Centre for Advanced Internet Architectures (CAIA) - Modeling...

Modeling Movement for Mobile IP

Networks

Andres [email protected]

Center for Advanced Internet ArchitecturesSwinburne University of Technology

30th March 2005

http://caia.swin.edu.au - [email protected]

Abstract

The modeling of user movements is used to investigate networkperformance of Mobile IP and Ad-Hoc networks. Of the severalmovement models which exist, one which is in common use is theRandom Waypoint Model (RWP). In this presentation we summarise themovement models in use and their application, and examine their validityfor Mobile IP by using a real-life trace of user movement.

http://caia.swin.edu.au - [email protected] JJ J � I II × 1

Background

• What's this got to do with LI (Lawful Interception)?

• Wanted to see how easy it was to predict user movements

• How are researchers studying the "mobility" part of Mobile IP?

• Mobility models and their reality/validity


Outline. . .

• Movement Models

• Gathering Data for My Own Trace

? Options? What was gathered?

• Analysis of collected data

• Conclusions


Outline. . .

• Movement Models




• Conclusions


Mobility Models

• Vehicular Tra�c Theory

• Mobile Telephony

• Ad-Hoc Networks

• Real Life Traces


Vehicular Tra�c Theory

• Fluid Flow Model

? Amount of tra�c �owing out of a region? Pop. Density, avg. speed, diameter? Aggregate model

• Gravity Model, Lam and Cox, et al [1]

? Models the "attractiveness" of a region? prob. of moving from region i to j

? Aggregate model, need to calculate parameters for regions.


Mobile Telephony Models

• Mean Handover Rate, Hong and Rappaport [2]

? Users are uniformly distributed in a cell? Each user chooses a direction: uniform distr., [0..2π]? Each user chooses a speed: uniform distr., [0..V max]? User reaching boundary of region = handover



• Random Walks, Akyildiz and Lin, et al [4]

? User resides in a cell for a time interval (governed by a certaindistribution)

? User either stays put or moves to neighbouring cell with a certainprobability

? No predetermined direction => no concept of a "trip"



• Mobility Models combined with Tra�c Models

• Network's performance


Ad-Hoc Networks

• Movement of users relative to each other

• Military, emergency response, etc

? Entity Mobility Models (All entities move according to the same setof rules)

? Group Mobility Models (Movement decisions depend upon otherusers in the group)

? Camp and Boleng, et al [5] surveys both types


Ad-Hoc Networks

• Random Waypoint Model (RWP)

? Select random destination (waypoint) : uniform distr., dest(x, y)? Select random speed : uniform distr., [V min, V max]? Pause for a certain time? iterate

• Extensions are many

? At boundary: bounce, reset, torus? smooth direction change, Bettstetter [6]


Ad-Hoc Networks

• Random Direction Model

? Similiar to RWP? Select random direction : uniform distr., [0..2π]


Mobile IP Mobility Models

• Not Mobile IP speci�c !


Real Life Traces as Input to Simulations

• Tracing of user movement is di�cult

? Hard to instrument a mobile telephony network - Optus

• LAN traces, Balachandran [7], ACM SIGCOMM '01

• WLAN traces, Kotz and Essein [8], trace campus WLAN at Dartmouth

• MAN traces, Tang and Baker [9], trace metropolitan area packet radiowireless network


• Above trace the movements of users within the network, to thecell/AP level


Outline. . .

• Movement Models




• Conclusions


Gathering Data

• Purpose

? Wanted to see how easy it was to predict user movements? Needed to obtain a real life trace of user movements? Application: Mobile IP over a metropolitan area (citywide)? Medium term length -> avg length of LI warrant ( 45 days)


Options for trace

• Tra�c Survey - highway tra�c survey

• GPS receiver

? Pros: detailed recording of location, speed? Cons: Possible loss of comms, limited recording of waypoints,battery life, cost


Options for trace

• GSM - triangulation

? Neve's Steplogger, www.neveits.com, [10]? Pros: detailed recording of location, speed, GPS with GSM backup? Cons: battery life, cost of unit, SMS's

• Log book method

? Pro: cheap, self motivation? Cons: self motivation, speed not accurate, location not exact


The Collected Data• Log book method

? based on Melbourne's street directory: Melway? A Melway reference, eg Map 29 D 8, can be converted to aCartesian coord

? Bounded by the coverage of the map

• Trips recorded at end of every day

? maps travelled, mode, avg speed, rest time at destination

• Period: Oct-Nov 2004

• Sample of 1 person!


Outline. . .

