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USC Computer Science Technical Reports, no. 765 (2002)

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USC Computer Science Technical Reports, no. 765 (2002)

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Content 1
IMPORTANT: A framework to systematically analyze the Impact of
Mobility on Performance of RouTing protocols for Adhoc NeTworks
Fan Bai, Narayanan Sadagopan, Ahmed Helmy
Abstract—A Mobile Ad hoc Network (MANET) is a col-
lection of wireless mobile nodes forming a temporary net-
work without using any existing infrastructure. Since not
many MANETs are currently deployed, research in this area
is mostly simulation based. Random Waypoint is the com-
monly used mobility model in these simulations. Random
Waypoint is a simple model that may be applicable to some
scenarios. However, we believe that it is not sufficient to cap-
ture some important mobility characteristics of scenarios in
which MANETs may be deployed. Our framework aims to
evaluate the impact of different mobility models on the per-
formance of MANET routing protocols. We propose various
protocol independent metrics to capture interesting mobility
characteristics, including spatial and temporal dependence
and geographic restrictions. In addition, a rich set of param-
eterized mobility models is introduced including Random
Waypoint, Group Mobility, Freeway and Manhattan mod-
els. Based on these models several ’test-suite’ scenarios are
chosen carefully to span the metric space. We demonstrate
the utility of our test-suite by evaluating various MANET
routing protocols, including DSR, AODV and DSDV. Our
results show that the protocol performance may vary dras-
tically across mobility models and performance rankings of
protocols may vary with the mobility models used. This ef-
fect can be explained by the interaction of the mobility char-
acteristics with the connectivity graph properties. Finally,
we attempt to decompose the routing protocols into mecha-
nistic “building blocks” to gain a deeper insight into the per-
formance variations across protocols in the face of mobility.
I. INTRODUCTION
A Mobile Ad hoc NETwork (MANET) is a collec-
tion of wireless nodes communicating with each other
in the absence of any infrastructure. Classrooms, bat-
tlefields and disaster relief activities are a few scenarios
where MANETs can be used. MANET research is gaining
ground due to the ubiquity of small, inexpensive wireless
communicating devices. Since, not many MANETs have
been deployed, most of this research is simulation based.
One of the most important elements of MANET simula-
tions is the mobility pattern. Mobility pattern, in many
Department of Electrical Engineering, University of Southern Cali-
fornia, E-mail: fbai@usc.edu
Department of Computer Science, University of Southern California,
E-mail: narayans@cs.usc.edu
Department of Electrical Engineering, University of Southern Cali-
fornia, E-mail: helmy@ceng.usc.edu
previous works was assumed to be Random Waypoint. In
this mobility model, at every instant, a node randomly
chooses a destination and moves towards it with a velocity
chosen uniformly randomly from
, where
is
the maximum allowable velocity for every mobile node.
This is a simple model to characterize node mobility.
In the future, MANETs are expected to be deployed in
myriads of scenarios having complex node mobility and
connectivity dynamics. This is expected to have a signif-
icant impact on the performance of the routing protocols
like DSR [2], DSDV [3] and AODV [5]. Random Way-
point is insufficient to capture the following mobility char-
acteristics:
1) Spatial dependence of movement among nodes.
2) Temporal Dependence of movement of a node over
time.
3) Existence of barriers or obstacles constraining mo-
bility.
We propose a framework to systematically analyze the im-
pact of mobility on the performance of routing protocols
for MANETs. This analysis attempts to answer the follow-
ing questions:
1) Whether mobility affects routing protocol perfor-
mance?
2) If the answer to 1 is yes, why?
3) If the answer to 1 is yes, how?
To answer Whether, the framework evaluates the perfor-
mance of these routing protocols over different mobil-
ity patterns that capture some of the characteristics listed
above. The mobility models used in our study include
the Random Waypoint, Group Mobility [7], Freeway and
Manhattan. To answer Why, we propose some protocol in-
dependent metrics such as mobility metrics and connectiv-
ity graph metrics. Mobility metrics aim to capture some of
the aforementioned mobility characteristics. Connectivity
Graph metrics aim to study the effect of different mobil-
ity patterns on the connectivity graph of the mobile nodes.
It has also been observed in previous works that under a
given mobility pattern, routing protocols like DSR, DSDV
and AODV perform differently [1] [11] [12]. This is
possibly because each protocol differs in the basic mecha-
nisms or “building blocks” it uses. For example, DSR uses
route discovery, while DSDV uses periodic updates. To
2
Mobility
Models
Mobility
Metrics
Connectivity
Graph
Connectivity
Metrics
Performance
Metrics
Routing
Protocol
Performance
Random Waypoint
Group Mobility
Freeway Mobility
Manhattan Mobility
DSR
AODV
DSDV
Relative Speed
Spatial Dependence
Link Duration Throughput
Overhead
Building
Block
Analysis
Flooding
Caching
Error Detection
Error Handling
Error Notification
Fig. 1. IMPORTANT Framework
answer How, we want to investigate the effect of mobility
on some of these “building blocks” and how they impact
the protocol performance as a “whole”.
In order to conduct our research and answer the above
questions systematically, we propose a framework for an-
alyzing the Impact of Mobility on the Performance Of
RouTing protocols in Adhoc NeTworks (IMPORTANT).
Through this framework we illustrate how modeling mo-
bility is important in affecting routing performance and
understanding the mechanism of ad hoc routing protocols.
As shown in Fig. 1, our framework focuses on the fol-
lowing aspects: mobility models, the metrics for mobil-
ity and connectivity graph characteristics, the potential re-
lationship between mobility and routing performance and
the analysis of impact of mobility on building blocks of ad
hoc routing protocols.
