A Survey on mobiliy Models & Is Applicaions Prof. Vikas Kumar Jain 1, Prof. Raju Sharma 2, Prof. Bhavana Gupa 3 1,2,3 Compuer Science & Engineering, CIST Absrac In his paper, we survey he curren scenario available in he field of wireless ad-hoc nework. The performance of mobile ad-hoc nework (MANET) applicaion depends on several parameers like no. of nodes, node densiy, communicaion raffic paern, communicaing range of a node, rouing proocol, baery power of a node, mobiliy ec. Ou of hese mobiliy plays an imporan role. There are several ypes of Mobiliy model like Random Way Poin (RWP), Mobiliy models wih emporal dependency, mobiliy model wih spaial dependency. We provide a discussion on he exising mobiliy models, heir definiions and heir limiaions along wih he simulaion done so far in he field of i. Keywords- MANET, Mobiliy Models- RWP, RPGM, Gauss Markov Mobiliy Model ec. I. INTRODUCTION Wireless nework has become very popular in he compuing indusry. Wireless nework are adaped o enable mobiliy. There are wo variaions of mobile nework. The firs is infrasrucure nework i.e. a nework wih fixed and wired gaeways. The bridges of he nework are known as base saions. A mobile uni wihin he nework connecs and communicaes wih he neares base saion, wihin he communicaion radius. Applicaion of his nework includes office WLAN. The second ype of nework is infrasrucure less mobile nework commonly known as AD-HOC nework. A Mobile Ad-Hoc Nework (MANET) is a self-configuring nework of mobile nodes conneced by wireless links, o form an arbirary opology. The nodes are free o move randomly. Thus he nework's opology may be unpredicable and may change rapidly. Ad hoc wireless nework can be deployed quickly a anyime and anywhere as hey do no required fixed infrasrucure seup. Hence, hese feaures make ad-hoc nework suiable for he many applicaions like mobile classroom, balefield communicaion, disasers relief, Museum ouring and emergency siuaions like naural disasers, miliary conflics, and emergency medical siuaions ec. Since mos of he MANETs are no deployed because of heir deploymen cos is very high so lo of research work is simulaion based and in mos of he simulaion work RWP Mobiliy model is used because of he simpliciy. 1.1 Mobiliy is an imporan parameer In all he MANET applicaion nodes can move from one locaion o anoher so here is mobiliy and if we sudy abou all hese mobiliy paerns hen we can see differen mobiliy paern and scenarios have been generaed in hese MANET applicaion hence mobiliy plays an imporan role in deermining he performance of MANET applicaion [1]. A mobiliy model, used in a simulaion mus be capable o show he same movemen paern of he nodes, as given in a race file for a real life applicaion. 1.2 Mobiliy Models and Is Need Mobiliy model describe he movemen paern of mobile nodes like how heir locaion, velociy, direcion and acceleraion will change wih respec o ime. Since movemen paerns may play a significan role in deermining proocol performance, i is desirable for mobiliy models o generae he movemen paern of argeed real life applicaions in a reasonable way. Oherwise, he observaions made and he conclusions drawn from he simulaion sudies may be misleading. For example, he nodes in Random Waypoin model behave quie differenly as compared o nodes @IJRTER-2016, All Righs Reserved 35
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) moving in groups [3]. Therefore, here is a need for developing a deeper undersanding of mobiliy models and heir impac on proocol performance. In his paper a discussion has been done on differen ypes of mobiliy models like Random Way Poin mobiliy model, Mobiliy Model wih Temporal dependency, Mobiliy Model wih spaial dependency. Furher, a survey on exising work done in he same field has been proposed. II. MOBILITY MODELS Mobiliy models are used o describe he saring locaion of he nodes, heir velociy direcion, velociy range and pause ime of he nodes. Based on mobiliy characerisics mobiliy models can be classified as, random way poin mobiliy model, mobiliy model wih spaial dependency, mobiliy model wih emporal dependency, mobiliy wih geographic resricions. 