IMPORTANT: A framework to systematically
analyze the "Impact of Mobility on Performance
Of RouTing in Ad-hoc NeTworks"
Fan Bai*, Narayanan Sadagopan+, Ahmed Helmy*
* Department of Electrical Engineering
+ Department of Computer Science
University of Southern California
{fbai,helmy}@ceng.usc.edu, narayans@cs.usc.edu
Outline
•
•
•
•
•
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Motivation and Contributions
Mobility Models and Metrics
Experiments and Observation
Relationship between Mobility and Performance
Building Blocks Approach
Conclusion and Future work
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MANET
• Mobile Ad hoc Network (MANET) is a collection of
wireless mobile nodes forming a network without using
any existing infrastructure
• Mobility and traffic are two significant factors affecting
protocol performance. In current simulation,
– Mobility Pattern: usually, uniformly and randomly chosen
destinations (random waypoint model)
– Traffic Pattern: usually, uniformly and randomly chosen
communicating nodes
• Impact of mobility on ad hoc routing protocols is expected
to be significant
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Motivation
• Randomized models (including random waypoint) do not
capture
– Spatial dependence (correlation) of movement among nodes
– Existence of barriers or obstacles constraining mobility
• A systematic framework is needed to investigate the
impact of various mobility models on the performance
of different routing protocols for MANETs
• This study attempts to answer
– Whether? Especially, to what degree does mobility affect routing
protocol performance?
– If the answer to 1 is yes, why?
– If the answer to 1 is yes, how?
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Framework Overview
Mobility
Models
Connectivity
Graph
Random Waypoint
Group Mobility
Freeway Mobility
Manhattan Mobility
Mobility
Metrics
Relative Speed
Spatial Dependence
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Routing
Protocol
Performance
DSR
AODV
DSDV
Building
Block
Analysis
Connectivity
Metrics
Performance
Metrics
Flooding
Caching
Link Duration
Error Detection
Error Handling
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Error Notification
Throughput
Overhead
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Framework Components
• Whether? and How much?
– Rich set of mobility models that capture characteristics of different
type of movement
– Protocol independent metrics such as mobility metrics and
connectivity graph metrics to capture the above characteristics
• Why?
– Analysis process to relate performance with a specific
characteristic of mobility
• How?
– Systematic process to study the performance of protocol
mechanistic building blocks across various mobility characteristics
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Mobility Metrics
• Relative Speed (mobility metric I)
– The magnitude of relative speed of two nodes, average over all
neighborhood pairs and all time

1 T N N 
R S   | v (i, t )  v ( j , t ) | ) if dist (( xi , yi ), ( x j , y j ))  2 R
P t 0 i 1 j 1
j i
• Spatial Dependence (mobility metric II)
– The value of extent of similarity of the velocities of two nodes that
are not too far apart, average over all neighborhood pairs and all
time T N N




Dspatial 
1
min( v (i, t ), v ( j , t )) v (i, t )  v ( j , t )
 
) if dist (( xi , yi ), ( x j , y j ))  2 R




P t 0 i 1 j 1 max( v (i, t ), v ( j , t )) | v (i, t ) || v ( j , t ) |
j i
For example, RWP model, Vmax=30m/s, RS=12.6m/s, Dspatial=0.03
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Connectivity graph metric
• Average link duration (connectivity metric I)
– The value of link duration, average over all nodes pairs
1 N N
L D   LD(i, j ) if there is a link between i and j
P i 1 j 1
j i
Performance Metrics
• Throughput(performance metric I): delivery ratio
• Overhead(performance metric II): number of routing
control packets sent
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Parameterized Mobility Models
• Random Waypoint Model (RWP)
– Each node chooses a random destination and moves towards it with a random
velocity chosen from [0, Vmax]. After reaching the destination, the node stops
for a duration defined by the “pause time” parameter. This procedure is
repeated until simulation ends
– Parameters: Pause time T, max velocity Vmax
• Reference Point Group Model (RPGM)
– Each group has a logical center (group leader) that determines the group’s
motion behavior
– Each nodes within group has a speed and direction that is derived by
randomly deviating from that of the group leader