The rest of this paper is organized as follows. Section II
gives a brief description of the related work and elabo-
rates our contribution. Section III discusses some limita-
tions of the Random Waypoint model and motivates part of
our framework. Section IV presents our proposed metrics
to capture characteristics of mobility and the connectiv-
ity graph between the mobile nodes. Section V describes
the mobility models used and introduces two new mod-
els, the Freeway mobility model and the Manhattan mobil-
ity model. Results of our simulation experiments are pre-
sented and discussed in Section VI. The analysis of the im-
pact of mobility on protocol buildingblocks is discussed in
Section VII. Finally, our conclusions from this study and
planned future work are listed in section VIII.
II. RELATED WORK
Extensive research has been done in modeling mobility
for MANETs. In this section, we mainly focus on experi-
mental research in this area. This research can be broadly
classified as follows based on the methodology used:
A. Random Waypoint Based Performance Comparisons
Much of the initial research was based on using Ran-
dom Waypoint as the underlying mobility model and CBR
traffic consisting of randomly chosen source destination
pairs as the traffic pattern. Routing protocols like DSR
[2], DSDV [3], AODV [5] and TORA [15] were mainly
evaluated based on the following metrics: packet deliv-
ery ratio (ratio of the number of packets received to the
number of packets sent) and routing overhead (number of
routing control packets sent). Ref. [1] concluded that on-
demand protocols such as DSR and AODV performed bet-
ter than table driven ones such as DSDV at high mobil-
ity rates, while DSDV performed quite well at low mobil-
ity rates. Ref. [11] performed a comparison study of the
two on-demand routing protocols: DSR and AODV , using
the performance metrics of packet delivery ratio and end
to end delay. It observed that DSR outperforms AODV
in less demanding situations, while AODV outperforms
DSR at heavy traffic load and high mobility. However,
the routing overhead of DSR was found to be lesser than
that of AODV . In the above works, focus was given on
performance evaluation, while parameters investigated in
the mobility model were change of maximum velocity and
pause time. In our work, however, we design our test
suites very carefully to pick scenarios that span a much
larger set of mobility characteristics. Not only do we use
Random Waypoint but also other mobility models such as
RPGM [7], Freeway and Manhattan in our evaluation of
the performance of routing protocols.
B. Scenario Based Performance Comparisons
Random Waypoint is a simple model that is easy to an-
alyze and implement. This has probably been the main
reason for the widespread use of this model for simula-
tions. Realizing that Random Waypoint is too general a
model, recent research has started focusing on alternative
mobility models and protocol independent metrics to char-
acterize them. Ref. [9] conducted a scenario based per-
formance analysis of the MANET protocols. It proposed
models for a few “realistic” scenarios such as a confer-
ence, event coverage and disaster relief. To differentiate
between scenarios used, the study introduced the relative
motion of the mobile nodes as a mobility metric. Their
conclusions about the performance of proactive and reac-
tive protocols were similar to [1]. Ref. [12] used a mo-
bility model in which each node computes its next posi-
tion based on a probability distribution. This model does
not allow significant changes in direction between succes-
sive instants. It concluded that proactive protocols per-
form better than reactive ones in terms of packet delivery
ratio and end-to-end delay. However, reactive protocols
were seen to incur a lower routing overhead. Ref. [7]
introduced the Reference Point Group Mobility (RPGM)
model, which is one of the mobility models used in this
3
study. Rate of link changes was used to characterize a few
group mobility patterns as well as Random Waypoint. It
observed that the rate of link change for Random Way-
point was higher than for RPGM. From experiments, it ob-
served that protocols like AODV , DSDV and HSR [13]
perform worse with Random Waypoint than with RPGM.
Thus, it concluded that mobility models do matter and it
is not sufficient to simulate protocols with only the “ran-
dom walk” like models. Ref. [6] proposed a mobil-
ity framework that consisted of a Mobility Vector Model
which can be used to generate “realistic” movement pat-
terns used in several varied applications. It proposed the
Displacement Measure that is a normalization of the ac-
tual distance traveled by the geographic displacement as a
metric to evaluate the different movement patterns includ-
ing those generated by Random Waypoint, Random Walk,
RPGM and Mobility Vector models. By experiments, it
observed that Random Waypoint and Random Walk pro-
duced higher Displacement Measure as compared to the
Mobility Vector model. It studied the effect of transmis-
sion range on throughput across different mobility models
and concluded that as the transmission range is increased,
the rate of link changes decreased and the throughput for
all protocols increased. However, the link change rate
does not seem to vary greatly across the different mobility
models. As far as routing overhead was concerned, Mobil-
ity Vector was seen to produce a worse overhead than Ran-
dom Waypoint. Our study is also framework based. How-
ever, we do not aim to provide a generic mobility model
from which all “realistic” mobility patterns can be derived.
Rather, our framework aims at systematically studying the
effect of mobility per se on performance of MANET rout-
ing protocols. The contributions of our proposed frame-
work are three fold:
1) Focus on mobility characteristics such as spatial
dependence, geographic restrictions and temporal
dependence. Define mobility metrics that capture
these characteristics. Choose mobility models that
span the metric space and use them to evaluate the
performance of routing protocols.
2) Define connectivity graph metrics. Study the inter-
action of mobility metrics and connectivity graph
metrics and its effect on protocol performance.
3) Analyze the reasons for the differences in protocol
performance as a “whole” by investigatingthe effect
of mobility on “parts” that build the protocol.
III. LIMITATIONS OF RANDOM WAYPOINT
Random Waypoint model was introduced in [1] and is
among the most commonly used mobility models in the
MANET research community. In this model, at every in-
stant, each mobile node chooses a random destination and
moves towards it with a speed uniformly distributed in
, where
is the maximum allowable speed
for a node. After reaching the destination, the node stops
for a duration defined by the “pause time” parameter. Af-
ter this duration, it again chooses a random destination and
repeats the whole process again until the simulation ends.