2.1 Random Way Poin Model (RWP): Because of he simpliciy of RWP model, i is one of he mos frequenly used mobiliy model in MANET simulaion. Also, Nework Simulaor (NS-2) provide he sedes ool o generae he node race of he of he Random Waypoin model. In his mobiliy model mobile nodes move randomly and freely wihou any resricions. Firs each mobile node selecs one locaion from he simulaion area as he desinaion. The node ravels owards he desinaion wih consan velociy chosen uniformly and randomly from [Vmin,Vmax]. Upon reaching he desinaion, he node sops for a duraion, defined by he pause ime parameer Tpause. This pause ime may be a consan value or i may follow a kind disribuion. Afer he pause ime, node again chooses anoher poin as a desinaion and chooses a velociy from he given velociy range and move owards i. The process is repeaed again and again unil simulaion ends. In RWP model, velociy range [Vmin,Vmax] and Tpause are he wo key parameers ha deermine he mobiliy behavior of he nodes. By varying hese wo parameers RWP model can generae various scenarios. Fig-1: Movemen of nodes in Random Way Poin Model 2.1.1 Limiaion of random way poin mobiliy model Though because of he simpliciy of implemenaion and analysis, RWP is widely used mobiliy model in mos of he MANET simulaion, however i is inefficien o capure mobiliy characerisics of real life scenarios including emporal dependency, spaial dependency and geographical resricions. a. Temporal dependency: In Random Waypoin, he velociy of mobile node is a memory less random process, i.e., he velociy a curren ime is independen of he previous velociy. This may cause o sharp urn, sudden sop and sudden accelerae. However, in many real life scenarios he speed of vehicles and pedesrians will accelerae incremenally and change heir direcion smoohly. @IJRTER-2016, All Righs Reserved 36
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) b. Spaial Dependency of Velociy: In Random Way poin, he mobile node can move independenly of oher nodes. However, in some scenarios including balefield communicaion, rescue operaions and museum ouring, he movemen paern of a mobile node may be influenced by cerain specific 'leader' node in is neighbourhood. c. Geographic Resricions of Movemen: In Random Way poin, he mobile nodes can move freely wihin simulaion field wihou any resricions. However, in many realisic cases, especially for he applicaions used in urban areas, he movemen of a mobile node may be bounded by obsacles, buildings, srees or freeways. Random Waypoin model fails o represen some mobiliy characerisics likely o exis in MANET. Random Way Poin model was insufficien o capure he all hese mobiliy characerisics hence oher mobiliy models have been proposed. 2.2 Mobiliy Model wih Spaial Dependency: In he RWP model, a mobile node moves independenly of oher nodes, i.e., he locaion, speed and movemen direcion of mobile node are no affeced by oher nodes in he neighborhood. While in real life his is no always possible, for example, on a freeway o avoid collision, he speed of a vehicle canno exceed he speed of he vehicle ahead of i. Also in some oher MANET applicaions like disaser relief and balefield communicaions eam collaboraion is required and eam members ry o follow heir eam leaders. Tha means, mobiliy of a node could be influenced by oher neighboring nodes. Such behavior of mobile nodes can be modeled by a group mobiliy model. 2.2.1 Reference Poin Group mobiliy Model (RPGM): In RPGM mobiliy model, nodes move in o he groups. Each group has a logical cenre called Group Leader, which deermines group s moion behavior. Iniially, each member of he group is uniformly disribued in he neighborhood of he group leader. Subsequenly, a each insan, every node has a speed and a direcion ha is derived by randomly deviaing from he Group Leader. Fig-2: Node movemen in RPGM mobiliy model a ime 0 and +1 [6] As shown in fig-2 he movemen of he group member i a ime insan is given as: V i = V group + RM i Where he moion vecor RM i is a random vecor deviaed by group member i from is own reference poin. The vecor RM i is an independen idenically disribued (i.i.d) random process whose lengh is uniformly disribued in he inerval [0, r max ] where r max is he maximum allowed disance @IJRTER-2016, All Righs Reserved 37
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) deviaion and direcion is uniformly disribued in he inerval [0,2*π ] and V group is he velociy of group leader. In RPGM model, he vecor RM i deermines how much he moion of group members deviae from heir leader. So, i s no possible o generae he various mobiliy scenarios wih differen levels of spaial dependency, by simple adjusmen of model parameers. So, a modified version of RPGM model is proposed. The movemen can be characerized as follows: V member θ member = V leader + random() SDR max _speed = θ leader + random() ADR max _angle Where 0 < SDR, ADR < 1. SDR is he Speed Deviaion Raio and ADR is he Angle Deviaion Raio. V i = Velociy vecor of node i a ime. θ i = Angle made by V i a ime wih he X-axis. 2.3 Mobiliy Model wih Temporal Dependency: In mos of he real life scenarios, node velociy, acceleraion and rae of change of direcion direcly depends on ime. Hence, curren velociy of a mobile node a a ime depends on is previous velociy. This mobiliy characerisic is called emporal dependency. However, he RWP mobiliy model is unable o capure his behavior. 