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									    Link analysis on Indoor and Outdoor environment for Mobile Ad Hoc
     Network using Random Way point Mobility Model and Manhattan
                              Mobility Model
                              Ibrahim khider ,Prof.WangFurong.Prof.YinWeiHua

  Ibrahim khider,Huazhong University of Science and Technology Communication Software and Switch
             Center,Dept of Electronic and Information Systems,Wuhan 430074,P.RChina
        E_Mail: Ibrahimchina2008@yahoo.com,WangFurong@mail.hust.edu.cn,yinyinwh@163.com

Mobility and connectivity metrics in Mobile Ad hoc NETwork (MANET) is the most important topic. Each
MANET mobility and connectivity metrics has its own strengths and weakness. It turns out the impact of
mobility model has become a critical issue in the research of performance comparison. In this paper, we provide
an a study of the link analysis in indoor and outdoor environment in which mobile nodes moves according to
random waypoint mobility model (RWP) and Manhattan mobility model (MH) respectively. The analysis
includes the mobility and connectivity metrics in environment model. We provide in-depth investigation such as
the impact of speed, number of nodes and simulation time on link, and how parameters of combined mobility
model influence link dynamics. Our study about the link analysis gives us an insight into the network topology
dynamics of MANET, and it provides us the basis of further analysis of MANET such as network connectivity.
Simulation results verify our analysis of the link of MANET in indoor and outdoor environment.

Keyword: mobility model, mobility metrics, connectivity metrics, environment, Mobile Adhoc Networks,

1 INTRODUCTION                                              particular environments in the realistic world are
A Mobile Ad hoc NETwork (MANET) is considered               studied. Accordingly, two environment-aware
to be an autonomous system of self-organized mobile         mobility models are introduced and simulated. The
nodes without relying on any infrastructure. Node           Random Waypoint model is used to model the
mobility is one of the key characteristics of MANET,        movement in buildings in the simulation area. The
and it is also one of the critical factors that have        Manhattan model can be used to construct streets
significant influence on the performance of MANET           such as in a city area. The remainder of this paper is
protocols,     mainly      the    routing     protocols.    organized as follows. The two mobility models
Conventional mobility models proposed for MANET             (Manhattan and RWP) are overviewed in section2
can be classified into two categories: Entity model         and 3 respectively. Section4 depicts a combine
and Group model. Entity models are used to                  mobility model in indoor and outdoor environment.
represent the movement of an individual mobile node.        Section 5 describes mobility and connectivity
Among Entity models, the Random Waypoint model              metrics Description of the intensive simulations and
(RWP) [1] is the most popular model used in this            a study of the results are given in section 6 and 7
field. However, the interaction among the mobile            respectively and the last section presents the
nodes cannot be reflected by Entity models. Group           conclusion.
models are therefore proposed. A typical mode is the        2-Random Waypoint
Reference Point Group Mobility (RPGM) model [2].            Random Way Point mobility model (RWP) [3] [4]
A major drawback of conventional models is that             [5] [6] is a simple, widely used, model in the many
some environment factors such as spatial constraints,       simulation studies of ad hoc routing protocols. In this
speed limits, etc are ignored. Street traffic system        model each node is assigned an initial position
could be an example environment. Cars are moving            uniformly distributed within a region (rectangular
along the roads and choose one way out if a junction        region). Then, each node chooses a destination
is met. The people follow the routes to building,           uniformly inside the region, and selects a speed
spend some time there then go out from one of the           uniformly from [minspeed, maxspeed] independently
room exits. Moreover, in some certain environment           of the chosen destination. That means the
such as arts exhibition, the destinations of visitors are   distributions of nodes’ speeds and locations are
not random, but more or less deterministic in that          stationary. To avoid the transient period from the
they always visit some places more attractive to them.      beginning, one solution is to choose the nodes’ initial
These mobility scenarios cannot be handled properly         locations and speeds according to the stationary
by most of existing models. In this paper, two              distribution; another one is to discard the initial time

