VIEWS: 96 PAGES: 8 CATEGORY: Emerging Technologies POSTED ON: 5/15/2012 Public Domain
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 Towards More Realistic Mobility Model in Vehicular Ad Hoc Network Dhananjay S. Gaikwad, MTech, Mahesh Lagad, MTech(Appear) Assistant professor, dhananjayg63@g mail.com Assistant professor, maheshlag ad@gmail.com Prashant Suryawanshi, MTech(Appear) Vaibhav Maske, MTech(Appear) Assistant professor, s prashant1234@gmail.com . Assistant professor, vamaske@gmail.com Co mputer Engineering Depart ment, HSBPVT‟S GOI, Co llege of Engineering, Kashti. India- 414701. Abstract— Mobility models or the movement patterns of packets need to pass through several nodes to reach nodes communicating wirelessely, play a vital role in the destination [2]. Veh icular Ad Hoc Network simulation-based evaluation of vehicular Ad Hoc (VANETs) are a special case of Mobile Ad Hoc Networks (VAN ETs). Even though recent research has Network (MANETs) and consist of a nu mber of developed models that better corresponds to real world mobility, we still have a limited understanding of the vehicles traveling on urban streets capable of level of the required level of mobility details for communicat ing with each other without fixed modeling and simulating VANETs. In this paper, we infrastructures. VA NETs are expected to benefit propose a new mobility model for VANETs that works safety applications, gathering and disseminating real- on the city area and map the topology of streets and time traffic congestion and routing information, behavior of vehicles at the intersection of roads. Our informat ion services Such as transparent connection model change the speed of nodes after some specific to internet etc [3]. distance in accordance to neighboring nodes that is One critical aspect of VA NETs simulat ion is the according to a desity of nodes, so that this will lead to a realistic situation on the roads. Our model accounts the movement pattern of vehicles, also called mobility various characteristics of VANETs such as traffic lights, models. Mobility model determine the location of acceleration/deceleration due to nearby vehicles, nodes in the topology at any given instant, which attraction points where maximum numbers of vehicle strongly affects network connectivity and throughput tends to go. Using the real and controlled map of street, [5]. There are several mobility models such as we compare our mobility model with the random random pattern, graph constrained commonly used in direction mobility model. Our result demonstrates that popular wireless simu lators such as ns2 [16] by probability of link availability in VAN ETs is more VA NET researchers [4]. But one problem with these sensitive to the vehicles waiting at intersections and models is that they ignores some critical aspects of acceleration/deceleration of vehicles. We also found that probability of link availability suffers at the interse ction the real world traffic such as queuing of vehicles at of the roads; because of some nodes cross the signal road intersection, traffic lights and traffic signs, continue movement in horizontal direction while some acceleration and deceleration according to neighbor nodes change the direction of traveling to vertical. vehicles. Mobility models should reflect as possible as the real behavior of vehicular t raffic on the road Keywords- Vehicular Ad Hoc Network (VANETs), [1]. In this paper, we propose a new mobility model, Mobile Ad Hoc Network (MANETs). which in corporates important features of mobility model on the road, such as presence of traffic light on I. INTRODUCTION the road, node movement is restricted to the road structure and speed changes in accordance to the Ad hoc network is a collection of wireless mobile neighboring vehicle. nodes without any fixed base station infrastructure The rest of paper is organized as follows: and centralized management. Each node acts as both section 2 describes some currently used mobility host and router, which moves arbitrarily and models and some tools for generation of mobility communicates with each other via mu ltiple wireless models. Section 3, describes our proposed mobility lin ks. It is a mult i-hop wireless network, where model and simu lation of our model. Finally Section 4 83 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 concludes the paper. difficult to co llect the real t ime traces of all the nodes. Follo wing section describe mobility models II. M OBILITY MODEL OVERVIEW that generate the trace file, contain the traces of Mobility model reflects the behavior of the nodes vehicles movement. throughout the simu lation time. It shows how the 2.1.1 Mobility model generator for Vehicular Network nodes change their speed and direction in (MOVE): Mobility model generator for Vehicular accountancy to the neighboring vehicles and Network (MOVE) [12] facilitates users to rapidly according to the traffic rule. Fo llo wing are some generate realistic mobility models for VANET important factors those affect the mobility of nodes in simulation with a visualization property. This model VA NETs. works with another