2.3.1 Gauss Markov Mobiliy Model: The Gauss-Markov Mobiliy Model was firs inroduced by Liang and Haas [8]. This model capures he velociy correlaion of a mobile node in ime and represens random movemen wihou sudden sops and sharp urns. A fixed inervals of ime movemen occurs by updaing he speed and direcion of each node. Afer each ieraion, he new parameer values are calculaed depending on he curren speed and direcion and on a random variable. In his he velociy of a node a any ime has been given as: V x x = α V 1 V y = α V y 1 + (1 α) V x + σ x (1 α 2 ) x W 1 + (1 α) V y + σ y (1 α 2 y ) W 1 Where V = [V x, V y ] and V 1 = [V x 1, V y 1 ] are velociies of node a ime and (-1) respecively, α = [α x, α y ] is memory level, V = [V x, V y ] is represening mean, σ = [σ x, σ y ] is sandard deviaion and W 1 = [W x 1, W y 1 ] is uncorrelaed Gaussian process wih mean zero and variance. Based on hese equaion Liang and Haas observe ha he velociy of a node a ime depends on he velociy a ime insan (-1). Therefore, he Gauss-Markov model is a emporally dependen mobiliy model whereas he degree of dependency is deermined by he memory level parameerα. This parameer also represens he randomness presen in Gauss Markov mobiliy model. By adjusing his parameer Liang and Haas has sae ha his model can be applied o various scenarios. 1. If he Gauss-Markov Model is memory less, i.e., α = 0, hen V x = V x + σ x x W 1 V y = V y + σ y y W 1 @IJRTER-2016, All Righs Reserved 38
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) Means velociy of a node a any ime slo is deermined by he drif velociy and Gaussian random variable. 2. If he Gauss-Markov Model has srong memory, i.e., α = 1, hen V x x = V 1 V y y = V 1 Means he velociy of mobile node a ime slo is exacly same as is previous velociy. 3. If Gauss Markov mobiliy model has some memory i.e. 0< α <1, hen he velociy a curren ime slo depend on boh, is velociy a he ime (-1) and on Gaussian random variable W. As he value of parameer α increases he velociy a curren ime will be more influenced by he velociy a previous ime slo. 2.4 Mobiliy Model wih Geographical Resricions: In mos real life applicaions, node s movemen is influenced by he many obsacles. In paricular, he moions of vehicles are bounded o he freeways or local srees in he urban area, and on campus he pedesrians may be blocked by he buildings and oher obsacles. Hence o capure all hese scenarios wo mobiliy models Free Way mobiliy model and Manhaan mobiliy model are used. In hese mobiliy models movemen is resriced by some geographical resricions. 2.4.1 Free Way Mobiliy Model: This model requires a map o guide he node movemen. This model describes abou freeway scenarios where roads are long sraigh wih few number of urns. In his mobiliy model nodes are free o move along wih is road and he velociy of a node is emporally dependen on he previous velociy. Fig-3: Map used in Freeway Mobiliy Model [6] 2.4.2 Manhaan Mobiliy Model: This mobiliy model also requires a map for he node movemen bu he map is composed of a number of horizonal and verical srees. Each sree has wo lanes for direcion. The nodes are no allowed o change heir lane in middle of i. In his model also nodes have emporal dependency as well as spaial dependency. @IJRTER-2016, All Righs Reserved 39
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) Fig-4: Map used in Manhaan mobiliy model [6] III. RELETED WORK & ANALYSIS Mos of he work has been done in he direcion o see he effecs of mobiliy on he various rouing proocols. Also some work has been done for he generaion of realisic mobiliy race, so ha i can be used for he simulaion purpose. In [4], auhors have sudied abou he performance of hree widely used rouing proocols, Desinaion Sequenced Disance Vecor (DSDV), Ad-hoc On-demand Disance Vecor (AODV) and Dynamic Source Rouing (DSR) under differen mobiliy models like RPGM, RWP, Gauss Markov and Manhaan mobiliy models. This work confirms ha he choice of mobiliy model maers and he performance ranking of rouing proocols depend on he node speed and he mobiliy involve in he nework, since mobiliy cause for he link failure and each rouing proocol reacs differenly during link failure. Alex Aravind and Hassan Tahir [5] have developed a sofware ool called RLMobiGen o design a realisic mobiliy model. Since, all he mobiliy models canno be implemened paricularly a mobiliy model involving realism a very challenging ask o implemen i. Using his sofware ool, desired mobiliy of he nodes in he sysem can be generaed and analyzed, and hen he race can be expored o be used in he performance sudies of proposed algorihms or sysems. In [6] auhor has observed ha here is no clear winner among he rouing proocols because differen mobiliy models give differen ranking of he rouing proocols. For his analysis auhor had designed a framework, which mainly focuses on mobiliy characerisics such as average degree of spaial dependency, average degree of emporal dependency and average relaive speed of nodes. Also, he framework