| Vmember (t ) |  | Vleader (t ) |  random()  SDR Vmax
 member (t )  leader (t )  random()  ADR   max
– Parameters: Angle Deviation Ratio(ADR) and Speed Deviation Ratio(SDR),
number of groups, max velocity Vmax. In our study, ADR=SDR=0.1
– In our study, we use two scenarios: Single Group (SG) and Multiple Group
(MG)
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Parameterized Mobility Models
• Freeway Model (FW)
– Each mobile node is restricted to its lane
on the freeway
– The velocity of mobile node is temporally
dependent on its previous velocity
– 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 preceding node
– Parameter: Map layout, Vmax
Map for FW
• Manhattan Model (MH)
– Similar to Freeway model, but it allows node
to make turns at each corner of street
– Parameter: Map layout, Vmax
Map for MH
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Mobility Models Summary
Spatial
Dependence
Application
Geographic
Restriction
Random
Waypoint
Model
General
No
No
Group
Mobility
Model
Battlefield
Yes
No
Freeway
Mobility
Model
Metropolitan
Traffic
Yes
Yes
Manhattan
Mobility
Model
Urban
Traffic
No
Yes
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Experiment I: Analysis of mobility
characteristics
• Simulation done by our mobility generator and
analyzer:
• Number of nodes(N) = 40, Simulation Time(T) = 900 sec
• Area = 1000m x 1000m
• Vmax set to 1,5,10,20,30,40,50,60 m/sec across simulations
• RWP, pause time T=0
• SG/MG, ADR=0.1, SDR=0.1
• FW/MH, map layout in the previous slide
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Mobility metrics
• Objective:
– validate whether proposed
mobility models span the
mobility space we explore
• Relative speed
– For same Vmax, MH/FW is
higher than RWP, which is
higher than SG/MG
Relative Speed
• Spatial dependence
– For SG/MG, strong degree of
spatial dependence
– For RWP/FW/MH, no obvious
spatial dependence is observed
Spatial Dependence
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Connectivity graph metric
• Link duration
– For same Vmax, SG/MG is higher
than RWP, which is higher than
FW, which is higher than MH
• Summary
– Freeway and Manhattan model
exhibits a high relative speed
– Spatial Dependence for group
mobility is high, while it is low for
random waypoint and other
models
– Link Duration for group mobility
is higher than Freeway, Manhattan
and random waypoint
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Link duration
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Experiment II: Protocol Performance
across Mobility Models
Simulations done in ns-2:
• Same set of mobility trace file used in experiment1
• Traffic pattern consists of source-destination pairs chosen at
random
• 20 source, 30 connections, CBR traffic
• Data rate is 4packets/sec (low data rate to avoid congestion)
• For each mobility trace file, we vary traffic patterns and run
the simulation for 3 times
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Results and Observations
• Performance of routing protocols may vary drastically
across mobility patterns
– Eg : DSR
Throughput
Routing Overhead
• There is a difference of 40% for throughput and an order of
magnitude difference for routing overhead across mobility
models!
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Which Protocol Has the Highest
Throughput ?
• We observe that using different mobility models may alter
the ranking of protocols in terms of the throughput!
Manhattan : AODV or DSR?
Random Waypoint : DSR?
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Which Protocol Has the Lowest
Overhead ?
• We observe that using different mobility models may alter
the ranking of protocols in terms of the routing overhead!
RPGM(single group) : DSR?
Manhattan : DSDV?
• Recall: Whether mobility impacts protocol performance?
• Conclusion: Mobility DOES matter, significantly, in evaluation
of protocol performance and in comparison of various protocols!
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Putting the Pieces Together
• Recall: If mobility affects protocol performance, why?
• We observe a very clear trend between mobility metric,
connectivity and performance
– With similar average spatial dependency
• Relative Speed increases Link Duration decreases Routing
Overhead increases and throughput decreases
– With similar average relative speed
• Spatial Dependence increase Link Duration increasesThroughput
increases and routing overhead decreases
• Conclusion: Mobility Metrics influence Connectivity
Metrics which in turn influence protocol performance
metrics !
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Putting the Pieces Together
Throughput
Relative Velocity
Link Duration
Spatial Dependence
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Overhead
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Mechanistic Building Blocks
• Recall: How mobility affects the protocol performance?
• Idea:
– The protocol is decomposed into its constituent mechanistic,
parameterized building block, each building block is to implement
a well-defined functionality
– Various protocols choose different parameter settings for the same
building block. For a specific mobility scenario, the building block
with different parameters behaves differently, which in turns affect