micro-simulator traffic model, Street structure: Streets force nodes to called SUMO [13]. MOVE model consists of two main confine their movements to well-defined components: Map Editor and Vehicle Movement Editor. paths. This constrained movement pattern Map Editor is used to create the road topology, which is determines the spatial distribution of nodes either created by manually, automatically or by and their connectivity. Streets can have importing the maps from databases such as TIGER either single or mu ltip le lanes and can allow ((Topologically Integrated Geographic Encoding and either one-way or t wo-way t raffic. Referencing). The Vehicle Movement Editor used for Block size: A city block can be considered the generation of vehicle movement. The output of the smallest area surrounded by streets. The MOVE is a mobility trace file which contains the block size determines the number of intersections in the area, wh ich in turn information on vehicle movement that can be used by determines the frequency with which a network simulator. All the parameter configuration of vehicle stops. vehicle movement is done in a static way. This model Traffic control mechanisms: The most does not consider micro – mobility features. common t raffic control mechanisms at 2.1.2 Street Random Waypoint (STRAW): Street intersections are stop signs and traffic lights. Random Way point (STRAW) is a tool [14] that These mechanisms result in the format ion of generates the mobility patterns with extraction of urban clusters and queues of vehicles at topologies from the TIGER database. It supports for the intersections and subsequent reduction of micro – mobility features of models. STRAW their average speed of movement. implements a complex intersection management using Interdependent vehicular motion: Movement traffic lights and traffic signs. Due to this characteristic, of every vehicle is influenced by the vehicle shows a more realistic behavior when reaching movement pattern of its surrounding at intersection. It includes a traffic control mechanisms vehicles. that force drivers to follow deterministic admission Average speed: The speed of the vehicle control protocol when encountering intersection. determines how quickly its position changes, Drawback of STRAW model is it does not give details which in turn determines the rate of network topology changes. about the traffic flows. Also it does not specify the lane Whenever a mobility model is designed for changing behavior. VA NETs, that model should consider the factors 2.2 Entity mobility model that affect the mobility of node. These types Entity mobility model represents mobile based on the number of nodes considered while node as a random entity wh ich moves randomly over designing the model and the way mobility the observed area, where speed and directions of informat ion is stored. node independent with the neighboring nodes. 2.1 Trace based mobility model 2.2.1 Random Walk mobility mode: Random Walk This type of model [6] is suitable to emulate mobility model is Entity mobility model, in which the real scenarios in MANET and VA NET. Traces mobile node moves from its current location to a new describe the movement of vehicles throughout the location by randomly choosing a direction and speed simu lation. Traces are the best information to find the in which to travel [4]. The new speed and direction mobility patterns of node, if we have traces of long are both chosen from pre-defined ranges, respectively period and involvement of many participants. Traces [min-speed,max-speed] and [0,2*pi] respectively. reflect the movement histories of the nodes in the Each movement in the Random Walk Mobility network. We can expect mobility patterns provided Model occurs in either a constant time interval T or a by them lead to realistic mobility modeling. But the constant traveled distance, at the end of which a new VA NET applications are not widely deployed; there direction and speed are calculated. are fewer traces for evaluation. Another issue related 2.2.2 Random way point model: In Random way point to traces is, the nature of network is decentralized and model [7], mobile nodes move randomly and freely 84 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 without any restrictions. In this model, the destination, phase, node independently selects its new direction speed and direction all are chosen randomly and and speed of movement. Speed and d irection are kept independent of other nodes. The fraction of nodes in constant for the whole duration of node move phase. network remains static for the entire simulation time. 2.3 Group Mobility model The velocity of node is uniformly chosen at random Entity mob ility models represent mult iple from the interval [Vmin, Vmax]. The node moves mobile nodes whose actions are completely towards destination with a velocity v. When it reaches independent of each other. In an ad hoc network, to destination, it remains static for the predefined pause however, there are many situations where it is time and moving again according to same rule. The necessary to model the behavior of mobile nodes as mobility behavior