage Degee of Tempoal Dependence and Aveage Relative Speed. Then we put them in a thee-dimentional matix and we calculate 3D centoid fo each of the mobility model classes. Theefoe in this phase the method leans mobility metics of each of the mobility models by calculating the 3D centoid of each of mobility model classes Classification Phase In classification phase we get mobility taces with unknown mobility model and attempt to classify each of them into one of the suppoted mobility models. We get 120 unknown taces as classification instances fom each of the suppoted mobility models (20 instances fo each one). Fo each of the unknown taces the method calculates values of 3 mobility metics (Aveage Degee of Spatial Dependence, Aveage Degee of Tempoal Dependence and Aveage Relative Speed) and calculates 3D centoid fo each of them. Then the method calculates Euclidean distance of the centoid of each of the unknown taces to the centoid of each of the mobility model classes (calculated in taining phase). Then the method classifies the unknown tace into the neaest class (lowest Euclidean distance between its centoid and centoids of classes). Using this method each of the unknown taces would be classified into only one of the mobility classes (mobility models). Ou simulation esults show significant pefomance of this method to ecognize the mobility model of all unknown taces into one of the suppoted mobility models classes. 4. Simulation Results We used MobiSim softwae to geneate 20 known and 20 unknown mobility taces fo each mobility model with 20 nodes, fo 10000sec, with aveage speed=40m/s, in a 500*500 simulation egion and the same simulation configuations as we mentioned fo simulations in section 2, then we used Mobility Analyze softwae to calculate mobility metics of each of the mobility taces. These featues (mobility metics) ae oganized in 3 featue vecto (Aveage Degee of Spatial Dependence, Aveage Degee of Tempoal Dependence and Aveage Relative Speed). We consideed c c 100 and c 1 (Equations 6,9,12) fo 1, 3 = calculating these 3 mobility metics. Figues 9, 10, 11 show 2 of the mobility metics and theielation in each of the mobility taces in a two dimensional featue vecto. As we can see mobility metics vay in diffeent mobility models. We discuss each of the mobility models and thei featues as following. In RPGM mobility model nodes move nea each othe as a goup with almost simila speed and diection angle theefoe this mobility model has vey high degee of spatial dependence because thee is high similaity in motion of nodes in a goup. But in compaison with othe mobility models it has lowest elative speed because each of the nodes in a goup chooses a andom speed and diection accoding the speed and diection of the goup leade so the speed and diection of each of goup membes ae almost same as each othe so the value of elative speed fo two of goup nodes is vey low so in aveage the elative speed of this model would be vey low. This model has high tempoal dependence because in each instance of time the motion of a mobile node in this mobility model is simila to its motion in pevious instance of time. The Random Waypoint and Random Diection models almost have same mobility metics because the motion manne in these two mobility models is almost the same. The main diffeence in thei motion manne is: in Random Diection the nodes stop on the egions of simulation aea fo a andom time called pause time, but in Random Waypoint model nodes may stop in any point of simulation egion. This can cause a poblem called non-unifom node spatial distibution in Random Waypoint. This phenomenon causes a small diffeence in aveage elative speed of these 2 mobility models. But othe mobility metics in this mobility models ae almost the same theefoe the only useful mobility metic which can be used to sepaate these to mobility models is elative speed. Because of existing of pause 2 =

7 time in motion of nodes in these 2 mobility models, movements of mobile nodes have low similaity theefoe they have high aveage elative speed. Gauss-Makov and Random Walk almost have simila mobility metics. Both of them have medium aveage elative speed and low aveage spatial dependence. Lack of similaity of speed and diection in motion of nodes caused almost high aveage elative speed and low aveage spatial dependence in these 2 mobility models. Aveage tempoal dependence is the only mobility metics that can sepaate these 2 mobility models. Because of constant speed and diection of a mobile node duing a tansition in Random Walk mobility model this model has vey high aveage tempoal dependence. But in Gauss-Makov mobility models speed and diection of a mobile node changes fequently duing a peiod of time so it has low aveage tempoal dependence. Manhattan mobility models has low aveage elative speed and spatial dependence because nodes in this mobility model almost move with simila speed and angle because they must conside the safe distance between each othe so neighboing nodes would have almost same speed and angle. This model also has low aveage tempoal dependence because of existing negative and positive acceleation in motion of nodes and