the overall performance of the protocol
• We are interested in the contribution of building blocks to
the overall performance in the face of mobility
• Case study:
– Reactive protocols like DSR and AODV
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Building Block Diagram for reactive
protocols
Route Setup
Flooding
Range of Flooding
Caching
Add Route
Cache
Num of Entry
Caching Style
Route
Reply
Expiration Timer
Localized/Non-localized method
Route
Invalidate
Route Maintenance
Error
Detection
Error
Handling
Notify
Detection
Method
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Error
Notification
Notify
Handling
Mode
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Recipient
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Examples
• Caching
– DSR uses aggressive caching, AODV does not
– Evaluation: Ratio of number of route replies from cache to total number
of route reply  aggressive caching is useful ? How about cache validity?
AODV
DSR
• Error Handling
– DSR uses localized salvaging, it only happens 2%~8% across various
mobility model  salvaging barely has an effect !
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Conclusions
• Defined protocol independent metrics to capture a few
mobility characteristics of interest and proposed a rich set of
mobility models
• Evaluated protocols over mobility models that span the
above mobility characteristics
• Performance trends and comparison results vary widely
with the choice of mobility
• Establish the logical relationship between mobility and
protocol performance
• Propose a method to analyze the interplay between building
block and mobility
• Mobility patterns are IMPORTANT
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Future Work
• Investigate more protocol independent metrics. e.g., path
duration[1]
• Establish the general framework to evaluate the design
choice based on building block methodology[2]
• Investigate the effect of other parameters. e.g., node
density
• Investigate other mobility models and other routing
protocols, e.g. ZRP,GPSR & expansion model
• Integrate the mobility tool with ns-2 [3]
[1] N.Sadagopan, F.Bai, B.Krishnamachari, A.Helmy, “PATHS: analysis of PATH duration Statistics and their impact on
reactive MANET routing protocols” MobiHoc 2003.
[2] F.Bai, N.Sadagopan, A.Helmy, “BRICS: A Building-block approach for analyzing RoutIng protoCols in ad hoc networkS- a
case study of reactive routing protocols”, USC-CS-TR-02-775, in submission.
[3] http://www-scf.usc.edu/~fbai/mobility.html
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Thanks!
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Related Work
• Random Waypoint based evaluation
– Mobility model: only Random Waypoint model
– [1] concluded that reactive protocols like DSR and AODV
would perform better than proactive protocols such as
DSDV under high mobility rate, while DSDV would
perform quite well under low mobility rate
– [2] observed that DSR would outperform AODV in less
demanding situations, but AODV would outperform DSR at
heavy traffic and high mobility scenario
– Consistent with our observations
[1]J.Broch, D.A.Maltz, D.B.Johnson et al, “A performance comparison of multi-hop wireless ad hoc network routing protocols”,
MOBICOM 1998.
[2]S.R.Das, C.E.Perkins, E.M.Royer, “Performance Comparison of two on-demand routing protocols for ad hoc network”,
INFOCOM 2000.
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Related Work
• Scenario based evaluation
– [3] proposed models for ‘realistic’ scenarios like conference,
disaster relief and event coverage
– Conclusion about reactive and proactive protocol is similar
to [1]
– [4] introduced the Reference Point Group Model(RPGM), it
is observed that AODV, DSDV and HSR would perform
worse with random waypoint model than with RPGM
– [5] proposed a generic mobility framework, Mobility Vector
Model, from which all ‘realistic’ mobility patterns like
MPGM can be derived
[3] P.Johansson, T.Larsson, N.Hedman et al, “Scenario-based performance analysis of routing protocols for mobile ad-hoc
network”, MOBICOM 1999.
[4] X.Hong, M.Gerla et al, “A group mobility model for ad hoc wireless network”, ACM/IEEE MSWiM 1999.
[5] X.Hong, T.Kwon, M.Gerla et al, “A mobility framework for ad hoc wireless networks”, ACM MDM 2001.
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Link Duration
• Re-run the single group mobility model for three
times
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Linear Correlation between Average Path
Duration and Protocol Performance
• The reciprocal of average path duration is analytically shown to
have a linear relationship with the throughput and overhead
Throughput  (1 
t repair
PD
)r
T
Overhead 
( PHit  (1  PHit ) N )
PD
• For DSR
– Pearson Correlation between 1/PD and throughput is –0.9165, -0.9597 and –0.9132
for RW, FW and MH, respectively
– Pearson Correlation between 1/PD and overhead is 0.9753, 0.9812 and 0.9978 for
RW, FW and MH, respectively
• Relationship between LD and PD?
[1] N.Sadagopan, F.Bai, B.Krishnamachari, A.Helmy, “PATHS: analysis of PATH duration Statistics and their
impact on reactive MANET routing protocols” MobiHoc 2003.
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