of nodes very much depends on the they move together. For example, a group of soldiers pause time and maximum speed of nodes. The in a military scenario may be assigned the task of parameters to describe a simulation setup of model are searching a particular plot of land in order to destroy Size and shape of the deployment region Q, initial land mines, capture enemy attackers. In order to spatial node distribution Fint (x), static parameter ps, model such situations, a group mobility model is with 0< ps<1, Probability density function fTp (tp) of pause time, Minimum and maximum speed : 0 < Vmin needed to simulate this cooperative characteristic. In ≤ Vmax. this section, we present reference group mobility The components of node distribution fx(x) is models. composed of three distinct components as shown in Reference Point Group Mobility model: The equation. (fx(x)=fs(x)+fp(x)+fm(x) …(1) Reference Point Group Mobility model represents the 2.2.3 Gauss Markov model: Random way-point random mot ion of a group of mobile nodes as well as model generates speed and direction of nodes the random motion of each indiv idual mob ile node independent on previous history. It directly selects within the group. Group movements are based upon speed and direction fro m its predefined range, so this the path traveled by a logical center for the group. It can create a sudden stop and sharp turn problem. is used to calculate group motion via a group motion Gauss Markov model [8] first calculates the speed vector, GM. The motion of the group center and direction of movement for each node. Then completely characterizes the movement of this nodes move with the calculated speed and direction corresponding group of mobile nodes, including their direction and speed. Individual mobile nodes for a period. After that period similar movements randomly move about their own pre-defined begins again. The time that is used in the movement reference points whose movements depend on the in each interval before the change in speed and group movement [11]. As the indiv idual reference direction, is constant. The current speed and direction point move fro m time t to t+1, their locations are related to the previous speed and direction shown by updated according to the group's logical center. Once equation (2) and (3). the updated reference group points, RP (t+1) are s n = α sn -1 + ( 1- α) * s + (1 -α 2) * s xn- 1 ½ (2)calculated, they are co mb ined with a random vector, d n = α d n-1 + (1 -α )* d + ( 1- α2 ) * s dn -1 ½ (3)RM, to represent the random motion of each mob ile node for mo ve individual pe riod time n, s n an d d n -1 ar Wh er e, s n an d d n ar e the valu es o f sp ee d a nd direction about its m ent in the reference point.-1The lengthe the values of speed and direction for movement in the of RM is uniform distributed within a specified period time n-1, α is the constant value in the range [0, 1], s and d are constants representing the mean radius centered at RP (t+1) and its direction is speed and direction, α sn-1 and αdn-1 are variables fro m uniformly distributed between 0 and Pi. a Gaussian distribution. Gauss Markov model overcomes sudden stop and sharp turn problems of 3. PROPOSED MOBILITY M ODEL Random way point model [7]. 2.2.4 Random direction mobility model: Random 3.1 Our proposed model direction mobility model is, bes ides the random We develop our model by considering the waypoint model, p robably the most widely used real scenarios on the roads. We consider several model. This model considers individuals moving on parameters of real t raffic situation on the roads of straight walk segments with constant speed and city. The parameters attraction point, speed variation, optional pauses between the walk segments. In this traffic light are considered. We change the speed of model each node alternates periods of movement nodes after some specific distance according to the (move phase) to periods during which it pauses density of the nodes. We model the signal based on (pause phase) [10]; at the beginning of each move the horizontal traffic time stamps and vertical traffic time stamps. 85 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 Attraction point: In real scenarios, vehicles do not intersection point, to handle the traffic in both the move randomly fro m one point to another. They used direction. to set some fixed destination to reach. We consider Node movement: At the beginning, the nodes are this fact in our proposed model. Vehicles generally distributed along the starting points of the horizontal move in deterministic way. Whenever vehicles enter in and vertical lane. Nodes are allowed to move only the city, they not always move in straight line. They through the predefined paths. Node knows the may change their direction of movement to a specific distance from the origination point or arriving point point according to the importance of that point. Suppose of traffic to the signaling point. The nodes have if the vehicle rider is a student, then he will definitely assigned probability values according to their point move to the college road. Like this if vehicle rider