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period of simulation to reduce the effect of such
transient period on simulation results. The node then              Current     State/
moves toward the chosen destination with the                                            MH state               RWP state
selected speed along a straight line starting from          Condition
current waypoint. After reaching the destination, the
node stops for duration called “pause time”, and then
repeats the procedure. All nodes move independently         Condition indoor            RWP                    RWP
of each other at all times.
3- Manhattan Mobility Model                                 Condition outdoor           MH                     MH
The MH model is used to emulate the nodes
movement on streets defined by maps [4] [5]. The             Fig.1 State Transition table of Random Waypoint
map is composed of a number of horizontal and                         and Manhattan Mobility Model
vertical streets. Each street has two lanes, one in each
direction (North and South for vertical streets, and
East and West for horizontal ones). Each node is
only allowed to move along the grid of horizontal
and vertical streets. At an intersection of horizontal                                                              State
and vertical streets, a mobile node can turn left, or         Condition                 Indoor
right, or go straight with probabilities 0.25, 0.25, and                            environment
0.5, respectively. The speed of a mobile node is                                        RWP
temporarily dependent on its previous speed If two
mobile nodes on the same freeway lane are within
the Safety Distance (SD), the velocity of the
following node cannot exceed the velocity of                         Outdoor
preceding node Mobility models capture the
geographic restrictions. The speed of a node s (t) is
updated according to: s (t + 1) = min (Smax, max (0,
s (t) + a (t) * X)) where X Uniform [−1, 1], and a (t)
is Acceleration Speed.                                                                   Outdoor                      Action
4- DESCRPTION OF COMBINED MOBILITY MODEL                        Transition              Manhattan

This section introduces a combined model of
Manhattan and Random Waypoint, the movements
of a node switch from one mobility model
(Manhattan or Random Waypoint) to another based
on its location in the network. When the nodes are on
the street, they move as Manhattan mobility model           Fig.2 State Transition Diagram of Random Waypoint
movement pattern, when they are located in the                         and Manhattan Mobility Model
building, they will move as Random Waypoint
model. The movement nodes are divided in two                In this paper, we limit the study to an urban area
groups depending on their speed a “pedestrian”              modelized by a Manhattan and RWP mobility model.
group with a low speed and a “vehicular” group with         The area is wrapped around North-South and West-
a higher speed. The pedestrian group of users is            East and the grid is composed of 3 by 3 buildings.
moving with a normal distributed speed with a mean          The buildings are 300x300 m and the street has two
of 3 km/h and a standard deviation of 0.3 km/h [4].         opposite lane, the distance between lane 1 m and the
The vehicular group of users has also a normal              width of lane 6 meter. Fig.2 shows the layout of
distributed speed but with a mean of 50 km/h and a          indoor and outdoor environments in urban area and
standard deviation of 2.5 km/h. At each cross-road,         the movement of nodes. Fig.3 shows the movement
users of both groups have can either continue straight      of nodes in simulation area using manhattan and
with the probability Pr (straight) = 0.5 or turn            Random Waypoint mobility models.
left/right with the probability Pr (right) = Pr (right) =
0.25.To represent the movement of mobile nodes in
outdoor environment (streets) and indoor
environment (buildings) we have used Finite State
Machine (FSM) to explain the movement of mobile
nodes by using state diagram and state transition
table as shown in fig.1 and TABLE.1, .

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                 Outdoor environment
                                                                                                ⎧ T X (i, j , t )
                                                                                                ⎪∑t =1 LC (i. j ) ifLC ≠ 0
 Indoor                indoor                  Indoor                                      LD = ⎨                             ........... (4)
environment           environment                                                               ⎪
                                                                                                ⎩∑t =1 X (i, j , t )otherwise

                                                                  Outdoor environment
                                                                                        3- Route based Metrics a metrics listed bellow used
 Indoor               Indoor                   RWP Indoor                               to measure routing protocol performance: -
environment           environment              environment                                -Number of link change LC (i, j): Number of link
                                                                                        changes for a pairs of nodes i and j is a number of
                                                                                        times the link between them down to up. Average
                                                                                        number of link changes LC (i, j) averaged over all
                                                                                        node pairs is given by:
Indoor                 Indoor                   Indoor
                      environment              environment
                                                                                              LC (i, j ) = ∑t =1 c(i, j, t ) ………… (5)
environment                                                                                                       T