The Random Waypoint model is widely accepted mainly
due to its simplicity of implementation and analysis. How-
ever, we observe that Random Waypoint fails to capture
the following mobility characteristics:
1) Temporal dependency: Due to physical con-
straints of the mobile entity itself, the velocity of
mobile node will change continuously and gently
instead of abruptly, i.e. the current velocity is
dependent on the previous velocity. However, the
velocities at two different time slots are independent
in the Random Waypoint model.
2) Spatial dependency: The movement pattern of a
mobile node may be influenced by and correlated
with nodes in its neighborhood. In Random Way-
point, each mobile node moves independently of
others.
3) Geographic restrictions: In many cases, the move-
ment of a mobile node may be restricted along the
street or a freeway. A geographic map may define
these boundaries.
In our study, we focus on the above-mentioned character-
istics. In the next section, we formally define metrics to
capture some of these characteristics.
IV. METRICS
To quantitativelyand qualitativelyanalyze the impact of
mobility on routing protocol performance, we make use of
several protocol independent metrics and protocol perfor-
mance metrics. The protocol independent metrics attempt
to extract the characteristics of mobility and the connec-
tivity graph between the mobile nodes. These metrics are
then used to explain the impact of mobility on the protocol
performance metrics. The metrics we use can be broadly
classified as:
1) Protocol Independent Metrics.
2) Protocol Performance Metrics.
A. Terminology
Before formally defining the metrics, we introduce
some basic terminology that will be used later in the pa-
per:
1) : Velocity vector of node at time
.
4
2) : Speed of node at time
.
3) : Angle made by at time t with the X-axis.
4)
: Acceleration vector of node at time
.
5) : X co-ordinate of node at time
.
6) : Y co-ordinate of node at time
.
7) : Euclidean Distance between nodes and at time
.
8) : Relative Direction(RD) (or cosine
of the angle) between the two vectors
is
given by .
9) : Speed Ratio(SR) between the two
vectors
is given by ! "$#
.
10) : Transmission range of a mobile node.
11) % : Number of mobile nodes.
12) & : Simulation time.
13) ’ )(+*),.- : returns a value uniformly distributed in
the interval
0/1 21 .
B. Protocol Independent Metrics
Mobility Metrics: We propose these metrics to differ-
entiate the various mobility patterns used in our study. The
basis of differentiation is the extent to which a given mo-
bility pattern captures the characteristics of spatial depen-
dence, temporal dependence and geographic restrictions.
In addition to these metrics, we also use the Relative Speed
metric that differentiates mobility patterns based on rela-
tive motion. This metric was proposed in [9].
1) Degree of Spatial Dependence: It is extent of sim-
ilarity of the velocities of two nodes that are not too
far apart. Formally,
435
$
6 87
9 9 : 9 9 The value of 435
$
6 is high when the nodes
and travel in more or less the same direction and
at almost similar speeds. However, ;35
$
6 decreases if Relative Direction or the Speed Ratio
decreases.
As it is rare for a node’s motion to be spatially de-
pendent on a far off node, we add the condition that
=2@ :
BAC435
$
6 D7
where
>2@E<
is a constant which will be determined
during our experiments in section VI.
Average Degree of Spatial Dependence: It is the
value of 435
$
6 averaged over node pairs
and time instants satisfying certain condition. For-
mally,
F 435
$
6 7HG
I J @ G K J @ G K J 0L8@
435
$
6 M where
M is the number of tuples
such that
435
$
6 =N 7 .
Thus, if mobile nodes move independently of one
another, then the mobilitypattern is expected to have
a smaller value for
F 435
$
6 . On the other hand, if the
node movement is co-ordinated by a central entity,
or influenced by nodes in its neighborhood, such
that they move in similar directions and at similar
speeds, then the mobility pattern is expected to have
a higher value for
F 435
$
6 .
2) Degree of Temporal Dependence: It is the extent
of similarity of the velocities of a node at two time
slots that are not too far apart. It is a function of the
acceleration of the mobile node and the geographic
restrictions. Formally,
O 5PQ 6 7
9 9 : 9 9 The value of O 5PQ 6 is high when the
node travels in more or less the same direction and
almost at the same speed over a certain time interval
that can be defined. However, O 5PQ 6 de-
creases if Relative Direction or the Speed Ratio de-
creases.
Arguing in a way similar to that for ;35
$
6 R R / R R S
AT
O 5PQ 6 7 where
>SU<
is a constant which will be determined
during our experiments in section VI.
Average Degree of Temporal Dependence: It is
the value of O 5PQ 6 averaged over nodes
and time instants satisfying certain condition. For-
mally,
F O 5PQ 6 7 G K J @ G I J @ G I J @ O 5PQ 6 M where
M is the number of tuples
such that
O 5PQ 6 UN 7 Thus, if the current velocity of a node is completely
independent of its velocity at some previous time
step, then the mobility pattern is expected to have
a smaller value for
F O 5PQ 6 . However, if the cur-
rent velocity is strongly dependent on the velocity at
some previous time step, then the mobility pattern is
expected to have a higher value for
F O 5PQ 6 .
3) Relative Speed (RS): We use the standard defini-
tion from physics i.e.
RS
87
/ As in the case of ;35
$
6 , we add the fol-
lowing condition:
5
==:
A RS
87
where
>< is a constant which will be determined
during our experiments in section VI.
Average Relative Speed: It is the value of
RS
averaged over node pairs and time
instants satisfying certain condition. Formally,
F RS
7 G K J @ G K J @ G I J @ RS
M where
M is the number of tuples
such that
RS
=N 7 4) Geographic Restrictions: We developed the no-
tion of degree of freedom of points on a map. De-
gree of freedom of a point is the number of direc-
tions a node can go after reaching that point
@ . We
do not quantitatively define the Geographic Restric-
tions, but we qualitatively include it in our study as
will be seen in Section V.