want of interest. Each node calculates the time required to to go restaurant, he will move to the restaurant road. reach at signal. The traffic signal is modeled on the Users move in group‟s towards the attraction points. time stamp basis. At the signal, each node checks its More number of nodes will be around the most next direction of movement according to the attractive point compare to less attractive. probability value and attraction point, i.e. destination In our model we have modeled the attraction point. point as the function of probability value at signaling 3.2 Operation of proposed mobility model point. In our model, each vehicle has assigned some probability values whenever they enter in the In this section, we describe algorith m of movement simu lation area according to their attraction behavior. of mobile nodes of proposed mobility model. The checking of the probability value is done at the Initially, all nodes start from the init ial point on the signaling point. road and moves up to the boundary of the simulat ion Speed variation : Generally whenever vehicles enter area. in the simulation area as city area in our proposed model, their speed do not remains constant 1. Define all the necessary variables signal throughout the simulation t ime and area. The speed time, signal coordinates, number of nodes, of vehicle is changes in according to the neighboring area of simu lation, TURN. vehicles, traffic lights, street layout, and pedestrian 2. Do for all nodes, fro m 1 to n, where n is movement. number of nodes in the simulation. In our proposed model, we change the speed 3. Node movement starts fro m in itial point of the vehicle after some specific distance. So it gives towards the boundary of simulat ion area in a scenarios that a vehicle changes its speed in both directions horizontal and vertical. according to the neighboring vehicles. So when any vehicle co mes closer to already moving vehicle 4. Set distance after which the node speed will for the overtaking, the moving vehicle will decrease change to 15m and speed of node= rando m the speed for some time and overtaking node will increase the speed at that time. Th is speed variation value between {5m/s and 25m/s}. parameter will increase the realis m of our proposed a. Calculate the time required to model. travel given dist by the formu la, Traffic light: Traffic light is for the management of time=d ist/speed. intersection on the roads. In our proposed model, we b. Add time and dist to the total time have modeled the traffic light as a coordinated traffic and total distance of the node light. For that first consider the single horizontal lane. respectively. The light turns green in such a manner that only 5. If node_distance==signal, then traffic along the single lane cross the intersection Check the time of signal and the probability simu ltaneously. Veh icles that need to turn left will go value of node. directly. We have modeled the signal in such that If signal_time==node_time (that means when the traffic light turns red, the vehicles that need signal is red) and p roba bility valu e! = TU R N. to cross the signal will wait other vehicle directly Then wait for the remain ing time till the take turn. signal turns green and go to step 8. Simulation area: In our proposed model, we have Otherwise, cross the signal without wait ing. considered the area of city for the deployment of our A nd t ak e th e tur n a n d go in up w a r d dir ectio n model. In that vehicles enter the simulat ion area on with out sto ppi ng at sig n al a nd g o to ste p 8 . the left side of the road. A vehicle then moves 6. Continues the nodes movement and go to towards the right direction with speed range fro m step 5. 5m/s to 25m/s. There are traffic lights at the 7. While (end of simu lation area). 86 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 8. Take a movement for other nodes and go to node moves towards the end of simu lation area and time for the rando m d irection mob ility model. This is step 2. because of each node takes random direction 9. While (nu mber o f nodes) independent on the previous node and that direction is 10. End not restricted to the particular road topology. But in our 11. Now d istance matrix contains the time and proposed model, the probability of link availability distance values for nodes. decreases only at the signaling point and probability 12. Take the values fro m the distance matrix. value remains high for rest time of simulation. This 13 Calculate the probability of link availability Table 2 and average number of neighbors per node. 14 Co mpare these values with random Parameter Values waypoint, gauss Markov, random direction Simulation time 100 seconds and city section models. Number of nodes 10,20,30,40,50,75 MATLAB version MATLAB 7.8.0.347 15 Show the results. Mobility model characteristics Horizontal and vertical lanes. 