                                                                                        4- Density-based Metrics
    Fig.3 Layout of Urban environment                                                   These metrics are mobility independent, but does
                                                                                        give an important feedback about the local
    5-Overview of Mobility and Connectivity Metrics                                     environment. Node Density is the number of
    Many metrics have been used with the aim to adapt                                   neighbors seen by the considered node. Average
    routing protocols to the possible changes in the                                    number of nodes that stay within a node radio range
    network topology due to node mobility [4]. In this                                  at a time t is given by the following equation:
    section we will present a variety metrics have been
    used in my work, and can help measuring link
                                                                                                     ∑ ∑
                                                                                                           T           N
                                                                                                                              N (i, t )
    analysis and the performance in environment model.                                          ND =       T =1        i =1
    Figs.2, 3 show a typical movement scenario in                                                                     TN
    indoor and outdoor environment that we will                                         Where, N is number of nodes, T is simulation time,
    principally use in our simulation tests.                                            and N(i,t)is the number of neighbor nodes for node i
    1- Speed-based Metrics: -                                                           at time t.
      - Average Relative Speed between all nodes (RS)                                   6- Simulation Description
    is the average magnitude of relative speed of two                                   A variety of matrices have been used for the
    nodes over all neighborhood pairs and all time. RS is                               MANET environment. In this section, we study the
    given by:                                                                           most popular metrics, relative speed, spatial
                               r         r                                              dependence, temporal dependence, number of link
              RS (i, j , t ) = Vi (t ) − V j (t ) ……… (1)
                                                                                        change and node degree in the environments where
      -Spatial Dependence: the average function value                                   MH and RWP exist. Our evaluations are based on
    of angle of Relative velocity of two nodes over all                                 the simulation using Network Simulator (NS-2)
    neighborhood pairs and all times, the following                                     environment with CMU wireless adhoc networking
    equation shows Degree of spatial dependence:                                        extension [7] and we extract the useful data from
                               r r                r r                                   trace file using mobility trace analyzer tool (version
       Dspatial(i, j, t) = RD vi (t),v j (t))* SR(vi (t),v j (t)).(2)
                                                                                        1.0 beta)[8], then the graphs are generated using
    RD is relative direction and SR is speed ratio                                      Matlab.Simulation environment consists of 40
    - Degree of temporal dependence: shows the                                          wireless nodes forming an ad hoc network, moving
    similarity of node’s velocities at different times [1],                             over a 1000 X 1000 flat space, DSR routing protocol
    and it is given by:                                                                 for 900 seconds of simulated time. Each run of the
                              r r                 r r
     Dtemporal(i, t, t′) = RD(vi (t),vi (t′))* SR(vi (t),vi (t′)) (3)                   simulator accepts as input a scenario file that
    2- Link-based Metrics:                                                              describes the exact motion of each node and the
    The idea is based on using link information’s                                       exact sequence of packets originated by each node,
    experienced by every node. There exists a link                                      together with the exact time at which each change in
    between two nodes, if they are in each other                                        motion or packet origination is to occur. We have
    transmission range. The most used for link-based                                    generated different scenario files with varying
    Metrics are:                                                                        movement patterns and traffic loads (CBR), the
        - Link Duration (LD): the duration of one link is                               traffic consist of cbr type with 40 connections, data
    calculated as the time that two nodes are within                                    rate 1 packet/sec, packet size 512 byte and the
    transmission rang of one other .LD is given by:                                     transmission range 250m. And then ran against each
                                                                                        of these scenario files. When Nodes in street they are
                                                                                        move according to manhattan model otherwise they
                                                                                        move as Random Waypoint model .The movement

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scenario files we used for each simulation are
characterized by a number of nodes and simulation                                                                    4.8
times. Each simulation ran for 900 seconds. We ran                                                                   4.6
our simulations with movement patterns generated
for 4 different numbers of nodes, 10, 20, 30, and 40,                                                                4.4
with constant speed and 40 nodes with different                                                                      4.2
simulation time and different maximum speed. In

                                                                                                Relative Spead
simulation, we chose to make evaluation according                                                                      4
to the following metrics: Relative Speed, Spatial                                                                    3.8
Dependence, Temporal Dependence, Link Duration,
Link Change and Node Degree.                                                                                         3.6
7- Simulation Results
The simulation results bring out some important
characteristic differences between mobility and                                                                      3.2
connectivity metrics. We conducted our simulations
on changing the parameters for mobile nodes'
movement scenarios and their connection pattern                                                                      2.8
                                                                                                                           0            10         20        30            40            50
files. We supposed different simulation for
                                                                                                                                                 Max Speed (m/sec)
"movement scenarios" files. Figures below
                                                                                                                                     Fig.4, Relative Speed Vs Speed
demonstrate the simulation results by applying
different maximum speed, simulation time of mobile
nodes and different number of nodes. Fig.4 shows                                               0.25
that the relative speed is high when maximum speed
increase with different maximum speed .the values
of spatial dependence is low as shown in fig.5 fig.6                                           0.2
shows that the values of temporal dependence
decrease when the maximum speed increase. The
                                                         Spatial Dependence

link duration and node degree are high as shown                                                0.15
fig.7 and 8 and this lead to increasing the throughput
accordingly fig.9 and 10 show the effect of number
of node in changing link and node degree, when                                                 0.1
number of node increase the link change and node
degree increase.fig.11 shows that the temporal
dependence increase exponentially when the time
increase. We can conclude that with the different
maximum speed spatial and temporal dependence are
low, relative speed and link duration are high. We
observe that by increasing number of nodes the link                                                               5            10       15     20      25     30     35    40   45            50
                                                                                                                                                     Max Speed (m/sec)
change and node degree almost increasing linearly.
The temporal dependence was effect by time                                                                                      Fig.5, Spatial Dependence Vs Speed
simulation results agree with expected results based                                                 1.25

on theoretical analysis
as much as possible. This metric is also studied in                                                     1.2
various communication and mobile nodes movement
scenarios.                                                                                        1.15
                                                                         Temporal Dependence