pause time in intesections and behind the taffic lights. In the taining phase the mobility analyze calculates each of the mobility metics fo each of known mobility taces and calculated a 3D centoid fo each of the mobility models. In the classification phase mobility analyze calculated mobility metics fo each of the unknown given mobility taces and calculates 3D centoid of these 3 mobility metics then it calculated Euclidian distance between each of the calculated centoids to centoids of each of the mobility classes and assigns each tace in neaest class. With this method we used 120 known mobility taces (20 tace fo each model) fo taining phase and classified 120 unknown mobility taces into 6 mobility models in classification phase. The classification esult is shown in figue 12. As we can see ou poposed method can successfully sepaate featues of 6 mobility models and classify unknown mobility taces into ight mobility model classes. Figue 10: Calculated values of 2 mobility metics fo mobility tace samples Figue 11: Calculated values of 2 mobility metics fo mobility tace samples Figue 9: Calculated values of 2 mobility metics fo mobility tace samples Figue 12: Classified mobility taces into 6 mobility model classes

8 5. CONCLUSION AND FUTURE WORKS In this pape we intoduced a new method fo classification and patten ecognition of mobility models in mobile Ad-hoc netwoks. This method uses a simple leaning based classification method to ecognize the existing mobility model in unknown mobility taces collected fom eal motion of mobile Ad-hoc nodes o mobility taces geneated by mobility simulatos. Ou simulation esults show significant pefomance of this method to ecognize the mobility model of all unknown taces into one of the suppoted mobility model classes. Fo the futue woks we ae woking on ou poposed method to make it moe efficient in classification of othe mobility models like Feeway, Pobabilistic Random Walk, and othe mobility models. Also we ae woking on othe mobility metics like Geogaphical Restictions. In addition we ae woking on othe patten ecognition methods like unsupevised leaning based clusteing methods to make ou mobility patten ecognition method moe useful and accuate with suppot of vaious mobility models. 6. ACKNOWLEDGEMENTS The authos would like to thank membes of Shaif Digital Media Lab (DML) fo thei invaluable coopeation. This wok was suppoted by Shaif Advanced Infomation and Communication Technology Cente (AICTC) & Ian Telecommunication Reseach Cente (ITRC). 7. REFERENCES [1] C. Siva Ram Muthy and B.S. Mano Ad Hoc Wieless Netwoks: Achitectues and Potocols, Pentice Hall, [2] Tacy Camp, Jeff Boleng, Vanessa Davis, A Suvey of Mobility Models fo Ad Hoc Netwok Reseach, Wieless Communication & Mobile Computing (WCMC): Special issue on Mobile AdHoc Netwoking: Reseach, Tends and Applications, vol 2, no 5, pp , [3] Fan Bai, Naayanan Sadagopan, Ahmed Helmy, The Impotant famewok fo analyzing the Impact of mobility on pefomance Of Routing Potocols fo Adhoc Netwoks, Elsevie Jounal of Ad Hoc Netwoks, 2003, pp [4] W. Su, S-J. Lee, and M. Gela, Mobility Pediction and Routing in Ad hoc Wieless Netwoks, Intenational Jounal of Netwok Managemen Vol. 11, No. 1, pp.3-30, [5] Zeeshan Hameed Mi, Deepesh Man Shestha, Geun-Hee Cho, Young-Bae Ko, Mobility Awae Distibuted Topology Contol fo Mobile Multi-hop Wieless Netwoks, ICOINS 2006, LNCS 3961, pp [6] Richad O. Duda, Pete E. Ha David G. Stok (2001) Patten classification (2nd edition), Wiley, New Yok [7] S. M. Mousavi, M. Moshef, H. R. Rabiee, A.Dabimoghaddam, MobiSim: a Famewok fo Simulation of Mobility Models in Mobile Ad-Hoc Netwoks, [8] J. Boch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, A pefomance compaison of multi-hop wieless ad hoc netwok outing potocols, in Poceedings of the Fouth Annual ACM/IEEE Intenational Confeence on Mobile Computing and Netwoking(Mobicom98), ACM, Octobe [9] L. Beslau, D. Estin, K. Fall, S. Floyd, J. Heidemann, A. Helmy, P. Huang, S. McCanne, K. Vaadhan, Y. Xu, and H. Yu, Advances in netwok simulation, in IEEE Compute, vol. 33, no. 5, May 2000, pp [10] E. M. Roye, P. M. Mellia-Smith, and L. E. Mose. An Analysis of the Optimum Node Density fo Ad hoc Mobile Netwoks, in Poceedings of the IEEE Intenational Confeence on Communications(ICC), Helsinki, Finland, June [11] C. Bettstette and C. Wagne. The Spatial Node Distibution of the Random Waypoint Mobility Model, in Poc. Geman Wokshop on Mobile Ad-Hoc Netwoks (WMAN), Ulm, Gemany, GI Lectue Notes in Infomatics, no. P-11, pp , Ma 25-26, [12] B. Liang and Z. Haas. Pedictive distance-based mobility management fo PCS netwoks. In Poceedings of the Joint Confeence of the IEEE Compute and Communications Societies (INFOCOM), Mach [13] X. Hong, M. Gela, G. Pei, and C.-C. Chiang, A goup mobility model fo ad hoc wieless netwoks, in Poc. ACM Inten. Wokshop on Modeling, Analysis, and Simulation of Wieless and Mobile Systems (MSWiM), August [14] P. Johansson, T. Lasson, N. Hedman, B. Mielczaek, M. Degemak, Scenaio-based pefomance analysis of outing potocols fo mobile ad-hoc netwoks, in: Intenational Confeence on Mobile Computing and Netwoking (MobiCom_99), 1999, pp