Connectivity Graph Metrics: Since routing protocol
performance is in general affected by the network topol-
ogy dynamics, we feel that it is useful to have metrics to
analyze the effect of mobility on the connectivity graph
between the mobile nodes. The connectivity graph met-
rics aim to study this effect. These metrics might also help
in relating mobility metrics with protocol performance,
which will be shown in Section VI.
The connectivity graph is the graph
7 , such that
= % and at time t, a link
iff .
Let be an indicator random variable which has a
value
1 iff there is a link between nodes and at time
.
D7 I J @ be an indicator random vari-
able which is 1 if a link existed between nodes and at
any time during the simulation, 0 otherwise.
1) Number of Link Changes: Number of link
changes for a pair of nodes and is the number
of times the link between them transitions from
“down” to “up”. Formally,
LC
87
I J @ where
is an indicator random variable such
that
7 1 iff / 1 H7
and
7 1 i.e. if the link between nodes and
is down at time
/ 1 , but comes up at time
.
Average Number of Link Changes: It is the value
of LC
averaged over node pairs satisfying cer-
Currently we do not have a good way of quantitatively aggregating
this definition for the whole map. This is part of our on going and future
work
tain condition. Formally,
F LC
7HG
K J @ G K J 0L8@
LC
M where
M is the number of pairs , such that
N 7 .
2) Link Duration: It is the average duration of the link
existing between two nodes and . It is a measure
of stability of the link between these nodes. For-
mally,
LD
7 G LC
if LC
=N 7 G I J @ otherwise
(1)
Average Link Duration: It is the value of LD
averaged over node pairs satisfying certain condi-
tion. Formally,
F LD
7HG
K J @ G K J 0L8@
LD
M where
M is the number of pairs , such that
N 7 .
3) Path Availability: It is the fraction of time during
which a path is available between two nodes and
. The node pairs of interest are the ones that have
communication traffic between them. Formally,
PA
7 G start !#"$
I&%
start
if & / start
<
otherwise
(2)
where ’ is an indicator random variable
which has a value 1 if a path is available from node
to node at time
, and has a value 0 otherwise.
start
is the time at which the communication
traffic between nodes and starts.
Average Path Availability: It is the value of
PA
averaged over node pairs satisfying certain
condition. Formally,
F PA
7 G K J @ G K J 0L8@
PA
M where
M is the number of pairs , such that & / start
<
.
C. Protocol Performance Metrics:
We evaluate the performance of the MANET routing
protocols using the metrics of throughput (ratio of the
number of packets delivered to the number of packets sent)
and routing overhead (number of routing control packets
sent) as done in several previous studies in this area of re-
search.
6
V. MOBILITY MODELS
As mentioned in Section I, Random Waypoint does not
seem to capture the mobility characteristics of spatial de-
pendence, temporal dependence and geographic restric-
tions. In the previous section, we defined mobility met-
rics that either qualitatively or quantitatively define these
characteristics. To thoroughly study the effect of mobil-
ity on MANET protocol performance, we seek to evaluate
the protocols over a rich set of mobility models that span
the design space of the mobility metrics. Thus, apart from
Random Waypoint, we use the following mobility mod-
els:
1) Reference Point Group Mobility (RPGM) Model
2) Freeway Mobility Model
3) Manhattan Mobility Model
Each of the above models has certain characteristics that
are different from Random Waypoint, which will be shown
by our metrics and simulations.
1) RPGM Model: Ref. [7] introduced this model.
Here, each group has a logical center (group leader)
that determines the group’s motion behavior. Ini-
tially, each member of the group is uniformly dis-
tributed in the neighborhood of the group leader.
Subsequently, at each instant, every node has a
speed and direction that is derived by randomly de-
viating from that of the group leader.
Applications: Group mobility can be used in mili-
tary battlefield communications where the comman-
der and soldiers form a logical group. More applica-
tions are mentioned in [7].
Important Characteristics:Each node deviates its
velocity (both speed and direction) randomly from
that of the leader. The movement in group mobility
can be characterized as follows:
a)
R R R =O O Q R R R 7 R R R +6 O $O
Q R R R ’ )(+*),.- : : - * b) =O O Q 7
6 O $O
Q ’ )(+*),.- : ’U : - )( where
’U 1 . SDR is the Speed De-
viation Ratio and ADR is the Angle Deviation Ra-
tio. SDR and ADR are used to control the deviation
of the velocity (magnitude and direction) of group
members from that of the leader.
max speed and max angle are used to specify the
maximum deviation a group member can take. In
our simulation, we set maximum speed for the group
leader as the max speed and set
1 as the max an-
gle. Since the group leader mainly decides the mo-
bility of group members, group mobility pattern is
expected to have high spatial dependence for small
values of SDR and ADR.
2) Freeway Mobility Model: We propose this new
model to emulate the motion behavior of mobile
nodes on a freeway.
Applications: It can be used in exchanging traffic
status or tracking a vehicle on a freeway.
Important Characteristics: In this model we use
maps. There are several freeways on the map and
each freeway has lanes in both directions. The dif-
ferences between Random Waypoint and Freeway
are the following:
a) Each mobile node is restricted to its lane on the
freeway.
b) The velocity of mobile node is temporally de-
pendent on its previous velocity.
c) If two mobile nodes on the same freeway lane
are within the Safety Distance (SD), the veloc-
ity of the following node cannot exceed the ve-
locity of preceding node.
The inter-node and intra-node relationships in-
volved are:
a)
R R R 1 R R R 7 R R R R R R?’