                                                                                                                       5        10       15     20      25    30      35   40       45        50
                                                                                                                                                      Max Speed (m/sec)

                                                                                                                                    Fig.6, Temporal Dependence Vs Speed

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                                              50                                                                                                     1.005



                                                                                                                            Temporal correlation
                        Link Duration (sec)


                                              42                                                                                                     0.98


                                                                                                                                                             100     20        300        400      500      600   700        800    900
                                              36                         8- CONCLUSION                                                                               0                    Simulation time (sec)
                                                    5   10       15      20     25     30       35      40   45        50
                                                                         Max Speed (m/sec)

                                                                       Fig.7, Link duration Vs Speed                                                               Fig.10, node degree Vs #mobile nod


                        Node Degree

                                              2.2                                                                                                    30
                                                                                                                                     node degree



                                              1.4                                                                                                    15

                                                    5   10       15       20     25     30      35     40    45        50                            10
                                                                           Max Speed (m/sec)
                                                             Fig.8, Node Degree Vs Speed
                                                                                                                                                            10            15         20           2          30         35         40
                              800                                                                                                                                                         # Mobile5nodes (node)

                                                                                                                                                                   Fig.11, temporal Dependence Vs time
# links change (link)


                                                                                                                                                                           8. CONCLUSION
                                                                                                                                                   The area of ad hoc networking has been receiving
                              400                                                                                                                  increasing attention among researchers in recent
                                                                                                                                                   years, as the available wireless networking and
                              300                                                                                                                  mobile computing hardware bases are now capable
                                                                                                                                                   of supporting the promise of this technology We did
                              200                                                                                                                  investigation on MANET in urban area and the effect
                                                                                                                                                   of mobility in this environment .In this paper we
                                                                                                                                                   introduce urban environment which include indoor
                                                                                                                                                   and outdoor environment with combine mobility
                                              10        15            20         25        30          35         40                               model (RWP and MH), in addition we investigate a
                                                                       # Mobile nodes (node)
                                                                                                                                                   set of metrics to capture the characteristics of
                                                                                                                                                   mobility then we implemented a combined mobility
                                                                  Fig.9, #link change Vs #mobile node
                                                                                                                                                   model in the IMPORTANT framework in NS-2 and
                                                                                                                                                   we used a framework to analyze the link .Our study

                                                                               Ubiquitous Computing and Communication Journal                                                                                           5
indicates that the mobility and connectivity metrics
are useful to capture and understand the mobility
characteristics , we observe that some scenario
improves with add velocity, number of nodes and
simulation time. Also we observe that the metrics
does influence the performance in environment
model in urban area. Our study has shown that the
simulation results are highly dependent on the
movement behaviours of mobile node and simulation
This work is supported by National Natural Science
Foundation of China under Grant NO 572047.

9- References
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of Mobility Models for Ad Hoc Network," Research
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(WCMC): Special issue on Mobile Ad Hoc
Networking: Research, Trends and Applications,
vol.2, no. 5, pp. 483-502, 2002.
[2]. X. Hong, M. Gerla, G. Pei, and C. Chiang. "A
group mobility model for ad hoc wireless networks,"
In Proceedings of the ACM International Workshop
on Modeling and Simulation of Wireless and Mobile
Systems (MSWiM), August 1999.
[3] J. Yoon, M. Liu, and B. Noble, “Sound Mobility
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Computing and Networking (MOBICOM ’03), pp.
205-216, September2003.
[4] F. Bai, N. Sadagopan, and A. Helmy,
“IMPORTANT: A framework to systematically
analyze the Impact of Mobility on Performance of
RouTing protocols for Adhoc NeTworks,” in IEEE
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[5] F. Bai and A. Helmy, “A Survey of Mobility
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[6]W. Navidi and T. Camp, “Stationary Distributions
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 [7] http://nile.wpi.edu/NS/
 [8] http://nile.usc.edu/important/software.htm

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