Towards More Realistic Mobility Model in Vehicular Ad Hoc Network by ijcsiseditor


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									                                                      (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                 Assistant professor, maheshlag

                Prashant Suryawanshi, MTech(Appear)                      Vaibhav Maske, MTech(Appear)
        Assistant professor, s .                 Assistant professor,

                                                  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

                                                                                                 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

                                                                                                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.

                                                                                                     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
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
                                                                              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.
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).

                                                                                                  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
       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.
                                                                          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
                                                                          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

                                                                                                               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

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 :-

                                                                                                     ISSN 1947-5500
                                                   (IJCSIS) International Journal of Computer Science and Information Security,
                                                   Vol. 10, No. 3, March 2012

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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       
considering the overtaking parameter into account.
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
                                                                  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.

                                                                                                 ISSN 1947-5500
                                               (IJCSIS) International Journal of Computer Science and Information Security,
                                               Vol. 10, No. 3, March 2012


                 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.

                                                                                          ISSN 1947-5500

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