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

Apr 2, 2003 INFOCOM 2003 2

• Motivation and Contributions• Mobility Models and Metrics• Experiments and Observation• Relationship between Mobility and Performance• Building Blocks Approach• Conclusion and Future work

Outline

Apr 2, 2003 INFOCOM 2003 3

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

Apr 2, 2003 INFOCOM 2003 4

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?

Apr 2, 2003 INFOCOM 2003 5

Framework Overview

Mobility Models

Mobility Metrics

ConnectivityGraph

Connectivity Metrics

Performance Metrics

RoutingProtocol

Performance

Random WaypointGroup Mobility

Freeway MobilityManhattan Mobility

DSRAODVDSDV

Relative SpeedSpatial Dependence

Link Duration ThroughputOverhead

BuildingBlock

Analysis

FloodingCaching

Error DetectionError Handling

Error Notification

Apr 2, 2003 INFOCOM 2003 6

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

Apr 2, 2003 INFOCOM 2003 7

Mobility Metrics

• Relative Speed (mobility metric I)– The magnitude of relative speed of two nodes, average over all

neighborhood pairs and all time

• 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

t

N

i

N

ijj

jjii RyxyxdistiftjvtivP

SR0 1 1

2)),(),,(()|),(),(|1

T

t

N

i

N

ijj

jjiispatial Ryxyxdistiftjvtiv

tjvtiv

tjvtiv

tjvtiv

PD

0 1 1

2)),(),,(()|),(||),(|

),(),(

)),(),,(max(

)),(),,(min(1

For example, RWP model, Vmax=30m/s, RS=12.6m/s, Dspatial=0.03

Apr 2, 2003 INFOCOM 2003 8

Connectivity graph metric

• Average link duration (connectivity metric I)– The value of link duration, average over all nodes

pairs jandibetweenlinkaisthereifjiLD

PDL

N

i

N

ijj

1 1

),(1

Performance Metrics• Throughput(performance metric I): delivery ratio

• Overhead(performance metric II): number of routing control packets sent

Apr 2, 2003 INFOCOM 2003 9

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

– 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)

max

max

())()(

()|)(||)(|

ADRrandomtt

VSDRrandomtVtV

leadermember

leadermember

Apr 2, 2003 INFOCOM 2003 10

• 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

• Manhattan Model (MH)– Similar to Freeway model, but it allows node

to make turns at each corner of street – Parameter: Map layout, Vmax

Parameterized Mobility Models

Map for FW

Map for MH

Apr 2, 2003 INFOCOM 2003 11

Mobility Models Summary

ApplicationSpatial

Dependence

Geographic

Restriction

Random

Waypoint

Model

Group Mobility

Model

Freeway Mobility Model

Manhattan Mobility Model

General

Battlefield

Metropolitan

Traffic

Urban

Traffic

No No

No

No

Yes

Yes

Yes

Yes

Apr 2, 2003 INFOCOM 2003 12

• 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

Experiment I: Analysis of mobility characteristics

Apr 2, 2003 INFOCOM 2003 13

• 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

• Spatial dependence– For SG/MG, strong degree of

spatial dependence– For RWP/FW/MH, no obvious

spatial dependence is observed

Mobility metrics

Relative Speed

Spatial Dependence

Apr 2, 2003 INFOCOM 2003 14

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

Link duration

Apr 2, 2003 INFOCOM 2003 15

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

Experiment II: Protocol Performance across Mobility

Models

Apr 2, 2003 INFOCOM 2003 16

Results and Observations

• Performance of routing protocols may vary drastically across mobility patterns– Eg : DSR

• There is a difference of 40% for throughput and an order of magnitude difference for routing overhead across mobility models!

Throughput Routing Overhead

Apr 2, 2003 INFOCOM 2003 17

Which Protocol Has the Highest Throughput ?

Random Waypoint : DSR? Manhattan : AODV or DSR?

• We observe that using different mobility models may alter the ranking of protocols in terms of the throughput!

Apr 2, 2003 INFOCOM 2003 18

Which Protocol Has the Lowest Overhead ?

RPGM(single group) : DSR? Manhattan : DSDV?

• We observe that using different mobility models may alter the ranking of protocols in terms of the routing overhead!

• Recall: Whether mobility impacts protocol performance?• Conclusion: Mobility DOES matter, significantly, in evaluation of

protocol performance and in comparison of various protocols!

Apr 2, 2003 INFOCOM 2003 19

• 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 increasesThroughput

increases and routing overhead decreases

• Conclusion: Mobility Metrics influence Connectivity Metrics which in turn influence protocol performance metrics !

Putting the Pieces Together

Apr 2, 2003 INFOCOM 2003 20

Putting the Pieces TogetherRelative Velocity

Spatial Dependence

Link Duration

Throughput

Overhead

Apr 2, 2003 INFOCOM 2003 21

• 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

Mechanistic Building Blocks

Apr 2, 2003 INFOCOM 2003 22

Building Block Diagram for reactive protocols

Route Maintenance

Flooding Caching

Range of Flooding

Num of Entry

Caching Style

Expiration Timer

Error Detection

Error Handling

Error Notification

Detection Method

Handling Mode

Recipient

Add Route Cache

Route Reply

Notify

Route Invalidate

Localized/Non-localized method

Route Setup

Notify

Apr 2, 2003 INFOCOM 2003 23

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?

• Error Handling– DSR uses localized salvaging, it only happens 2%~8% across various mobility model

salvaging barely has an effect !AODV DSR

Apr 2, 2003 INFOCOM 2003 24

• 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

Conclusions

Apr 2, 2003 INFOCOM 2003 25

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

Apr 2, 2003 INFOCOM 2003 26

Thanks!

Apr 2, 2003 INFOCOM 2003 27

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.

Apr 2, 2003 INFOCOM 2003 28

• 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

Related Work

[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.

Apr 2, 2003 INFOCOM 2003 29

Link Duration

• Re-run the single group mobility model for three times

Apr 2, 2003 INFOCOM 2003 30

• The reciprocal of average path duration is analytically shown to have a linear relationship with the throughput and overhead

• 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?

Linear Correlation between Average Path Duration and Protocol

Performance

))1((

)1(

NPPPD

TOverhead

rPD

tThroughput

HitHit

repair

[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.