• Movement Models




• Conclusions


Analysis of Collected Data

22

0 50 100 150 200 250

200

150

100

500

Travel Around Melbourne, Oct & Nov 2004

x

y

23

120 130 140 150 160 170

7060

5040

3020

Travel Around Melbourne, Oct & Nov 2004 (zoom)

x

y

24

140 145 150 155 160 165

8070

6050

40

Travel: Home−Work−Home, Oct & Nov 2004

x

y

25

X

0.00.2

0.40.6

0.8

1.0

Y

0.0

0.2

0.4

0.6

0.81.0

count

0

20

40

60

80

100

Count of Visits

(X:110−180, Y:20−100)

26

X

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Y

0.00.10.20.30.40.50.60.70.80.91.0

cumm

ulative rest (log10(s))

0.00.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

Cummulative Rest Time (X:130−170, Y:20−70)

27

Random Waypoint Model (RWP)

• ns-2 implementation

? X taken from Uniform Distr? Y taken from Uniform Distr? speed taken from Uniform Distr

28

●

●

●

●

●

●

●

●

●

●

0.0 0.2 0.4 0.6 0.8

0.2

0.4

0.6

0.8

Random Waypoint Model − x,y chosen from Uniform Distr.

(random speed, random pause)x

y

29

0 50 100 150 200 250

0.0

0.2

0.4

0.6

0.8

1.0

Cummulative Distribution of Destinations (Waypoints) in X

x

Fn(

x)Normal Distr.Uniform Distr.

30

130 140 150 160 170 180

0.0

0.2

0.4

0.6

0.8

1.0

Cumm. Dist. of Destinations (Waypoints) in X

(X:120−180)x

Fn(

x)

31

50 100 150 200

0.0

0.2

0.4

0.6

0.8

1.0

Cumm. Dist. of Destinations (Waypoints) in Y

y

Fn(

x)

Normal Distrib.

32

20 30 40 50 60 70 80

0.0

0.2

0.4

0.6

0.8

1.0

Cumm. Dist. of Destinations (Waypoints) in Y

(Y:20−80)y

Fn(

x)

Normal Distrib.

33

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●

●●●●●●

●

●●

●

●●

●

0 50 100 150 200 250 300

050

000

1000

0015

0000

Rest Times at Destinations − (Sorted)

Index

Res

t Tim

es (

s)

34

Histogram of Rest Time Frequencies

Rest Times (s)

Fre

quen

cy

0 50000 100000 150000

050

100

150

200

35

Histogram of Rest Time Frequencies

(Rest Times < 100k)Rest Times (s)

Fre

quen

cy

0 20000 40000 60000 80000

050

100

150

200

36

Conclusions

• Many models, many applications

• No speci�c mobility models for Mobile IP, metro area

• Collection of real-life data

? RWP with uniform distr. not validated? Normal Distr. better for X, Y? Rest times suited by Exponential? Method not suitable for study on speed? 1 person sample

37

References

[1] D. Lam, D. Cox, and J. Widom, �Teletra�c modeling for personal communications services,�1997.

[2] D. Hong and S. S. Rappaport, �Tra�c Model and Performance Analysis for Cellular Mobile RadioTelephone Systems with Prioritized and nonprioritized Hando� Procedures,� IEEE Transactions onVehicular Technology, vol. 35, no. 3, pp. 77�92, 1986.

[3] M. M. Zonoozi and P. Dassanayake, �User mobility modeling and characterization of mobilitypatterns.� IEEE Journal on Selected Areas in Communications, vol. 15, no. 7, pp. 1239�1252,1997.

[4] I. Akyildiz, J. Ho, and Y. Lin, �Movement based location update and selective paging for pcsnetworks,� 1996.

[5] T. Camp and J. Boleng and V. Davies, �A Survey of Mobility Models for Ad Hoc NetworkResearch,� Wireless Communication and Mobile Computing (WCMC), vol. 2, no. 5, pp. 483�502,2002.

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[6] C. Bettstetter, �Mobility modeling in wireless networks: Categorization, smooth movement, andborder e�ects,� ACM Mobile Computing and Communications Review, vol. 5, no. 3, pp. 55�67,July 2001.

[7] A. Balachandran, G. Voelker, P. Bahl, and P. Rangan, �Characterizing user behavior and networkperformance in a public wireless lan,� 2002.

[8] D. Kotz and K. Essein, �Analysis of a campus-wide wireless network,� in Mobile Computing andNetworking, 2002, pp. 107�118.

[9] D. Tang and M. Baker, �Analysis of a metropolitan-area wireless network,� in Mobile Computingand Networking, 1999, pp. 13�23. [Online]. Available: citeseer.ist.psu.edu/498326.html

[10] �Neve - Steplogger,� Neve ITS Pty Ltd, 2004.

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Questions?

40

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Slide presentation supported by the foiltex package.

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