define abou he conneciviy graph merics having a se of parameers like average number of link changes, average link duraion and average pah availabiliy. On hese parameers any of he rouing proocol can be compared under he given mobiliy model. In [3] comparison of hree rouing proocols DSDV, AODV and DSR have done in hree differen real life scenarios in order o ge significan resuls closer o he realisic scenarios. These scenarios are: conference room where he area has been divided ino hree zones, speaker zone, audience zone and enrance zone, even coverage are where people change heir posiions frequenly for example a group of reporers covering a poliical even and disaser area where several rescue groups works ogeher. In ad-hoc wireless nework, nework pariioning is anoher big issue [7]. I causes disrupion of he ongoing roues, which affecs he nework performance severely. By exploiing he group mobiliy one can predic he fuure nework pariioning and can minimize he disrupion of roues. In real life, here are many siuaions where here are several obsacles which come in beween he way of mobile nodes. For example, Low-power radios used for indoor communicaion canno propagae signals hrough walls, doors, and oher obsacles in a building, wihou severe aenuaion. Similar condiions may exis in an oudoor scenario, where objecs in he errain, such as buildings, cars, ec. may shadow radio ransceivers. In order o ge significan resuls in a @IJRTER-2016, All Righs Reserved 40
Inernaional Journal of Recen Trends in Engineering & Research (IJRTER) simulaion claiming o be realisic, obsacles o radio propagaion should be modelled. Hence, auhors have inroduced hree realisic scenarios, conference room, disaser area and even coverage o es he proocols. IV. CONCLUSION I is very imporan o use he bes fi mobiliy model ha can produce simulaion resuls ha are reliable boh qualiaively and quaniaively. As menioned in he previous secion, researcher s need o carefully examine he models and he mehod wih which he models are used in a simulaion sudy o avoid inerjecing sources of error. I is highly desirable o make simulaion sysems as realisic as possible wihin he consrains of complexiy and cos. One approach is o use racedriven simulaion, which use real races o rigger evens in a simulaion. Such an approach usually requires large amoun of measured daa. A cheaper bu equally efficien way of achieving his is o build synheic models afer real daa races so ha hey generae synheic/simulaed races similar o a real race. V. FUTURE WORK To ge he more accurae resul of simulaions i is imporan o propose a mehod o answer abou he bes fi mobiliy model for he given race. This will cerainly increase he accuracy of resuls obained during simulaion. REFERENCES 1. Olafur Helgason, Sylvia T. Kouyoumdjieva and Gunnar Karlsson "Does Mobiliy Maer?" in IEEE/IFIP WONS The Sevenh Inernaional Conference On Wireless On-Demand Nework Sysems and Services,2010. 2. J. Geeha and G. Gopinah, "Ad Hoc Mobile Wireless Neworks Rouing Proocols A Review", Journal of Compuer Science, Science Publicaion, 2007. 3. P. Johansson, T. Larsson, N. Hedman, B. Mielczarek, and M. Degermark, Scenario-based performance analysis of rouing proocols for mobile ad-hoc neworks, in Inernaional Conference on Mobile Compuing and Neworking (MobiCom'99), 1999, pp. 195--206. 4. T. Valenina, S. Mirjana and R. B. Slavica," MANET Rouing Proocols vs. Mobiliy Models: Performance Analysis and Comparison", in Proceeding of he 9h WSEAS Inernaional Conference on APPLIED INFORMATICS AND COMMUNICATIONS (AIC 09). 5. Alex Aravind and Hassan Tahir,"Towards Modeling Realisic Mobiliy for Performance Evaluaions in MANET", ADHOC-NOW 2010, LNCS 6288, pp. 109,122, 2010. 6. F. Bai, N. Sadagopan and A. Helmy,"Imporan: a framework o sysemaically analyze he impac of mobiliy on performance of rouing proocols for ad hoc neworks ", in Proceedings of IEEE Informaion Communicaions Conference (INFOCOM 2003), San Francisco, Apr. 2003. 7. K. H. Wang and Li Baochun,"Group Mobiliy and Pariion Predicion in Wireless Ad-Hoc Neworks", In IEEE Inernaional Conference on Communicaions, Vol. 2 (Augus 2002), pp. 1017-1021. 8. B. Liang, Z. J. Haas, "Predicive Disance-Based Mobiliy Managemen for PCS Neworks", in Proceedings of IEEE Informaion Communicaions Conference (INFOCOM 1999), Apr. 1999. 9. J. Philo, O. Hidekazu and W. Yong, "Energy-Efficien Compuing for Wildlife Tracking: Design Tradeoffs and Early Experiences wih Zebra Ne", in Proceedings of he 10h inernaional conference on Archiecural suppor for programming languages and operaing sysems, Vol. 37, No. 10. (Ocober 2002), pp. 96-107. 10. Zebra Ne Movemen, Yong Wang and Pei Zhang and Ting Liu and Chris Sadler and Margare Maronosi," Movemen Daa Traces from Princeon Zebra Ne Deploymens", CRAWDAD Daabase, hp://crawdad.cs.darmouh.edu, year 2007. @IJRTER-2016, All Righs Reserved 41