Modeling Movement for Mobile IP Networks

Transcription

1 Modeling Movement for Mobile IP Networks Andres Rojas Center for Advanced Internet Architectures Swinburne University of Technology 30th March anrojas@swin.edu.au Abstract The modeling of user movements is used to investigate network performance of Mobile IP and Ad-Hoc networks. Of the several movement models which exist, one which is in common use is the Random Waypoint Model (RWP). In this presentation we summarise the movement models in use and their application, and examine their validity for Mobile IP by using a real-life trace of user movement. - anrojas@swin.edu.au 1

2 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 - anrojas@swin.edu.au 2 Outline... Movement Models Gathering Data for My Own Trace Options What was gathered? Analysis of collected data Conclusions - anrojas@swin.edu.au 3

3 Outline... Movement Models Gathering Data for My Own Trace Options What was gathered? Analysis of collected data Conclusions - anrojas@swin.edu.au 4 Mobility Models Vehicular Trac Theory Mobile Telephony Ad-Hoc Networks Real Life Traces - anrojas@swin.edu.au 5

4 Vehicular Trac Theory Fluid Flow Model Amount of trac 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. - anrojas@swin.edu.au 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 - anrojas@swin.edu.au 7

5 Mobile Telephony Models Random Walks, Akyildiz and Lin, et al [4] User resides in a cell for a time interval (governed by a certain distribution) User either stays put or moves to neighbouring cell with a certain probability No predetermined direction => no concept of a "trip" - anrojas@swin.edu.au 8 Mobile Telephony Models Mobility Models combined with Trac Models Network's performance - anrojas@swin.edu.au 9

6 Ad-Hoc Networks Movement of users relative to each other Military, emergency response, etc Entity Mobility Models (All entities move according to the same set of rules) Group Mobility Models (Movement decisions depend upon other users in the group) Camp and Boleng, et al [5] surveys both types - anrojas@swin.edu.au 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] - anrojas@swin.edu.au 11

7 Ad-Hoc Networks Random Direction Model Similiar to RWP Select random direction : uniform distr., [0..2π] - anrojas@swin.edu.au 12 Mobile IP Mobility Models Not Mobile IP specic! - anrojas@swin.edu.au 13

8 Real Life Traces as Input to Simulations Tracing of user movement is dicult 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 radio wireless network - anrojas@swin.edu.au 14 Above trace the movements of users within the network, to the cell/ap level - anrojas@swin.edu.au 15

9 Outline... Movement Models Gathering Data for My Own Trace Options What was gathered? Analysis of collected data Conclusions - anrojas@swin.edu.au 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) - anrojas@swin.edu.au 17

10 Options for trace Trac Survey - highway trac survey GPS receiver Pros: detailed recording of location, speed Cons: Possible loss of comms, limited recording of waypoints, battery life, cost - anrojas@swin.edu.au 18 Options for trace GSM - triangulation Neve's Steplogger, [ 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 - anrojas@swin.edu.au 19

11 Log book method The Collected Data based on Melbourne's street directory: Melway A Melway reference, eg Map 29 D 8, can be converted to a Cartesian 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! - anrojas@swin.edu.au 20 Outline... Movement Models Gathering Data for My Own Trace Options What was gathered? Analysis of collected data Conclusions - anrojas@swin.edu.au 21

12 Analysis of Collected Data 22 Travel Around Melbourne, Oct & Nov 2004 y x 23

13 Travel Around Melbourne, Oct & Nov 2004 (zoom) y x 24 Travel: Home Work Home, Oct & Nov 2004 y x 25

14 Count of Visits count X Y (X: , Y: ) 26 Cummulative Rest Time (X: , Y:20 70) cummulative rest (log10(s)) X Y 27

15 Random Waypoint Model (RWP) ns-2 implementation X taken from Uniform Distr Y taken from Uniform Distr speed taken from Uniform Distr 28 Random Waypoint Model x,y chosen from Uniform Distr. y x (random speed, random pause) 29

16 Cummulative Distribution of Destinations (Waypoints) in X Fn(x) Normal Distr. Uniform Distr x 30 Cumm. Dist. of Destinations (Waypoints) in X Fn(x) x (X: ) 31

17 Cumm. Dist. of Destinations (Waypoints) in Y Fn(x) Normal Distrib y 32 Cumm. Dist. of Destinations (Waypoints) in Y Fn(x) Normal Distrib y (Y:20 80) 33

18 Rest Times at Destinations (Sorted) Rest Times (s) Index 34 Histogram of Rest Time Frequencies Frequency Rest Times (s) 35

19 Histogram of Rest Time Frequencies Frequency Rest Times (s) (Rest Times < 100k) 36 Conclusions Many models, many applications No specic 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

20 References [1] D. Lam, D. Cox, and J. Widom, Teletrac modeling for personal communications services, [2] D. Hong and S. S. Rappaport, Trac Model and Performance Analysis for Cellular Mobile Radio Telephone Systems with Prioritized and nonprioritized Hando Procedures, IEEE Transactions on Vehicular Technology, vol. 35, no. 3, pp. 7792, [3] M. M. Zonoozi and P. Dassanayake, User mobility modeling and characterization of mobility patterns. IEEE Journal on Selected Areas in Communications, vol. 15, no. 7, pp , [4] I. Akyildiz, J. Ho, and Y. Lin, Movement based location update and selective paging for pcs networks, [5] T. Camp and J. Boleng and V. Davies, A Survey of Mobility Models for Ad Hoc Network Research, Wireless Communication and Mobile Computing (WCMC), vol. 2, no. 5, pp , [6] C. Bettstetter, Mobility modeling in wireless networks: Categorization, smooth movement, and border eects, ACM Mobile Computing and Communications Review, vol. 5, no. 3, pp. 5567, July [7] A. Balachandran, G. Voelker, P. Bahl, and P. Rangan, Characterizing user behavior and network performance in a public wireless lan, [8] D. Kotz and K. Essein, Analysis of a campus-wide wireless network, in Mobile Computing and Networking, 2002, pp [9] D. Tang and M. Baker, Analysis of a metropolitan-area wireless network, in Mobile Computing and Networking, 1999, pp [Online]. Available: citeseer.ist.psu.edu/ html [10] Neve - Steplogger, Neve ITS Pty Ltd,

21 Questions? 40 Latex, foiltex, PPower4 Latex used (pdatex). Slide presentation supported by the foiltex package. These slides were given the nal form by post-processing the PDF generated from L A TEX source with PPower4. 41