)(+*),.- 8:
b) )
A ,
if is ahead of in its lane.
Due to the above relationships, the Freeway mobil-
ity pattern is expected to have spatial dependence
and high temporal dependence. It also imposes strict
geographic restrictions on the node movement by
not allowing a node to change its lane.
3) Manhattan Mobility Model: We introduce the
Manhattan model to emulate the movement pattern
of mobile nodes on streets defined by maps.
Applications: It can be useful in modeling move-
ment in an urban area where a pervasive computing
service between portable devices is provided.
Important Characteristics: Maps are used in this
model too. The map is composed of a number of
horizontal and vertical streets. Each street has two
lanes for each direction (North and South direc-
tion for vertical streets, East and West for horizon-
tal streets). The mobile node is allowed to move
along the grid of horizontal and vertical streets on
the map. At an intersection of a horizontal and a ver-
tical street, the mobile node can turn left, right or go
straight. This choice is probabilistic: the probability
of moving on the same street is 0.5, the probability
of turning left is 0.25 and the probability of turning
right is 0.25.
The velocity of a mobile node at a time slot is depen-
dent on its velocity at the previous time slot. Also,
7
a node’s velocity is restricted by the velocity of the
node preceding it on the same lane of the street. The
inter-node and intra-node relationships involved are
the same as in the Freeway model.
Thus, the Manhattan mobility model is also ex-
pected to have high spatial dependence and high
temporal dependence. It too imposes geographic re-
strictions on node mobility. However, it differs from
the Freeway model in giving a node some freedom
to change its direction.
Most of the mobility models mentioned above are pa-
rameterized. E.g. SDR and ADR are some of the parame-
ters used in RPGM, while maps are important parameters
in the Freeway and Manhattan models. Although we did
not quantitatively define Geographic Restrictions in Sec-
tion IV, we qualitatively include them in our study by us-
ing the Freeway and Manhattan models. Using a parame-
terized approach, we aim to get a good coverage of design
space of the proposed mobility metrics by producing a rich
set of mobility patterns that can be used as a “test-suite” for
further research.
VI. EXPERIMENTS
As a first step, we wanted to validate if our proposed
metrics differentiate the mobility models. Once this was
done, we focused on answering the following questions:
Whether mobility affects protocol performance?, if yes,
we attempt to answer the questions Why? and How? men-
tioned in Section I.
A. Validating the Mobility Metrics
Our mobility scenario generator produced the different
mobility patterns following the RPGM, Freeway and Man-
hattan models according to the format required by network
simulator (ns-2) [14]. Random Waypoint mobility pat-
tern was generated using the setdest tool which is a part
of the ns-2 distribution. In all these patterns, 40 mobile
nodes moved in an area of 1000m x 1000m for a period
of 900 seconds. For RPGM, we used 2 different mobil-
ity scenarios: single group of 40 nodes and 4 groups of 10
nodes each moving independently of each other and in an
overlapping fashion. Both Speed Deviation Ratio (SDR)
and Angle Deviation Ratio (ADR) were set to 0.1. For the
Freeway and Manhattan models, the nodes were placed on
the freeway lanes or local streets randomly in both direc-
tions initially. Their movement was controlled as per the
specifications of the models. The maximum speed
was set to 1, 5, 10, 20, 30, 40, 50 and 60 m/sec to generate
different movement patterns for the same mobility model.
On evaluating these patterns with our mobility metrics, we
observed that some of the metrics were able to differentiate
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
10
20
30
40
50
Average Relative Speed (m/sec)
Random Waypoint
RPGM (Single Group)
RPGM (4 Groups)
Freeway
Manhattan
Fig. 2. Average Relative Speed
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
Average Degree of Spatial Dependence
Random Waypoint
RPGM (Single Group)
RPGM (4 groups)
Freeway
Manhattan
Fig. 3. Average Degree of Spatial Dependence
between the mobility patterns based on the characteristics
we focused on, while the others failed.
Average Relative Speed (
F RS): We experimented with
different values of the constant
> mentioned in Section
IV. For the value of
> 7 ,
F RS could differentiate be-
tween the different mobility patterns very clearly. As seen
in Fig 2,
F RS has the lowest value for RPGM (single group
and multiple group mobility) as the nodes move together
in a co-ordinated fashion with little deviation, while it has
a medium value for Random Waypoint. Its value for the
Freeway and Manhattan mobility patterns is the highest
and almost twice that for Random Waypoint. This high
value is because of the movement in opposite direction for
both Freeway and Manhattan mobility patterns.
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
200
400
600
800
1000
Average Link Duration (sec)
Random Waypoint
RPGM (Single Group)
RPGM (Multiple Groups)
Freeway
Manhattan
Fig. 4. Average Link Duration
8
Average Degree of Spatial Dependence (
F 435
$
6 ): We ex-
perimented with different values of the constant
>.@
men-
tioned in Section IV. For the value of
> @ 7 ,
F 435
$
6 could differentiate between the different mobility patterns
very clearly. As seen in Fig 3,
F 435
$
6 has a higher value
for single group mobility (around 0.5) than multiple group
mobility (about 0.35). However, for the Random Way-
point, Manhattan and Freeway, its value is almost 0. In-
tuitively, in RPGM, the group leader controls the move-
ment of the mobile node and thus the mobility pattern has
a high spatial dependence. Initially, we expected the Free-
way and Manhattan mobility patterns to have a high spatial
dependence as a node’s movement is influenced by nodes
before it in the lane. Due to the use of lanes in opposite di-
rections in the map, the positive Degree of Spatial Depen-
dence of a node with nodes in the same direction cancels
the negative Degree of Spatial Dependence of the node
with nodes traveling in the opposite direction.
