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TRANSCRIPT
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 2
Outline. . .
• Movement Models
• Gathering Data for My Own Trace
? Options? What was gathered?
• Analysis of collected data
• Conclusions
http://caia.swin.edu.au - [email protected] JJ J � I II × 3
Outline. . .
• Movement Models
• Gathering Data for My Own Trace
? Options? What was gathered?
• Analysis of collected data
• Conclusions
http://caia.swin.edu.au - [email protected] JJ J � I II × 4
Mobility Models
• Vehicular Tra�c Theory
• Mobile Telephony
• Ad-Hoc Networks
• Real Life Traces
http://caia.swin.edu.au - [email protected] JJ J � I II × 5
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.
http://caia.swin.edu.au - [email protected] JJ J � I II × 6
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 7
Mobile Telephony Models
• 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"
http://caia.swin.edu.au - [email protected] JJ J � I II × 8
Mobile Telephony Models
• Mobility Models combined with Tra�c Models
• Network's performance
http://caia.swin.edu.au - [email protected] JJ J � I II × 9
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 10
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]
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Ad-Hoc Networks
• Random Direction Model
? Similiar to RWP? Select random direction : uniform distr., [0..2π]
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Mobile IP Mobility Models
• Not Mobile IP speci�c !
http://caia.swin.edu.au - [email protected] JJ J � I II × 13
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 14
• Above trace the movements of users within the network, to thecell/AP level
http://caia.swin.edu.au - [email protected] JJ J � I II × 15
Outline. . .
• Movement Models
• Gathering Data for My Own Trace
? Options? What was gathered?
• Analysis of collected data
• Conclusions
http://caia.swin.edu.au - [email protected] JJ J � I II × 16
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)
http://caia.swin.edu.au - [email protected] JJ J � I II × 17
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 18
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
http://caia.swin.edu.au - [email protected] JJ J � I II × 19
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!
http://caia.swin.edu.au - [email protected] JJ J � I II × 20
Outline. . .
• Movement Models
• Gathering Data for My Own Trace
? Options? What was gathered?
• Analysis of collected data
• Conclusions
http://caia.swin.edu.au - [email protected] JJ J � I II × 21
Analysis of Collected Data
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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)
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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)
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Random Waypoint Model (RWP)
• ns-2 implementation
? X taken from Uniform Distr? Y taken from Uniform Distr? speed taken from Uniform Distr
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●
●
●
●
●
●
●
●
●
●
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
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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.
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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.
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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.
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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●
●●●●●●
●
●●
●
●●
●
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
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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?
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