3.3 Co mparison of mobility model Randomly assigned between 0 popularity of attraction points to 1. Table 1 MAXSPEED 25 m/s. MINSPEED 5 m/s. Fea tures Random waypoint Our model model Dist anc e aft er that spe ed 10m, 15m. Horizontal No horizontal and Horizontal and chang es and vertical vertical lane present, vertical lane, nodes are lane which shows the road Our proposed model and move randomly around Mobility model of the city. Random direction model. simulation area. Cross point No cross point Cross points are present, show the road would give the actual situation on the roads. intersection. When we increase the number of nodes fro m 20 to Att ra ction No attra ction point Check the probability point of the nodes. 30, the figure 2 shows the results. The results indicate Speed Const ant for som e Change after some that, there is improvement in the probability of link variation time interval specific distance. availability. This is due to the increase in the density Table 1 shows broadly the comparison between our of nodes. proposed mobility model and random waypoint B. Behavior of our mobility model with varying model. number of nodes. 3.5 Simulation results Case 1: This section shows the behavior of our We performed simulation using the proposed mobility model, by varying the number of MATLAB. All nodes position is shown by their x nodes, while keeping the speed constant between and y coordinate values. We are not taking into 5m/s to 25m/s. The figure 3 shows the results. The account the third dimension (z- direct ion) of position. So nodes are assumed to move in t wo dimensional results indicate that the probability value increases planes all the time. All nodes initialized by their with the increase in the nu mber of nodes in the initial position and make them travel to a specified simu lation. This is because the density of nodes destination point. Simu lation parameters are shown in increases number of connections between the nodes. the table 2. Case 2: This section shows the behavior of our A. Varying number of nodes model with varying nu mber of nodes and increasing This section compares the mobility models with the speed of nodes. The figure 4 shows the results different number of nodes in 800mX800 road with the increase in the speed of nodes. The speed of topology. Figure 1 co mpares our proposed mobility nodes is in between 0m/s to 25m/s. The result model with random d irect ion models with 20 indicates that there is imp rovement in probability of numbers of nodes. The results indicate that probability of link lin k availability value for 10 and 20 nodes. But the availability is h igher at the init ial time of simulat ion probability values decrease for 30 and 40 nodes and and it is gradually decreases as time passes i.e. as again increases for 50 nodes. So by comparing results 87 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 in figure 3 and figure 4, we can show that the speed The figure 5 and 6 show the average number of of the nodes play an important ro le during the neighbors per node throughout the simu lation t ime. communicat ion. If we increase the speed of nodes The number of neighbors is being observed for that does not mean that we get the better results. So increased level of density. The average number of neighbors per nodes varies smoothly fro m start to end the performance of routing protocols very much of the simu lation time. At some point in the depends on the speed and density of the nodes. simu lation, the value decreases, Figure 4: probability of link availability versus number of simul ated nodes. Figure 1: Probability of Link Availability versus Time. this is due to the intersection of the road. Our model shows that the number of neighbors decreases only at the road intersections, as some nodes change the direction of traveling. W ith the increase in the density of the nodes, there is increase in the numbers of neighbors per node. This is due to that more nu mber of neighbors per node increase as there are more nodes on the road. Our proposed model gives the better results in terms of average number of neighbors per node. This will give mo re stability to our model. Our model shows better results in all the experiments, while showing the real situation on the road. Figure 2: Probability of Link Availability Versus Time. Figure 5: Average numbers of neighbors per node for 50 nodes Figure 3: Probability of Link Availability versus Number of Simulate d Nodes. Case 3: Average number of neighbors per node :- 88 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 REFERENCES [1]. Harri, J.; Filali, F.; Bonnet, C.; "Mobility models for vehicular ad hoc networks: a survey and taxonomy," Communications Surveys & T utorials, IEEE, vol.11, no.4, pp.19-41, Fourth Quarter 2009 [2]. Azarmi, M.; Sabaei, M.; Pedram, H., "Adaptive routing protocols for vehicular ad hoc networks," Telecommunications, 2008. IST , 2008. International Symposium on, vol., no., pp.825- 830, 27-28 Aug. 2008. [3]. Yufeng Chen; Zhengtao Xiang; Wei Jian; Weirong Jiang; , "An improved AOMDV routing protocol for V2V communication," Intelligent Vehicles Symposium, 2009 IEEE , vol., no., pp.1115-1120, 3-5 June 2009. [4]. http://en.wikipedia.org/wiki/Mobility_model. [5]. Mouzna, J.; Uppoor, S.; Boussedjra, M.; Pai, M.M.M., Figure 6: Ave rage numbers of neighbors per node for 75 "Density aware routing using road hierarchy