Average Degree of Temporal Dependence (
F O 5PQ 6 ):
This metric could not differentiate between the various
mobility patterns used in our study. The usefulness of this
metric is still under investigation.
In summary,
F RS and
F 435
$
6 are found to be useful mo-
bility metrics in our study. Fig. 2 and 3 show that for
each of these metrics, we had scenarios with relatively low
values, medium values and relatively high values. Simi-
larly, for Geographic Restrictions, the Freeway does not
allow a node to change directions as freely as the Manhat-
tan model. So, we believe that our “test-suite” has given a
reasonably good coverage of the mobility metric space.
B. Validating the Connectivity Graph Metrics
To study the effect of mobility on the connectivity
graph, we evaluated the connectivitygraphs resulting from
the mobility patterns used in Section VI-A. We had the fol-
lowing observations about the connectivity graph metrics:
Average Link Duration (
F LD): As seen in Fig. 4,
F LD has
a higher value for single group and multiple groups than
Random Waypoint. For the Freeway and Manhattan its
value is similar to Random Waypoint or even worse. Since
nodes in a group move at velocities that are deviated by a
small fraction from the group leader, an already existing
link between two nodes is expected to have a higher dura-
tion. The low value for the Freeway and Manhattan may
be because of the opposite direction of motion and high
relative speeds.
Average Number of Link Changes(
F LC): This metric was
not able to differentiate between the several mobility pat-
terns used in our study.
Average Path Availability(
F P A): It was observed that a path
is available at least of the time across all the models
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
Random Waypoint
RPGM (Single Group)
RPGM (4 Groups)
Freeway
Manhattan
Fig. 5. DSR Throughput
used. However, the difference across the models was very
small to be of any help.
In summary,
F LD is found to be a useful metric to differen-
tiate the connectivity graph arising from the different mo-
bility patterns used in our study.
To evaluate the effect of mobility on the performance
of protocols, we carried out simulations in the network
simulator (ns-2) environment with the CMU Wireless Ad
Hoc networking extension. The transmission range of
the nodes was 250m. The mobility patterns used were
the same as those used to Section VI-A. The traffic pat-
tern was generated by the cbrgen tool that is part of ns-
2 distribution. The traffic consisted of 20 Constant Bit
Rate (CBR) sources and 30 connections. The source-
destination pairs were chosen at random. The data rate
used was 4 packets/sec and the packet size was 64 bytes.
To remove any effects due to randomness of the traffic pat-
tern, we used different random seeds to generate 3 differ-
ent traffic patterns having the same number of sources and
connections. The results for each model (for a given
)
are averaged over simulation runs using these 3 different
traffic patterns.
C. Whether mobility affects protocol performance?
We evaluated the performance of DSR, AODV and
DSDV across this rich set of mobility models and ob-
served that the mobility models may drastically affect pro-
tocol performance. We use DSR as an illustrative exam-
ple. DSR shows a maximal difference of almost in
throughput from Manhattan to the RPGM (Single Group)
model as seen from Fig. 5. Also, there is an order of mag-
nitude difference in the routing overhead of DSR across
the various models as shown by Fig. 6. Similar perfor-
mance differences were observed for other protocols used
in our study. We observed that DSR, DSDV and AODV
achieve the highest throughput and the least overhead with
RPGM and incur high overhead and low throughput with
both Freeway and Manhattan models. This is consistent
with the observationsmade in [7] which evaluated the pro-
9
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
20000
40000
60000
80000
Routing Overhead (packets)
Random Waypoint
RPGM (Single Group)
RPGM (4 Groups)
Freeway
Manhattan
Fig. 6. DSR Routing Overhead
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
DSDV
AODV
DSR
Fig. 7. Random Waypoint: Throughput
tocols using Random Waypoint and several other group
mobility applications. However, we take a step further and
attempt to analyze the reason for this performance differ-
ence in Section VI-D.
Relative Performance of Protocols Across Mobility
Models: In this part, we investigated the effect of mobility
on relative rankings of protocol performance. As shown in
Fig. 7, 8, 9, 10 and 11, DSR seems to produce the highest
throughput in most cases, while AODV seems to outper-
form DSR (by as far as 11%) in the Manhattan model. As
seen from Fig. 8 and 11, the relative ranking of AODV and
DSDV in terms of throughput seems to depend on the un-
derlying mobility model.
Also, DSR incurs the least routing overhead in most cases,
while DSDV has a lower overhead than DSR in the Free-
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
DSDV
AODV
DSR
Fig. 8. RPGM (Single Group): Throughput
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
DSDV
AODV
DSR
Fig. 9. RPGM (4 Groups): Throughput
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
DSDV
AODV
DSR
Fig. 10. Freeway: Throughput
0 10 20 30 40 50 60
Maximum Speed (m/sec)
50
60
70
80
90
100
Throughput (%)
DSDV
AODV
DSR
Fig. 11. Manhattan: Throughput
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
25000
50000
75000
1e+05
1.25e+05
1.5e+05
Routing Overhead (packets)
DSDV
AODV
DSR
Fig. 12. Random Waypoint: Routing Overhead
10
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
2000
4000
6000
8000
10000
Routing Overhead (packets)
DSDV
AODV
DSR
Fig. 13. RPGM (Single Group): Routing Overhead
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
2000
4000
6000
8000
10000
Routing Overhead (packets)
DSDV
AODV
DSR
Fig. 14. RPGM (4 Groups): Routing Overhead
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
25000
50000
75000
1e+05
1.25e+05
1.5e+05
Routing Overhead (packets)
DSDV
AODV
DSR
Fig. 15. Freeway: Routing Overhead
0 10 20 30 40 50 60
Maximum Speed (m/sec)
0
25000
50000
75000
1e+05
1.25e+05
1.5e+05
Routing Overhead (packets)
DSDV
AODV
DSR
Fig. 16. Manhattan: Routing Overhead
way and Manhattan models as shown in Fig. 15 and 16.
The relative ranking of DSR and DSDV in terms of rout-
ing overhead seems to depend on the underlying mobility
model as shown in Fig. 12, 13, 14, 15 and 16.
Thus, we conclude that relative rankings of protocols may
vary with the mobility model used. We also observe
that DSDV achieves a higher throughput than AODV (by
around 10%) in RPGM. Thus, in general it is not true that
on demand protocols perform better than table driven ones
in terms of throughput. Also, a protocol with the least
overhead does not always produce the highest throughput.
E.g. in the Freeway model, DSDV seems to have the least
throughput and the least overhead.
Although, these results were somewhat expected, the
quantitative analysis helped us gain a lot of insight to an-
swer the next question.
D. Why mobility affects protocol performance?
First, the relationship between the mobility metrics and
the performance metrics was unclear. But after introduc-
ing the connectivity graph metrics, we were able to ob-
serve a very clear correlation between Average Degree
of Spatial Dependence, Average Relative Speed, Average
Link Duration and protocol performance metrics. The mo-
bility pattern influences the connectivity graph which in
turn influences the protocol performance. In general, it
was observed that DSR, DSDV and AODV had a higher
throughput and lower overhead for the group mobility
models than for the Random Waypoint model. At the
same time, all the protocols had a higher throughput and
lower overhead for Random Waypoint than the Freeway
and Manhattan models. One plausible reason for this ob-
servation can be as follows:
1) With similar relative speed, between Random Way-
point and RPGM, high degree of spatial dependence
(for RPGM) means higher link duration, which in
turn will result in higher throughput and lower rout-
ing overhead.
2) With the same degree of spatial dependency, be-
tween Freeway/Manhattan and Random Waypoint,
high relative speed (for Freeway/Manhattan) means
lower link duration, which will result in lower
throughput and higher overhead.
The above reasoning can be explained as follows: For a
given relative speed, if a mobility pattern has a high de-
gree of spatial dependence, an already existing link be-
tween two nodes is expected to remain stable for a longer
period of time as the nodes are likely to move together.
Thus fewer packets will be dropped due to link breakage
leading to higher throughput. At the same time, the con-
trol overhead is lower as little effort is needed to repair the
11
seldom broken link. For a given spatial dependence, if a
mobility pattern has a high relative speed, the nodes might
move out of range more quickly. Thus an already exist-
ing link may remain stable for a relatively shorter duration.
This may lead to more packets being dropped due to link
breakage, resulting in lower throughput. Higher control
overhead is needed to repair the more frequently broken
link. We also note that the Freeway and Manhattan mo-
bility patterns have high relative speed and low degree of
spatial dependence leading to the worst performance of all
the protocols while using these models.
VII. ANALYSIS OF BUILDING BLOCKS
Unlike the conventional evaluation studies we pursue
our analysis beyond the “whole protocol” level and
attempt to answer Why mobility affects protocol per-
formance by looking into the “parts” that constitute the
MANET routing protocols. We propose an approach to
systematically decompose a protocol into its functional
mechanistic ”building blocks”. Each building block can
be thought of as a parameterized ”black box”. The param-
eter settings define the behavior of each block, while the
nature of interaction between the building blocks defines
the behavior of the protocol as a ”whole”. We use the
analysis of reactive protocols as an example to illustrate
this approach. In this section, we carry out a preliminary
analysis of the impact of mobility on two building blocks
after identifying the basic building blocks of MANET
routing protocols.
Basic Building Blocks: The mechanism of several
MANET routing protocols is composed of two major
phases:
1) Route Setup Phase: Route Discovery is the ma-
jor mechanism in this phase. It is initiated if
there is no cached route available to the destination.
This mechanism consists of the following building
blocks:
Controlled Flooding: Flooding is mainly used for
Route Discovery if the route to the destination does
not exist in the cache. One of its parameter is the
range of flooding, generally described by TTL field
in IP header. Depending on the value of TTL, either
a non-propagating direct-neighbor inquiry (DSR) or
an expanding ring search (AODV) can be initiated
before the global route discovery flooding.
Caching: Caching is used in both Route Discov-
ery and Route Maintenance (discussed next) to in-
crease the possibility of finding a route without ini-
tiating the flooding. One of its parameters is num-
ber of allowed cache entries for a source destination
pair. Only one entry is allowed for each source des-
tination pair in AODV , while all possible routes can
be cached in DSR. The other parameter is whether
aggressive caching is allowed i.e. whether the mo-
bile node can cache the route information it over-
hears? In DSR, aggressive caching is the default.
Currently, AODV does not implement the above op-
tions for Caching.
2) Route Maintenance Phase: Route Maintenance
phase takes the responsibility of detecting broken
links and repairing the corresponding routes. This
phase is made up of the following building blocks:
Error Detection: It is used to monitor the status of
the link with its immediate neighbors.
Error Handling: It is in charge of finding alterna-
tive routes to replace an invalid route. One of the
parameters to this block is whether localized recov-
ery should be used? In a non-localized recovery, the
node detecting the link breakage will ask the source
to reinitiate the route discovery (AODV), while in
a localized recovery, the node detecting the broken
link will attempt to find an alternative route in its
own cache before asking the source to reinitiate the
route discovery (DSR packet salvaging).
Error Notification: It is used to notify the nodes in
the network about invalid routes. One of the param-
eters to this block is the recipient of error notifica-
tion. Either only the source is notified (DSR) or the
entire network is notified (AODV , due to the peri-
odic routing updates).