for vehicular nodes. networks," Service Operations, Logistics and Informatics, 2009. SOLI „09 IEEE/INFORMS International Conference, vol., no., pp.443-448, 22-24 July 2009. [6]. Vetriselvi, V. and Parthasarathi, R., “Trace based mobility model for ad hoc networks,” In Proceedings of the Third IEEE 4. Conclusion and Future Work international Conference on Wireless and Mobile Computing, In this research, we have proposed the new Networking and Communications.WIMOB, IEEE Computer Society, Washington, DC, pp.81-81, 2007. mobility model that covers the city area. In our [7] Christian Bettstetter, Giovanni Rasta and Paolo Santi,; “The proposed model we change the speed of nodes after Node Distribution of the Random Waypoint Mobility Model for some particular distance, in accordance to Wireless Ad Hoc Networks”, IEEE TRANSACT IONS ON MOBILE COMPUTING, VOL. 2, NO. 3, JULY-SEPTEMBER neighboring nodes. We assigned probability values to 2003. the nodes based on the attraction point, where nodes [8] Ariyakhajorn, Jinthana; Wannawilai, Pattana; Sathitwiriyawong, Chanboon; , "A Comparative Study of Random most likely to move. This would leads to the actual Waypoint and Gauss-Markov Mobility Models in the Performance scenarios on the road. Evaluation of MANET," Communications and Information Through simu lation we have shown that our Technologies, 2006. ISCIT '06. International Symposium on , vol., no., pp.894-899, Oct. 18 2006-Sept. 20 2006. model performs better than random direction model [9] Boundless mobility model, http://www- in terms probability of lin k availab ility. We public.itsudparis.eu/~gauthier/MobilityModel/mobilitymodel.ht compared our proposed model with rando m direction ml #Boundless. [10] Zhi Ruxin; Gao Fei; Yang Jie; "Nonuniform Property of model through the simu lation. Fro m the simulation Random Direction Mobility Model for MANET, " Wireless we got the result that shows that probability of lin k Communications, Networking and Mobile Computing, 2009. WiCom '09. 5th International Conference on , vol., no., pp.1-4, 24- availability decreases at the traffic signal point. Th is 26 Sept. 2009. is because of the vehicle either waits or takes turn [11] Ng, J.M.; Yan Zhang; , "Reference region group mobility and change the direction of traveling. Our result model for ad hoc networks," Wireless and Optical Communications Networks, 2005. demonstrates that probability of link availab ility in [12] F. Karnadi, Z. Mo, K.-C. Lan,”Rapid Generation of Realistic VA NETs is more sensitive to the vehicles wait ing at Mobility Models for VANET ”, Poster Session, 11th Annual intersections and acceleration/deceleration of International Conference on Mobile Computing and Networking (MobiCom 2005), August 2005. vehicles. We have tested our model by increasing [13]SUMO, speed of the nodes. We found that the connectivity http://sourceforge.net/apps/mediawiki/sumo/index.php?title=Main among the nodes is very much depends on the speed _Page. of the nodes. We also found that performance of [14] D. Choffnes, F. Bustamante, “An Integrated Mobility and Traffic Model for Vehicular Wireless Networks”, 2nd ACM mobility model is depends on both the speed and Workshop on Vehicular Ad Hoc Networks (VANET 2005), density of the nodes. Thus our model tries to depict September 2005. the realistic scenarios on the real road. [15] CANU Project Home Page, http://canu.informatik.uni- In future, we will try to extend our model by stuttgart.de. [16] http://www.isi.edu/nsnam/ns/index.html. considering the overtaking parameter into account. [17] http://pcl.cs.ucla.edu/projects/glomosim/. We will try to run our model on a two lane of the [18] Gainaru, A.; Dobre, C.; Cristea, V.;"A Realistic Mobility road. Model Based on Social Networks for the Simulation of VANET s," Vehicular Technology Conference, 2009. VT C Spring 2009.IEEE th 69 ,vol. no., pp.1-5, 26-29 April 2009. [19] M. T reiber, A. Hennecke, D. Helbing,;”Congested traffic states in empirical observations and microscopic simulations”, Phys. Rev. E62, Issue 2, August 2000. [20] Bhandari, Shiddhartha Raj; Lee, Gyu Moung; Crespi, Noel; , "Mobility Model for User's Realistic Behavior in Mobile Ad Hoc Network," Communication Networks and Services Research Conference (CNSR), 2010 EighthAnnual , vol., no., pp.102-107, 11-14 May 2010. 89 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 3, March 2012 AUTHORS PROFILE My self Dhananjay Gaikwad, completed Mtech from National Institute of Technology, surat (India) 2011. Since july 2011, I am working as Assistant professor in Parikram college of engineering, kashti. My papers have been published in Springer and IEEE explorer. My self Mahesh Lagad, pursuing Mtech (MS) from University of Pune (India). I have two year industrial experience, joined the college in july 2011. My paper has been published in International conference. I, Prashant suryawanshi currenty doing Mtech, from Hyderabad (India). I have total six year Industrial plus academic experience. I, Vaibhav maske currenty doing Mtech, from Hyderabad (India). I have total four year teaching experience. 90 http://sites.google.com/site/ijcsis/ ISSN 1947-5500