Impact of Mobility on Building Blocks: We speculate
that the optimal parameter settings of the building blocks
are affected by mobility pattern. To validate our specu-
lation, we analyze the effect of mobility on the following
building blocks:
Caching: As most previous studies, we observe that DSR
has a higher throughput than the other protocols under
most mobility patterns with high or moderate link dura-
tion (like Random Waypoint model or RPGM ).However,
we observe that DSR performs worse than AODV (by
about 11%) under the mobility patterns with extremely
low link duration and weak route stability (like Manhat-
tan) as shown in Fig. 11. One possible explanation for
this observation is that the price paid for eliminating the
stale cached routes obtained by aggressive caching more
than evens out the benefit gained from aggressive caching.
Thus, whether aggressive caching should be adopted de-
pends on the mobility scenarios the protocol will be de-
ployed in.
Controlled Flooding: There is high possibility of finding
cached route in a node’s neighborhood under mobility sce-
12
narios with stable routes and high link duration while this
possibilityis low under the mobilityscenarios with smaller
link durations. Thus, whether Controlled Flooding should
be used depends on the underlying mobility scenarios.
During the analysis, we noticed that DSR attempts to apply
several optimizations and optimal parameter settings for
most building blocks i.e. non-propagating direct-neighbor
inquiry for Controlled Flooding, multiple cache entries
and aggressive caching for Caching, localized error re-
covery for Error Handling. In summary, DSR is a well-
designed protocol whose parameters have been adjusted to
achieve the optimal performance.
Our current study of classifying the building blocks and in-
vestigating its effect on the performance of various rout-
ing protocols is mainly based on intuitive analysis. To
understand the functionality of building blocks and their
contributions to the routing performance, we plan to con-
duct a quantitative analysis using the procedure profiling
of the building blocks we mentioned. We are interested in
how the contributions of these building blocks will change
across mobility patterns, which will help us, better answer
Why mobility affects protocol performance.
VIII. CONCLUSIONS & FUTURE WORK
In this paper, we proposed a framework to systemat-
ically analyze the impact of mobility on routing perfor-
mance of mobile ad hoc network. In our study, we observe
that the mobility pattern does influence the performance of
MANET routing protocols. This conclusion is consistent
with the observation of previous studies. But unlike pre-
vious studies that compared different ad hoc routing pro-
tocols, there is no clear winner among the protocols in our
case, since different mobility patterns seem to give differ-
ent performance rankings of the protocols. We hope that
our “test-suite” of mobility models can be incorporated
into the current scenarios used to test the MANET routing
protocols.
Moreover, we observe that the mobility pattern influences
the connectivity graph that in turn influences the protocol
performance. In addition, we did a preliminary investiga-
tion of the common building blocks of MANET routing
protocols, the effect of mobility on these building blocks
and how they influence the protocol as a “whole”.
In the future, we plan to study the impact of our “test-
suite” on the performance of other ad hoc network proto-
cols like multicast ad hoc, geographic routing protocols.
This study would help us understand the impact of mobil-
ity more deeply and clearly. We believe that several pa-
rameters such as traffic patterns, node density and initial
placement pattern of nodes may affect the routing perfor-
mance and need to investigate them further. We are cur-
rently investigating the quantitative analysis of the build-
ing blocks.
REFERENCES
[1] Josh Broch, David A. Maltz, David B. Johnson, Yih-Chun Hu,
and Jorjeta Jetcheva. A Performance Comparison of Multi-
Hop Wireless Ad Hoc Network Routing Protocols. In Proceed-
ings of the Fourth Annual ACM/IEEE International Confer-
ence on Mobile Computing and Networking, ACM, Dallas,
TX, October 1998.
[2] David B. Johnson, David A. Maltz, and Josh Broch. DSR: The
Dynamic Source Routing Protocol for Multi-Hop Wireless Ad
Hoc Networks. In Ad Hoc Networking, edited by Charles E.
Perkins, Chapter 5, pp. 139-172, Addison-Wesley,2001.
[3] C. E. Perkins and P. Bhagwat. Highly dynamic destination se-
quenced distance vector routing (DSDV) for mobile comput-
ers, In ACM SIGCOMM, pages 234-244, 1994.
[4] David A. Maltz, Josh Broch, Jorjeta Jetcheva, and David B.
Johnson. The Effects of On-Demand Behavior in Routing Pro-
tocols for Multi-Hop Wireless Ad Hoc Networks. IEEE Jour-
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bile and wireless networks. August 1999.
[5] Charles Perkins, Ad hoc On Demand Distance Vector (AODV)
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[9] P. Johansson, T. Larsson, N. Hedman, B. Mielczarek and M.
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[10] S.-J. Lee, M. Gerla, and C.-K. Toh.A Simulation Study of
Table-Driven and On-Demand Routing Protocols for Mobile
Ad Hoc Networks. IEEE Network,vol. 13, no. 4, Jul/Aug
1999, pp. 48-54.
[11] S. R. Das, C. E. Perkins and E. M. Royer. Performance Com-
parison of Two On-demandRouting Protocols for Ad Hoc Net-
works. Proceedingsof INFOCOM 2000 Conference, Tel-Aviv,
Israel, March 2000.
[12] S. R. Das, R. Castaneda, J. Yan and R. Sengupta. Comparative
Performance Evaluation of Routing Protocols for Mobile, Ad
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Communications and Networks (IC3N), Lafayette, LA, Octo-
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[13] G.Pei, M. Gerla, X. Hong and C.-C.-Chiang. A wireless hier-
archical protocol with group mobility. In proceedings of IEEE
WCNC ’99, September 1999.
[14] http://www.isi.edu/nsnam/ns
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Description

Fan Bai, Narayanan Sadagopan, Ahmed Helmy. "IMPORTANT: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 765 (2002).

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Core Title USC Computer Science Technical Reports, no. 765 (2002)

Alternative Title IMPORTANT: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks (title)

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